Mattia Merluzzi

Miltiadis C Filippou

Leonardo Gomes Baltar

Emilio Calvanese Strinati
email: emilio.calvanese-strinati@cea.fr

Effective Goal-oriented 6G Communications: the Energy-aware Edge Inferencing Case

Keywords: Machine Learning, 6G, goal-oriented communications, sustainable operation

Currently, the world experiences an unprecedentedly increasing generation of application data, from sensor measurements to video streams, thanks to the extreme connectivity capability provided by 5G networks. Going beyond 5G technology, such data aim to be ingested by Artificial Intelligence (AI) functions instantiated in the network to facilitate informed decisions, essential for the operation of applications, such as automated driving and factory automation. Nonetheless, while computing platforms hosting Machine Learning (ML) models are ever powerful, their energy footprint is a key impeding factor towards realizing a wireless network as a sustainable intelligent platform. Focusing on a beyond 5G wireless network, overlaid by a Multi-access Edge Computing (MEC) infrastructure with inferencing capabilities, our paper tackles the problem of energy-aware dependable inference by considering inference effectiveness as value of a goal that needs to be accomplished by paying the minimum price in energy consumption. Both MEC-assisted standalone and ensemble inference options are evaluated. It is shown that, for some system scenarios, goal effectiveness above 84% is achieved and sustained even by relaxing communication reliability requirements by one decimal digit, while enjoying a device radio energy consumption reduction of almost 23% at the same time. Also, ensemble inference is shown to improve system-wide energy efficiency and even achieve higher goal effectiveness, as compared to the standalone case for some system parameterizations.

I. INTRODUCTION

Recently, research on the sixth generation of mobile communication systems (6G) has kicked off all around the world [START_REF] Uusitalo | 6G vision, value, use cases and technologies from European 6G flagship project Hexa-X[END_REF], [START_REF] Saad | A vision of 6G wireless systems: Applications, trends, technologies, and open research problems[END_REF], aiming to identify new technological enablers for wireless networking and service operation of unprecedented capability. Different from previous mobile systems generations, 6G will embed human, physical and digital worlds into the same ecosystem and infrastructure [START_REF] Uusitalo | 6G vision, value, use cases and technologies from European 6G flagship project Hexa-X[END_REF]. To this end, both human and machine type communications will play a key role, paving the way to a holistic system in which different in-network intelligent agents exchange and process massive amounts of data in real-time to achieve common goals in several diverse scenarios (e.g. industrial, automotive). Therefore, among the several pillars needed to enable 6G, it is worth mentioning: i) a massive deployment of distributed computing and storage resources at the network edge; ii) a new communication perspective, no longer driven by radio link performance, but rather by the actual outcome of a communication strategy (effectiveness), per a predefined goal. This work has been partly funded by the European Commission through the H2020 project Hexa-X (Grant Agreement no. 101015956).

The former is possible thanks to the Multi-access Edge Computing (MEC) paradigm [START_REF] Kekki | ETSI White Paper: MEC in 5G networks[END_REF], which enables a powerful Information Technology (IT) environment including a plethora of new services at network edge in short proximity to the end user. The MEC paradigm poses several challenges in terms of a joint design of communication and computing aiming to strike the best trade-offs in terms of latency, energy, trustworthiness and, more in general, sustainability targets. Focused on Artificial Intelligence/Machine Learning (AI/ML) related workloads, an additional challenge is how to enhance learning/inference reliability given a fixed pool of radio and processing resources as parts of the network. Indeed, when considering connect-compute services tailored to task offloading use cases (including, but not limited to learning/inference tasks) [START_REF] Chen | Task offloading for mobile edge computing in software defined ultra-dense network[END_REF], energy, end-to-end (E2E) latency, service dependability, etc. are affected by the availability of both radio and computing resources, whose scarcity may be severe in dense user deployments [START_REF] Emara | Spatiotemporal dependable task execution services in MEC-enabled wireless systems[END_REF]. In this context, new measures of reliability going beyond the classical communication concepts, are ready to be explored, building on a paradigm shift from data-oriented to goal-oriented communications [START_REF] Letaief | Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications[END_REF], [START_REF] Strinati | 6G networks: Beyond Shannon towards semantic and goal-oriented communications[END_REF].

Goal-oriented communications go beyond the common paradigm of transmitting data with a targeted communication reliability, as focus is rather on the actual effect that the decoded information has on performing an action, a concept also known as effectiveness [START_REF] Strinati | 6G networks: Beyond Shannon towards semantic and goal-oriented communications[END_REF]. While this is clear for humanto-human communications, the application to connected intelligence is a subject to be explored, paving the way to define new target performance indicators, beyond the "legacy" ones defining wireless technologies up to and including 5G. Such new metrics are expected to bring up new trade-offs involving several cross-layer parameters and performance. Related work: Edge inference has recently gained considerable attention [START_REF] Letaief | Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications[END_REF], [START_REF] Zhou | Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing[END_REF]. In particular, data and model compression techniques have been shown to enable energyefficient and low latency edge inference, while guaranteeing target performance in terms of accuracy [START_REF] Ran | DeepDecision: A Mobile Deep Learning Framework for Edge Video Analytics[END_REF]- [START_REF] Merluzzi | Wireless Edge Machine Learning: Resource Allocation and Trade-Offs[END_REF], also in the case of cooperative inference [START_REF] Shi | Communication-efficient edge AI: Algorithms and systems[END_REF], [START_REF] Merluzzi | Dynamic ensemble inference at the edge[END_REF]. Goal-oriented communications have been proposed as a promising solution calling for the design of data encoding and transmission strategies, such that a specific task is performed [START_REF] Letaief | Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications[END_REF], [START_REF] Shao | Bottlenet++: An end-to-end approach for feature compression in device-edge co-inference systems[END_REF]. In [START_REF] Lee | Deep learningconstructed joint transmission-recognition for internet of things[END_REF], the authors propose a joint transmission and recognition scheme, showing the effect of communication reliability on the accuracy, ignoring delay and energy consumption, as well as cooperative inference. Finally, task replication has been shown to improve reliability [START_REF] Shlezinger | Collaborative inference via ensembles on the edge[END_REF], also in the presence of random edge server struggling or poor channel conditions [START_REF] Li | Exploiting computation replication for mobile edge computing: A fundamental computation-communication tradeoff study[END_REF].

Our contributions: Building on the aforementioned technological enablers, in this paper we investigate the edge inference task from a goal-oriented perspective. In particular, the goal embraces targeted inference accuracy/reliability and E2E delay (goal value), subject to energy consumption (goal cost), all affected by changing system parameters, such as wireless channels, MEC Hosts' (MEHs') availability, and other cross-layer parameters. Both standalone and ensemble (cooperative) inference settings are investigated, to also show the impact of inference task replication. The main contributions of this work are summarized as follows: i) we show how the goal-oriented approach reduces device energy consumption requirements at target effectiveness (with the latter involving delay and accuracy), even in the presence of degraded uplink communication reliability regimes (Bit Error Rate -BER); ii) we also show how a distributed deployment of MEHs enhances the success rate of the goal (effectiveness), and even further reduces the goal cost in some circumstances. Organization of the paper: Section II presents the system scenario and network deployment specifics and Section III defines an edge inference goal, with its accompanied requirements and its incurred costs; Section IV elaborates upon standalone and ensemble-based inference. Section V presents thoroughly the performed numerical evaluations and observations. Finally, Section VI concludes the paper.

II. SYSTEM MODEL AND SETUP

Fig. 1: MEC-assisted inference scenario; use of helper MEH is optional to enhance inference performance.

The system architecture, the main system entities, and their operations are shown in Fig. 1. In particular, in our setting, K end devices (e.g., sensors or devices carrying them) of limited computing capability and power autonomy upload, through the wireless connection to an Access Point (AP), input data to nearby MEHs hosting intelligent agents, which are onboarded with inferencing task-relevant pre-trained and pre-uploaded ML models. The intelligent agents, in their turn, produce inferencing outputs (e.g., predictions or recommendations) and provide them to the calling client application via downlink radio transmission. The general assumed MEC infrastructure deployment consists of multiple MEHs deployed across a network coverage area, where, each MEH is either collocated to a radio AP, or located at aggregation points of the access network, thus, being accessible by multiple APs. For brevity, we focus on a part of a deployment consisting of two MEHs. The first one is a primary MEH, which is collocated to the AP. Also, device requests can be further possibly treated by another MEH, interfaced with the primary MEH via wired (e.g., fiber) backhaul connection; this is termed after as a helper MEH. The aim of a helper is to improve performance, e.g., in terms of goal accomplishment, i.e., inference effectiveness, thanks to offering single point of failure avoidance, as well as through inference diversity gains.

Let us now elaborate on the goal definition, its requirements and its incurred costs for the focused system setup.

III. EDGE INFERENCE GOAL: REQUIREMENTS & COSTS

A goal is defined as an information processing task accompanied by a set of minimum requirements that must be fulfilled as part of task accomplishment. In the case of edge inference, the goal is to provide inference results, within a delay budget, overcoming a minimum tolerable level of inference accuracy. In this work, the cost of the edge inference goal is split into an energy cost for end devices to upload data via the uplink connection to an AP, and an energy cost for the MEH(s) to execute the edge inference task. To this end, we will present: i) the E2E delay model, comprising transmission signaling and computing times; ii) the goal achievement and the corresponding outages (affecting effectiveness); iii) the end devices and MEH energy consumption models.

A. End-to-end delay as a goal requirement

Edge inference tasks fall into the category of computation offloading services, whose goal is to delegate the execution of computationally heavy workloads from end devices (e.g., vehicles or robots carrying sensors) to nearby accessible MEHs, and whose E2E delay generally entails three components for an inference task: i) uplink communication delay to transmit input inference data; ii) computation delay at the MEH to process input data; iii) downlink communication delay to transmit inference outputs (e.g., labels) back to the requesting device. In this paper, we neglect the last delay term, as in several scenarios the result represents a significantly reduced amount of bits, as compared to the input data size. Let us now elaborate on each delay component.

1) Uplink transmission delay: Let us suppose, without loss of generality, that device k issues one inference request at a given time instant, and that the pattern for inference is encoded with n k bits. Thus, assuming that the transmission has to occur with target BER denoted by BER k , the uplink transmission delay is given by:

D u k = n k B u k log 2 1 + φ(BER)h u k p u k N0B u k , (1)
where B u k is the uplink bandwidth assigned to device k, h u k is the channel power gain, p u k is device k's uplink transmit power, and N 0 is the noise power spectral density at the receiver; whereas, the BER margin can be approximated as

φ(BER k ) = -1.5 ln(5BER k ) , or φ(BER k ) = -1.5
ln(0.5BER k ) in case the spectral efficiency falls below 41 [START_REF] Goldsmith | Variable-rate variable-power MQAM for fading channels[END_REF]. This delay component is affected by both the device transmit power and BER requirements, a fundamental aspect in defining the difference between classical and goal-oriented communications.

2) Remote computing delay for inferencing: We assume that each MEH j (being a primary -p or a helper -h) is equipped with a CPU of maximum clock frequency f max j . A portion of f max j is assigned to each device submitting a request for inference output. We denote by α k,j ∈ [0, 1] the percentage of CPU power assigned to device k at MEH j. Furthermore, we assume that this portion is not always fully available, as MEH j may dedicate part of it to higher priority traffic upon request. We assume this priority traffic to be an exogenous parameter that we cannot control, therefore, we denote by β k,j ∈ [0, 1] the percentage of available CPU for device k at a given time instant, also taking into account the concurrent arrival of higher priority processing tasks. Therefore, the actual CPU cycle frequency allocated to device k by MEH j is given by:

f k,j = α k,j β k,j f max j .
(2)

Obviously, system performance is strongly affected by the statistics of β k,j . In the case MEH h cooperates in the inference process, input inference data must be transferred from MEH p to MEH h, and results should be then sent back to MEH p to operate the necessary output aggregation. For simplicity, we assume a high capacity backhaul with fixed Round Trip Time (RTT) D bh p,h between MEH p and MEH h. Therefore, the total inference computation delay is

D c k = max J k,p f k,p , J k,h f k,h + D bh p,h , (3)
where J k,p and J k,h denote the numbers of CPU cycles needed to run the inference for device k at MEH p and h, respectively. Also, we neglect the time needed by MEH p to aggregate results, as we assume this to be a simple operation, compared to the inference task. Finally, the overall inference delay accounts for communication and computation delays, thus reading as

D tot k = D u k + D c k .
As a matter of goal accomplishment, we refer as a delay outage, to the event {D tot k > D max k }, i.e., a delay outage occurs if the total inference delay of a request exceeds a predefined threshold, required by the device during a service agreement phase. However, this represents only the first step of goal accomplishment, as from a goal perspective (correct in-time result), an outage does not only depend on the delay of edge inference task execution, but also on the inference effectiveness, as it follows in the next section.

B. Inference effectiveness and goal outage

The inference effectiveness can be directly represented by the accuracy of a task, or by any other metric that can quantify the success of inference operations. For example, for a classification task, it can be represented by the correct classification rate, or by the entropy computed on a posteriori probabilities at the output of a discriminative classifier. Instantaneously, an inference outage occurs whenever a predefined measure does not meet a target threshold, e.g., the mean squared error in a regression task is higher than a value, or the predicted label in a classification task is incorrect. Let us refer to this measure as inference value. Let us denote by Θ k,j ∈ {0, 1} a binary variable that equals 1 if and only if the inference output related to a pattern generated by device k and processed by MEH j, is accurate. For example, for an image classification task, Θ k,j = 1 if an image generated by device k is correctly classified. Thus, we can refer to the event {Θ k,j = 0}, as inferencing outage. When exploiting ensemble inference, the inference effectiveness can be improved by requesting the same inference task computation to be addressed by both MEHs, with the aim of exploiting computation and/or inference diversity. Therefore, we can write an aggregated inference value as Θ k = Θ k,p ∪ Θ k,h , where the union operator represents an aggregation of inference values. Let us note that this operation can be implemented in several ways, such as majority voting and minimum entropy in classification tasks, or any other combining strategy for different inference tasks. Finally, for a generic pattern generated by device k, a goal outage event occurs if either a delay outage or an inference outage occurs. Therefore, we can write the goal accomplishment event as

{{Θ k = 1} ∩ {D tot k ≤ D max k }}.

C. Energy consumption as the cost of a goal

In our scenario, we can identify two sources of energy consumption: i) the end devices, consuming energy to transmit data; ii) the MEHs, consuming energy to process data.

1) Uplink transmission energy consumption: We assume that the device energy consumption is only due to the uplink transmission. Therefore, recalling (1), the k-th device energy consumption for uploading a given pattern for inference is

E u k = p u k D u k
, which, similarly to the uplink transmission delay, is affected by both transmit power and BER requirements.

2) MEH computation energy consumption: The energy consumed by MEH j to satisfy one inference request of device k can be written as E k,j = κ j f 2 k,j J k,j [START_REF] Yuan | Energy-efficient CPU scheduling for multimedia applications[END_REF], where κ j is the effective switched capacitance of the processor. Thus, the total energy consumption of both involved MEHs can be written as

E mec = K k=1 (E k,p + E k,h
), where we recall that K is the number of devices in the system.

D. The trade-off between energy consumption & goal outage

The aim of this paper is to show the trade-off between average system energy consumption and goal accomplishment rate, which we term after as goal effectiveness. The former translates into the expectation of the energy terms described in Section III-C, taken with respect to random parameters that include: i) wireless fading channels (h u k , ∀k); ii) MEH availability (β k,i , ∀k, i). The latter, recalling the goal accomplishment event presented in Section III-B, reads as:

P g k = E 1{Θ k,p = 1} • 1{D tot k ≤ D max k } , ∀k (4)
where 1{•} denotes the indicator function, i.e. the argument of the expectation is a Bernoulli random variable, so that its expectation is indeed the probability of the event. It should be noted that, in this work, we do not perform any particular optimization of the introduced parameters (e.g., power, computing resources, helper MEH selection, etc.), as the aim is an exploration of the performance aimed at providing insights on system deployment requirements, strategies, and optimization variables to be controlled during system operations. A full optimization of the system is beyond the scope of this paper and is left for future investigations. In the following section, we detail the operation of standalone (i.e., single MEH) and ensemble inference, from a goal effectiveness perspective.

IV. STANDALONE VERSUS ENSEMBLE INFERENCE

We assume that a central controller at network side, which is aware of the goal requirements of a client application, collects information on channel realizations, MEHs availability, and inter-MEH backhaul delay, aiming to decide upon which MEH (MEH p, MEH h, or both) is selected to serve each incoming inference request. The central controller also provides as its output feedback (e.g., via a downlink control channel) on MEH resource availability, that can be used by each requesting device to regulate its uplink transmit power towards meeting the E2E delay requirement of the goal. Standalone inference: Standalone inference takes place whenever MEH p cannot rely on the aid of a MEH h. In this case, only MEH p can host devices' requests, and a correct in-time inference is guaranteed only if MEH p has enough computing capacity to guarantee the delay constraint (given the current connect-compute network conditions) and, at the same time, the inference is performed with the targeted inference value. To save device energy, we assume the device uplink transmit power to be chosen to exactly meet the delay constraint, i.e., so as to meet a transmission delay

D u k = max 0, D max k - J k,p f k,p .
If the goal cannot be achieved with the device maximum power p u,max k , we assume that the device transmits at p u,max k , thus getting the inference results as soon as possible, i.e., with a best effort philosophy. Ensemble inference: Ensemble inference takes place whenever both primary and helper MEHs cooperate in processing inferencing requests, and it can end up in the following cases, depending on the current connect-compute network conditions: i) only the primary MEH performs inference; ii) only the helper MEH performs inference; iii) both MEHs perform inference, and the result is obtained from a combination of both results (cooperative inference). The latter is obtained by a requesting device sending a pattern to the primary MEH, which, in its turn, sends a copy of the pattern to the helper MEH, while, in the meantime, starts processing the task; the device, in this case will await for the response of both primary and helper. This operation is feasible iff is the minimum communication delay, obtained by plugging the maximum device transmit power p u,max k into (1). If this feasibility check returns a positive result, device k's request is treated by means of a cooperation, choosing its transmit power so as to guarantee a transmission delay

D u,min k + max J k,p f k,p , J k,h f k,h + D bh p,h ≤ D max k (cf. ( 3
D u k = D max k -max V. PERFORMANCE EVALUATION & DISCUSSION
For the sake of performance evaluation, we focus on an edge image classification task. As indicator of inference effectiveness, we use the correct classification over a test set. Communication parameters: In our performance evaluation, we consider a single device scenario, for the sake of simplicity in commenting the results. However, a multi-device scenario would not strongly affect the take home message of the simulations, which would only be influenced by the chosen task scheduling strategies at both communication and computation tiers. Device position is chosen uniformly randomly in a circle of radius 150 meters around a single AP, serving it at 3.5 GHz, with bandwidth of B = 10 MHz. We generate the channels with path loss [START_REF] Gpp | Scenarios, frequencies and new field measurement results from two operational factory halls at 3.5 GHz for various antenna configurations[END_REF], and Rayleigh fading with zero mean and unit variance. At the receiver, the noise power spectral density is N 0 = -174 dBm/Hz, while the maximum device transmit power is p u,max 1 = 20 dBm. Computation parameters: At the MEC deployment side, we consider the primary MEH collocated with the AP, and one helper MEH, reachable within finite RTT D bh p,h , to be specified for each simulation. Each MEH operates at maximum CPU frequency of f max h = f max p = 4.5 GHz. Furthermore, we assume β 1,j , j = p, h (cf. ( 2)), to address device requests, taking different values to compare performance in terms of MEHs availability. Specifically, we assume β 1,p and β 1,h to be uniformly distributed (in three different simulations) between 0 and β max 1,j = [1, 0.5, 0.25], j = p, h, where we also aim to capture the full (deterministic) MEH availability benchmark. The delay threshold is set to D max 1 = 100 ms. Learning parameters: We evaluate the performance on the CIFAR-10 dataset [START_REF] Krizhevsky | Learning multiple layers of features from tiny images[END_REF], which consists of 60000 32x32 pixel color images divided in 10 classes, with 6000 images per class. The training set is made of 50000 instances, which we split into two equal sub-data sets to train the Convolutional Neural Networks (CNNs) that are deployed at primary and helper MEHs, with the purpose of learning diversity, so as to improve inference performance in the case of cooperative inference. The test set is made of 10000 instances. We use a Deep CNN trained via usual backpropagation with an l 2penalized cross-entropy loss, and an existing architecture 2 . Whenever exploited, the ensemble is built by summing the scores (i.e., a posteriori probabilities) of the two classifiers, and choosing the label with the highest aggregated score [START_REF] Shlezinger | Collaborative inference via ensembles on the edge[END_REF]. As already mentioned in Section III-D, results in terms of energy consumption and goal effectiveness are obtained by averaging over realizations of wireless channels and MEH's availability. More specifically, we consider 10 5 realizations.

A. The trade-off between goal effectiveness and costs

As a first performance evaluation, in Fig. 2a, we show the goal effectiveness (cf. ( 4)) as a function of the device transmit energy, obtained by varying the BER requirements during transmission. Also, to have a complete view of system performance, Figs. 2b and2c show, respectively, the goal effectiveness and the device energy consumption as functions of the same BER requirements used to obtain Fig. 2a. In these figures, the different colors encode different values of β max 1,j , j = p, h (cf. ( 2)). At the same time, solid lines represent standalone inference (i.e., only the primary MEH participates (-

1; i = 1 ; i = p ; h ) - m ax 1; i = 1 ; i = p ; h - m ax 1; i = 0 :5 ; i = p ; h - m ax 1; i = 0 :2 5 ; i = p ; h
Per-device CPU availability Fig. 2: Goal achievement and costs with different connect-compute network conditions.

in the inference process, without helper involvement), while dashed dotted lines represent the ensemble inference case. Let us first focus on the solid lines (i.e., standalone inference), to gain insights on goal effectiveness with respect to BER requirements (i.e., classical communication reliability) and device energy costs. Ensemble performance analysis will then come naturally as an enhancement of the standalone case, to be described in what follows below.

The effect of BER on goal effectiveness: First of all, we can notice how the effectiveness increases, as the device transmit energy consumption increases, as expected, with different performance depending on MEH's availability levels. This is due to the fact that the BER requirements are stricter (cf. ( 1)). However, we can appreciate how, above a given threshold, BER requirements do not strongly affect goal effectiveness, meaning that a slightly increased BER leads to similar performance in terms of the effectiveness, in all network conditions (obviously with different values according to MEH availability). It is noteworthy that effectiveness is invariant to communication reliability changes in the range BER = [10 -4 , 10 -3 ] (see Fig. 2b). This first observation (obviously dependent on the specific application) suggests that classical communication related reliability performance can be relaxed almost without any impact on goal effectiveness, and, as a by-product of such relaxation, non negligible gains in terms of device energy consumption are expected, as visible from Fig. 2c, where the device energy consumption is plotted as a function of the BER, for β max 1,i = 1, i = p, h. The effect of ensemble inference: Having appreciated that, for certain system regimes, goal effectiveness may be kept almost constant by significantly relaxing classical communication related reliability, we can now analyze the beneficial effect of the helper MEH presence into the system. Indeed, from the dashed dotted lines, we can notice how goal effectiveness experiences a great improvement, if compared to the standalone inference case, on the basis of the same device energy consumption cost (i.e., the same BER requirements). Equivalently, the same effectiveness can be achieved at a lower goal cost, by further relaxing the BER requirements (e.g., see the 80 % effectiveness case).

Of course, the lower energy consumption at the device side translates into a higher energy consumption of the MEHs, as visible from Fig. 2d, where we show the energy consumption of the MEC system for a single inference request, as a function of β 1,i , i = p, h, in case of standalone (first bar) and ensemble inference (second bar). By comparing standalone and ensemble performance for the same MEH availability, we obtain an approximately doubled energy consumption at the MEC infrastructure level for the ensemble case, as visible from Fig. 2d, due to the obvious fact that, on average, the two MEHs address requests at the same CPU speed. Nevertheless, we can notice an interesting outcome: by comparing the ensemble inference at half maximum available MEH CPU (red dashed dotted line), with the standalone inference at full maximum available CPU availability (blue solid line), better performance in terms of effectiveness against device energy consumption is guaranteed for the prior case, while also achieving a lower energy consumption at the MEC network infrastructure level. This result is due to the quadratic law of the dynamic CPU energy consumption with respect to the CPU cycle frequency, and it shows that better performance in terms of goal achievement can be obtained without paying any price (but actually experiencing a gain), in terms of network energy consumption. In particular, this behavior suggests that, in the case of ensemble inference, it is convenient to have two available CPUs at half availability, rather than one CPU at full availability (on average), for a twofold reason: i) the total MEC energy consumption decreases; ii) the reliability improves, since single point of failure issues are counteracted. Let us also notice that, for the deterministic, the full CPU, and the half CPU availability cases, ensemble inference outperforms the deterministic standalone method, due to the fact that the two classifiers cooperate to improve their standalone capabilities. Also, the ensemble inference at 25% CPU availability outperforms the deterministic standalone classifier, with a greatly decreased energy consumption (around 20 times) at the MEC infrastructure level. The effect of the backhaul delay: Of course, this remarkable superiority of the ensemble setting is dictated by the zero delay backhaul assumption assumed in this first simulation, which is more than ideal in practice. To this end, we now show the effect of the backahul RTT on the performance enhancement of the ensemble with respect to the standalone inference case. In Figs. 2e,2f, we plot the effectiveness as a function of the device energy consumption (i.e. the same result as Fig. 2a), for two different backhaul RTT conditions, namely D bh p,h = 0.25D max 1 and D bh p,h = 0.75D max 1 , respectively. Five MEC network conditions are considered in each figure: i) the deterministic standalone ii) the deterministic ensemble; iii) the ensemble with β max 1,i = 1, i = p, h; iv) the ensemble with β max 1,i = 0.5, i = p, h; v) the ensemble with β max 1,i = 0.25, i = p, h. From the plots, we can notice how the effect of the backhaul RTT plays a key role in determining the beneficial effects of the helper in the edge inference task, which decreases as the backhaul RTT increases. Indeed, in Fig. 2f, the ensemble exhibits even degraded performance compared with the standalone, although this is due to the fact that only the deterministic standalone (i.e., its best case) is shown in the figure, which translates into a higher network energy consumption (Fig. 2d).

VI. CONCLUSIONS

In this paper, we presented the performance evaluation of an edge inference service, following a goal-oriented approach, in which system performance is not measured by classical radio reliability parameters (such as the BER), but rather measured by the actual outcome of a communication strategy with respect to the effectiveness of a properly defined goal. We described a MEC-enabled wireless network in which multiple MEHs can cooperate to improve edge inference performance, while trading off end device and MEC network energy as costs of the goal. Through numerical simulations, we have shown how the effectiveness can achieve good performance also in the presence of relaxed BER requirements, suggesting that high communication reliability is not needed, as far as inference performance reaches target levels. Furthermore, we have shown how ensemble inference (implemented thanks to MEHs' cooperation) can help improving goal performance, depending on specific deployment characteristics, such as the availability of MEHs and the backhaul delay interconnecting them. We believe that such a goal-oriented performance evaluation can pave the way for system deployment considerations involving communication and computing perspectives. Also, thanks to the gained insights on system performance, we hope that cross-layer optimization strategies can build on these results, to further improve trade-offs involving energy consumption and effectiveness in future wireless networks.

  i = 1; i = p; h) -max 1;i = 1; i = p; h max 1;i = 0:5; i = p; h max 1;i = 0:25; i = p; h(a) Goal effectiveness vs. device energy cons.

  i = 1; i = p; h) -max 1;i = 1; i = p; h max 1;i = 0:5; i = p; h max 1;i = 0:25; i = p; h(b) Goal effectiveness vs. BER.

  Device energy cons. vs. BER.D e t.

Tighter approximations can be used under more specific assumptions on modulation and coding, which go beyond the scope of this paper.

J k,p f k,p , J k,h f k,h + D bh p,h .On the other hand, if cooperation is not feasible under the current conditions, standalone inference is performed at the fastest MEH, with transmit power selected to guarantee the delay constraint, and with the best effort strategy in case of infeasibility.

https://appliedmachinelearning.blog/2018/03/24/achieving-90-accuracyin-object-recognition-task-on-cifar-10-dataset-with-keras-convolutionalneural-networks/


Keywords: D-band, alignment procedure, phased array antenna, hardware, backhauling, beyond5G/6G

This paper describes the antenna beam alignment strategy of a D-band backhaul link adopted in the DRAGON project. The aim is to continuously compensate for antenna vibrations. This strategy is based on a periodic assessment of beams stored in a codebook, and can be implemented independently in both ends of the link. Hardware requirements coming from the envisioned architecture of the whole transceiver are taken into consideration. In particular, communications among the different involved boards restrict the periodicity at which the beams of the codebook can be assessed. The design of the codebook and the algorithm for beams selection are also discussed. Simulations based on a software specifically developed demonstrate that the solution meets the tracking specifications for an D-band link associated to realistic antenna movements.

I. INTRODUCTION, THE DRAGON PROJECT

5G paves the way for the densification of telecommunication networks, especially in highly populated areas. Such networks will provide better coverage and less exposure of users to electromagnetic waves. Nevertheless, due to the increasing demand for data rate, it is expected that 100 Gb/s will be required soon at each access point (AP) in dense urban areas. The issue of fast links to connect APs to the core network therefore arises. The solution of providing backhaul with optical fiber is not sustainable as it requires long and costly works (especially in crowded urban areas) and does not allow for network reconfigurability. As 5G networks open to millimeterwave (mmWave) frequencies, a response to this problem is wireless backhauling in the D-band (130-174.8 GHz), where a lot of spectrum is available [START_REF] Frecassetti | D-Band Radio Solutions For Beyond 5G Reconfigurable Meshed Cellular Networks[END_REF]. This concept is illustrated by Fig. 1, where ➀ is the 5G core network, ➁ is the optical fiber transport, ➂ are the gNBs (5G base stations), ➃ are the mmWave backhauls and ➄ are the small cells. This approach would allow rapid and inexpensive deployment of small cells. Nevertheless, many technical challenges must be addressed, which is the purpose of the DRAGON project.

DRAGON (2020-2023) is a project funded by the European Union, bringing together 12 partners from research, academia, This work was conducted within the framework of the H2020 DRAGON project, which is partially funded by the Commission of the European Union (Grant Agreement No. 955699)      Fig. 1: Envisioned D-band wireless backhaul links in DRAGON to ease deployment and improve network flexibility.

SMEs and industrial sectors. It leverages the results of the DREAM project [START_REF] Frecassetti | D-Band Transport Solution to 5G and Beyond 5G Cellular Networks[END_REF]. The goal is to support the emergence of new markets and new market actors in Europe for the exploitation of the radio spectrum in D-band. To that aim, the objectives of the project are:

• on-field network demonstration of a wireless link at data rate up to 100 Gb/s in D-band exploiting (i) SiGe BiCMOS chip-set, (ii) 1024-element phased-array antenna, (iii) 2 256-Quadrature Amplitude Modulation and (iv) Fflexible duplexing, full duplexing and Line of Sight MIMO; • commercially enable wireless small cell backhauling links (thanks to compact and reliable D-band radios, low latency, multi-gigabit end-to-end connectivity and low visual impact); • reduction of the cost and power consumption (green radio) of backhaul/fronthaul links made possible by radios with high Effective Isotropic Radiated Power (EIRP) and by antennas with fine beam adjustment. To achieve these ambitious goals, one of the challenges -going one step further than in the previous DREAM project [START_REF] Lamminen | Technologies for D band links with beam steering functionality[END_REF] is the prototyping and the engineering of a highly integrated Dband transceiver comprising inexpensive and mass producible SiGe BiCMOS chip sets assembled to a multilayer antennain-package and proper thermal management. Other difficulties lie in the prototyping of the Base-Band (BB) processor and in the need to keep the antenna beams in the link aligned.

This paper describes the solution to the latter point. A real-time antenna beam alignment (BA) management system is developed, called Phased Array Management (PAM). The paper is organized as follows: the first part describes the alignment problem coming from the antennas movements in a real-world environment. The following section depicts the hardware (HW) architecture of the DRAGON system from the PAM point of view and how it constraints the BA process. This process is then detailed in Section IV. Finally, before providing some leads on next steps of the project in the conclusion, system simulations are presented in section V, demonstrating that the proposed solution succeeds in tracking realistic antenna movements.

II. ALIGNMENT PROBLEM

Pole-mounted antennas, Fig. 1 ➄, and to a lesser extent mast-mounted antennas, Fig. 1 ➂, are subject to fast vibrations due to wind gusts and slow vibrations due to temperature changes during the day. Those movements can be twists or sways (typical, first vibration mode of a pole), as illustrated by Fig. 2. The target distance between small cells and gNBs -100 m to 1 km -imposes very high gain antennas, and therefore the use of very narrow beams (in the order of 3.5°of half-power beam-width). However, this feature may cause a misalignment between transmitter and receiver beams when poles vibrate, leading to a rapid drop of link gain. A measurement campaign by NOKIA [START_REF] Kalimulin | Impact of mounting structures twists and sways on point-to-point millimeter-wave backhaul links[END_REF] has shown that poles can experience sways and twists oscillations with up to 5°and 1°of amplitude respectively, with a frequency up to 5 Hz. Along with the dynamical misalignment due to vibrations, a static misalignment can also exist. Such deviation can be caused by various factors such as an initial misalignment during deployment of the system, an impact that permanently moves the fixing part supporting the antenna, or screws that loosen over time. In this context, the role of the PAM is to correct the antenna beam direction (with respect to a pre-defined criterion, e.g. maximizing the received power) at all times to guaranty a minimum quality of service.

III. HARDWARE ARCHITECTURE

A. High-level architecture Fig. 3 shows a high-level schematic view of the architecture considered in the DRAGON project, presented from the PAM perspective (i.e. only structures that are related to BA are depicted). The PAM board simultaneously manages four phased-array antennas: a receiver (RX) and a transmitter (TX) for two carrier frequencies. Aside from that, the PAM board also receives information about the signal quality from the Base-Band board (BB board) in order to perform the BA procedure. The precise role of this BB board with respect to the alignment procedure is explained later, in section IV. Finally, the Transceiver Controller (TC) board introduces a timing constraint that has to be taken into consideration, as explained in section III-C. Fig. 3: High-level schematic view of structures involved in beam alignment

B. Beam control

The proposed phased-array antenna architecture is based on an antenna array built from clustered radiating elements because of Integrated Chip (IC) integration issues at Dband frequencies. Specifically, each phased-array antenna is composed of 64 RF ICs, each containing 4 clusters of 4 antenna patches. Overall, a phased-array antenna is comprising 1024 radiating elements, allowing an antenna gain of about 35 dBi associated to a half power beam-width of about 3.5°. Each cluster in the antenna is driven by or drives one RF chain, as illustrated in Fig. 4 for a transmitter antenna. In the transmitter part, a direct I/Q up-conversion is used to translate the BB signals to D-Band. The D-Band signal is then distributed to the RF chains feeding each antenna cluster. Each RF-chain is comprising a phase-shifter for beam steering of the transmitting antenna array. The configuration of each phaseshifter is adjusted from the PAM-board. Aside from that, the gains in each RF chain can be controlled from the TC-board to maximize the EIRP.

In the receiver part, the D-band signal received by each antenna cluster is fed to a different RF-chain. Each RF-chain is comprising a phase-shifter for beam steering of the receiving antenna array. Again, the configuration of each phase-shifter is adjusted from the PAM-board and the gains in each RF-chain are maintained stable from the TC-board. The combination of the signals coming from the different RF-chains are fed to an IQ down-converter to generate the received BB IQ signals.

To set a beam direction, a 22-bit coefficient must be written to each of the 256 phase shifters managing antenna clusters.

Phase-shifters are implemented as vector modulators similar to [START_REF] Del Rio | A Compact and High-Linearity 140-160 GHz Active Phase Shifter in 55 nm BiCMOS[END_REF] and the 22-bit coefficient word is used to specify the gains in the I and Q components of the vector modulator and the quadrant. In the rest of the document, we refer to a set of 256 of these 22-bit coefficients as a beam configuration.

Antenna coefficients are not computed in real-time; all beam configurations are stored in a codebook (obtained during the calibration phase prior to on-field deployment) to account for packaging and integration losses and RF impairments (see V-A3). Each phase-shifter can only store three beam configurations at once: one register contains the active configuration -which is in use in the antenna cluster -and two other registers constitute a bank from which the active register can be changed. These registers are depicted in Fig. 5. Because of this limited amount of memory space, the codebook is stored in the PAM board. To modify the beam direction of an antenna, the PAM board first has to send the corresponding beam coefficients to one of the two bank registers of each phase-shifter. Due to the amount of data to be transferred to these 256 phase-shifters, this operation takes time (hundreds of µs), as discussed in next section. On the other hand, once the configuration is loaded, the time to replace the active one with one from the register bank is negligible.

C. Beam coefficients transfer

Serial Peripheral Interface (SPI) is used to communicate with the RF chains (and in particular, to send beam coefficients to phase-shifters). In each antenna array, 64 RF chains are grouped in the same SPI daisy-chain. Therefore, each antenna array communicates with the PAM board through 4 SPIs and the PAM board manages a total of 16 SPI communications in parallel. The SPI interface uses 16-bit words which contain 10 bits of data. We call SPI transaction the fact of sending one 16-bit word to each RF chain. As the coefficient for the phase-shifter is a 22-bit word, three SPI transactions need to be performed to write a full beam configuration. With a target SPI frequency of 10 MHz, one SPI transaction takes about 130 µs; and a full beam configuration takes about 390 µs.

In addition to that, we also mentioned that the TC board needs to communicate to RF chains to adjust gain values. This communication uses the same SPI channels as the coefficients transfer; therefore the PAM board has to periodically allocate SPI transactions to relay messages between the TC board and the antennas. This adds a timing constraint on the BA procedure: the SPI links are not always available. To arbitrate the use of SPI channels between beam coefficients transfer and TC gain adjustment requests, a dedicated SPI frame has been defined. The default SPI frame is composed of 3 transactions for beam coefficients transfer followed by one transaction dedicated to TC requests. It therefore contains four SPI transactions and lasts about 520 µs. This HW constraint has an impact on the BA procedure periodicity. This point is discussed later, in section IV-C.

IV. ALIGNMENT PROCEDURE A. Principle

The chosen solution to the misalignment problem is to periodically try a beam direction that is different from the one currently used for communication, and get a feedback on the relevance of this new beam. If it is better than the previous one, it is kept for further communications; otherwise, the previous direction is restored. The feedback takes the form of a difference between the Received Signal Level (RSL) of the two beams, which is a direct measurement of the parameter that has to be optimized. Auxiliary information brought for example by accelerometers could also help in keeping a good alignment, but the overhead incurred by adding sensors was not deemed necessary with our specifications.

Switching the beam direction may cause a significant, even if temporary, loss of received power at the RX side since the tested beam may be noticeably misaligned with the remote transmitting beam. Therefore, dedicated fields are allocated for beam trial in the transmitted radio frames. During these so called BA fields, only dummy symbols are transmitted. The BA procedure is strictly restricted to this BA field, so that communications are not impacted. The high-level structure of the radio frame is depicted in Fig. 6, where each end of frame contains a BA field. In the current state of the DRAGON project, frames are expected to last between 180 µs and 1 ms. The duration of the BA field is in the order of a few µs, which is long enough to complete a full BA procedure, see IV-B, while adding a relatively small overhead in radio frames.

B. Full beam alignment procedure

In this section, we consider a D-band communication between two sites: the gNB (on a mast) and the small cell (on a pole), for only one of the two carriers. Two antennas are collocated on each site: RX and TX (for carrier #0 for example). There are therefore two communication links, local TX to remote RX and remote TX to local RX, that are in general not synchronized. The BA procedure is slightly different for the RX antennas and the TX antennas. In the first subsection below, we show how the procedure works on the local RX side, assuming no beam switch at the remote TX. The second subsection incorporates the local TX beam switch to the procedure and shows that the full procedure can also be applied, independently, to the remote site. 1) RX beam alignment: Even if the PAM board is responsible for the BA procedure, the BB board is also an integral part of it. Indeed, it performs two tasks: signaling to the PAM board when it is possible to switch beams during a BA field; and computing and transmitting the ∆-RSL of the new beam as compared to the previous one.

The RX BA procedure described in the following is illustrated in Fig. 7. Before performing the BA procedure, beam coefficients are sent to the phase shifters, see III-C. This transfer may take longer than a radio frame. In that case, the BA fields received during the transfer are simply ignored by the PAM board; the PAM listens to synchronization signals coming from the BB board only when it is ready.

Right before a BA field, the BB board measures the RSL with the current beam. Then, actions in the BA field are divided in three phases, noted P1, P2 and P3. During P1, the BB board informs the PAM board that the RX beam can be switched and the PAM actually switches the beam. Then, during P2, the BB measures the RSL with the new beam, computes the ∆-RSL and sends it to the PAM board. Finally, during P3, the PAM board decides whether to keep the new RX beam or to switch back to the previous one. At the end of the procedure, the PAM can start sending to the antennas the next beam configuration to assess, to one of the phase shifter registers.

Given this procedure, it is important for the remote TX beam to stay constant between the two RSL measurements (i.e. during P1 and P2). If the remote TX beam were to switch in-between, the ∆-RSL would not be a relevant measurement anymore to determine whether the new RX beam is better than the previous one. 2) TX beam alignment: Since the local RX and TX antennas are located on the same plane, it is possible to apply the same beam direction for both antennas. Therefore, the beam direction chosen at the end of the BA procedure for the local RX can be applied to the local TX. As shown by Fig. 8, the alignment error incurred by such hypothesis is relatively low and decreases with the distance between the two endpoints. For a local RX -local TX distance of 40 cm and a communication range of 100 m, the deviation is 0.25°for high amplitude of vibrations, happening less than 0.01 % of the time.

The TX BA procedure is depicted in Fig. 9. Since choosing the local TX beam only consists in following the decision for the local RX beam, beam coefficients are transferred to both antennas at the same time, in parallel (even if the direction is the same, coefficients might differ between RX and TX). If the new RX beam is worse than the previous one, no beam switch is done on the TX side (and the RX side switches back to previous beam). If the new RX beam is better, the TX switches to the new beam, already stored in the bank (and the RX keeps the same beam). The only constraint on the TX is to wait for the next BA field on its side (because a beam switch can only be done during a BA field). The simultaneous transfer of RX and TX beam coefficients guaranties that the delay between RX and TX beam switches is kept to a minimum: the maximum delay is approximately the length of a radio frame.

The last point to address in this TX BA procedure is the precise timing of the TX switch. If we only consider the alignment of the local beams, then the TX switch could happen at any point during its transmitted BA field. It is however possible to achieve better functionality. In previous section, we highlighted the fact that to perform the local RX alignment, the remote TX beam has to stay constant during P1 and P2. This was the only constraint involving the two ends of the link. Therefore, if the TX switch is restricted to P3 in its transmitted BA field, the whole procedure becomes "reversible": it can be applied, independently, in both ends of the link (local and remote). The timings ensure that the local and remote beam switches never disturb the BA procedures of the other endpoint. Also, it can be noted that the whole procedure does not require any information from the other end-point and only requires a local ∆-RSL measurement.

C. Beam alignment periodicity

Section III-C showed that an SPI frame lasts about 520 µs. Section IV-A mentioned that a radio frame lasts between 180 µs and 1 ms. Finally, we also know from section IV-B1 that once a beam configuration has been sent, the PAM has to wait for a BA field before starting the BA procedure. Given these data, the expected periodicity of the BA procedure can be determined for various radio frame durations. This is shown in Fig. 10 with two curves: the maximum delay (solid) between two BA procedures, and the average one (dashed). This distinction stems from the fact that the fourth SPI transaction of an SPI frame is unrelated to the BA procedure. If a BA field is received during this transaction, the BA procedure can be performed there. In this case, the next SPI frame (so the next beam configuration) can be sent immediately after the end of the previous one. If however a BA field is not received during the fourth transaction, an additional delay is necessary at the end of the SPI frame. These two cases can happen alternatively, depending on the radio frame duration.

In the figure, we can clearly see thresholds: some radio frame durations are more desirable than others as far as BA is concerned. For example, a radio frame of 390 µs leads to a (constant) BA periodicity of 780 µs, while a radio frame of 400 µs leads to an average periodicity of 600 µs (with a maximum delay of 680 µs). The latter therefore offers a 30 % increase in average BA frequency, while also allowing a slightly bigger payload. The BA periodicity typically lies between 500 µs and 1000 µs. With a radio frame of 180 µs, the expected periodicity is 540 µs.

V. BEAM SELECTION ALGORITHM A. Codebook design

In the following, we consider the reference frame of a vibrating antenna. In this reference frame, the direction of each beam is constant and the remote site is moving. As mentioned previously, our solution relies on a codebook containing the coefficients to apply to the antenna elements. One set of coefficients makes one beam, pointing in a given direction. This set of predefined beams must be carefully chosen.

1) Overlap: An important parameter is the overlap between adjacent beams. A high overlap means that it is possible to switch beams earlier (i.e. with less gain loss). However, more beams are necessary in the codebook to cover a solid angle. The handoff process between two adjacent beams is illustrated by Fig. 11; the arrow represents the movement of the gNB from beam A to beam B. In this figure, the two circles represent the x dB aperture of each beam, i.e. the area where the beam gain is higher than the maximal gain minus x dB. x is representing the beam overlap and is a parameter specifying the maximal allowed system loss. The dotted vertical line is the 'handoff frontier': if the alignment procedure is performed before crossing this line, beam A will be selected, whereas beam B will be selected if it is performed after. In order not to degrade the communication link, it is therefore mandatory to assess beam B after gNB crosses this frontier, but before it leaves the range of beam A. The allowed time to find beam B is thus t on Fig. 11. On the one hand, the higher the frequency of the vibrations the shorter the time t, and on the other hand, due to the HW constraints described above, only a few beams can be assessed during this interval. All these parameters must therefore be taken into consideration when choosing the overlap of beams. 12, without showing the actual overlap for clarity. According to the vibrations specification described in an earlier section, the North-South direction of the gNB has been found more significant than the East-West direction (sways have a much bigger amplitude than twists). This asymmetry can be taken into account in the design of the codebook by setting more beams in the North-South direction, and introducing a higher overlap between them in this direction. The final beam arrangement will be decided later in the project, when other parameters are set in stone.

Radial distribution

Disk uniform distribution Rectangular grid 3) Calibration: Due to variations in the manufacturing process, simply applying theoretically computed coefficients to the antenna elements does not necessarily give satisfactory results for a given beam direction. Therefore, a calibration process is required to finely tune the phase shifts to be applied to the antenna elements. The resulting beam configurations are stored in the codebook. Furthermore, the HW is sensitive to temperature changes, so as many sets of coefficients as specified temperature ranges must also be calibrated and stored. Currently, the codebook for one antenna is intended to store beams for about 25 directions and 5 temperature ranges, for a total of 125 beams. This calibration phase is out of the scope of this paper as it will be realized later in the project.

B. Selection algorithm

To determine the order in which beams are tested, and to validate the BA strategy, a house-made software simulator has been designed. It models the link between a mast TX antenna and a vibrating pole RX antenna at the small cell side, as specified on Fig. 2. It allows to set the gain, at RX side, of each beam of a specified codebook in the direction of the gNB over time, for any values of amplitude and frequency for the twists and sways movements.

The result for the specifications of the DRAGON project given by Table I are shown on Fig. 13: the colored thin lines represent the gain of each of the 25 beams at the pole RX over time. The goal of the BA strategy is to stay as much as possible at the highest achievable gain, switching if necessary from one beam to another. The black bold line is the resulting continuous gain from the beam selected by the algorithm over time. The strategy on Fig. 13 is to test the beam on the North (N) of the current beam, then South (S), then another beam direction; and then repeat this sequence of three, ending each time with a different test direction. If the beam is switched the sequence starts again, relatively to the current new beam. The periodicity is 1 ms (i.e. a new beam is compared to the current one every 1 ms), which can be considered as the worst case scenario, see IV-C. With these parameters, the maximum misalignment loss with respect to the maximal gain (35 dBi) is about 1.5 dB: the black bold line always stays above 33.5 dBi. The figure also shows power drops that happen at beam switches, such as the ones in the red ovals. These drops appear because the BA periodicity is not null. The small delay in BA causes a loss with respect to an optimal, instantaneous, selection. As shown in Fig. 13, this loss is however considered as small for the system: less than 1 dB at max.

VI. CONCLUSION AND NEXT STEPS

In this paper we have presented a beam alignment solution for D-band antennas subjected to vibrations. This solution takes into account HW constraints imposed by other elements of the system (antenna architecture, phase shifters' memory space, arbitration of SPI communications). An in-house simulator has been designed, which led us to conclude that the chosen BA strategy allows to satisfy the vibration specifications set out in the project. This procedure will therefore be If even more stringent antenna motion specifications were to be established, the systematic beam testing strategy would have to be reviewed. We could then imagine using machine learning to predict the most likely beams based on predicted pole and mast movements. The geometry of the codebook could also be further optimized to better fit the motion.

Fig. 2 :

 2 Fig. 2: Considered antenna vibration motions (fast due to wind gusts and slow due to sunlight)

Fig. 4 :

 4 Fig. 4: Block diagram of one transmitter IC containing four RF chainsIn the transmitter part, a direct I/Q up-conversion is used to translate the BB signals to D-Band. The D-Band signal is then distributed to the RF chains feeding each antenna cluster. Each RF-chain is comprising a phase-shifter for beam steering of the transmitting antenna array. The configuration of each phaseshifter is adjusted from the PAM-board. Aside from that, the gains in each RF chain can be controlled from the TC-board to maximize the EIRP.In the receiver part, the D-band signal received by each antenna cluster is fed to a different RF-chain. Each RF-chain is comprising a phase-shifter for beam steering of the receiving antenna array. Again, the configuration of each phase-shifter is adjusted from the PAM-board and the gains in each RF-chain are maintained stable from the TC-board. The combination of the signals coming from the different RF-chains are fed to an IQ down-converter to generate the received BB IQ signals.To set a beam direction, a 22-bit coefficient must be written to each of the 256 phase shifters managing antenna clusters.

Fig. 5 :

 5 Fig. 5: Simplified view of the phase shifter registers.

Fig. 6 :

 6 Fig. 6: Frame structure including BA fields.

Fig. 7 :

 7 Fig. 7: RX beam alignment procedure (not to scale).

Fig. 8 :Fig. 9 :

 89 Fig. 8: Beam angle deviation with various local RX -local TX spacing, for θ sway = 4°(90°vibration plane) and θ twist = 1°.

Fig. 10 :

 10 Fig. 10: Beam alignment periodicity as a function of the radio frame duration.

Fig. 11 :

 11 Fig. 11: Movement of the gNB antenna (in the small cell antenna frame of reference) from beam A to beam B2) Geometry: Some examples of beam arrangements stored in the codebook are depicted in Fig.12, without showing the actual overlap for clarity. According to the vibrations specification described in an earlier section, the North-South direction of the gNB has been found more significant than the East-West direction (sways have a much bigger amplitude than twists). This asymmetry can be taken into account in the design of the codebook by setting more beams in the North-South direction, and introducing a higher overlap between them in this direction. The final beam arrangement will be decided later in the project, when other parameters are set in stone.

Fig. 12 :

 12 Fig. 12: Example of beam arrangements stored in the codebook

  all four directions Codebook geometry Rectangular 2 rings, 25 beams: Fig. 12 Sequence of beams assessed, relative to the current beam N,S,E,N,S,W,N,S,NE,N,S,SW,N,S,SE, N,S,NW and repeat NOTE: the distances between the mast and the pole and their relative height have little influence on the results.

Fig. 13 :

 13 Fig. 13: Beam tracking with 1 ms periodicity. Thin colored lines are the gain of each beam of the codebook in the direction of the gNB. The black bold line is the continuous gain resulting from beams selected by the algorithm.

TABLE I :

 I Parameters for the definition of codebook and algorithm.


Nicolas Cassiau

Ilgyu Kim

Emilio Calvanese Strinati

Gosan Noh

Antonio Pietrabissa

Fabrice Arnal

Guido Casati

Taesang Choi

You-Jun Choi

Heesang Chung

Sylvain Colombero

Pierre Dal

Emanuele De Santis

Jean-Baptiste Doré

Alessandro Giuseppi

Jean-Michel Houssin

Junhyeong Kim

Marc Laugeois

Federico Pigni

Xavier Popon

Leszek Raschkowski

Marjorie Thary

Seok Ho

5G-ALLSTAR: Beyond 5G Satellite-Terrestrial Multi-Connectivity

Keywords: 5G, multi-connectivity, UE satellite 5G direct access, terrestrial 5G access, traffic control

This paper presents a summary of the results of the 5G-ALLSTAR project. It describes the enablers that have been developed and validated and will help make 5G and beyond satellite-terrestrial multi-connectivity (MC) a reality in the near future. We proposed and evaluated solutions for critical aspects of the integration of non-terrestrial networks into a 5G and beyond terrestrial network. The OpenAirInterface implementation of the 5G physical layer (PHY) has been upgraded to meet the satellite radio channel constraints. We addressed the issue of co-tier interference between satellite and terrestrial systems. We designed and implemented customized 5G Physical layer, specifically adapted for terrestrial-satellite spectrum sharing. On top of the dedicated beam-forming and hardware design, we validated the full potential of MC by conceiving and testing our proposed resource allocation algorithms based on a custom multipath TCP protocol. The contribution of MC in vehicular use cases has been demonstrated onsite by implementing a terrestrial 5G PHY in conjunction with a satellite/terrestrial traffic controller. Finally, radio resource management solutions were examined. Thanks to these tools, the presence of industry partners in the consortium and to an active participation in standardization, the 5G-ALLSTAR project is an accelerator for the integration of non-terrestrial networks in 5G and beyond.

I. INTRODUCTION

The European-Korean joint research project 5G-ALLSTAR (5G AgiLe and fLexible integration of SaTellite And cellulaR), 2018-2021, aimed to demonstrate satellite-terrestrial multiconnectivity (MC), a key feature to ensure 5G performance anywhere and anytime.

The integration of non-terrestrial networks (NTN) into terrestrial communication standards has been studied since the

The research leading to these results has received funding from the European Union H2020 under grant n. 815323 and is supported by the Institute for Information & communications Technology Promotion (IITP) grant funded by the Korean government (MSIT) (No. 2018-0-00175, 5G AgiLe and fLexible integration of SaTellite And cellulaR). development of 4G. Today, after having released the first generation of 5G networks and planning research for 6G networks [START_REF] Strinati | 6G in the sky: On-demand intelligence at the edge of 3D networks (invited paper)[END_REF], convergence of these standards could not be achieved yet. The advent of 5G changed the game. Industry and academia reached the consensus that NTNs will play an fundamental role in 5G and beyond standards: satellite, aerial, and terrestrial platforms will jointly enable connectivity and support of on-demand edge cloud and intelligence services also on rural areas and in the three-dimensional space [START_REF] Strinati | 6G in the sky: On-demand intelligence at the edge of 3D networks (invited paper)[END_REF].

Research activities and engineering of technology for making an operational reality the integration of satellite and terrestrial networks is recently exploding. Among these, the demonstrator in [START_REF] Völk | Satellite integration into 5G: Accent on first over-the-air tests of an edge node concept with integrated satellite backhaul[END_REF] and [START_REF] Liolis | Over-the-air demonstration of satellite integration with 5G core network and multi-access edge computing use case[END_REF], from the ESA ARTES AT project SATis5, uses the 5G network core. Another ESA ARTES project, ASCENT [START_REF] Höyhtyä | Licensed shared access field trial and a testbed for satellite-terrestrial communication including research directions for 5G and beyond[END_REF], has conducted field trials of a system sharing satellite-terrestrial spectrum. The authors use interference measurements to free up occupied frequency resources that can be used by a predefined primary user. The H2020 5G-PPP project SaT5G [START_REF] Goratti | Satellite integration into 5G: Accent on testbed implementation and demonstration results for 5G Aero platform backhauling use case[END_REF] [START_REF] Evans | An integrated satellite-terrestrial 5G network and its use to demonstrate 5G use cases[END_REF] demonstrates through measurements the possibility of 5G connectivity to mobile platforms (such as aircraft).

In contrast to the above-mentioned projects, which only consider the satellite as a backhaul link, and building on the 5GCHAMPION project findings [START_REF] Strinati | 5GCHAMPION Disruptive 5G Technologies for Roll-Out in 2018[END_REF], the 5G-ALLSTAR project focuses on demonstrating MC in the direct user-tosatellite access. Therefore, here we present original solutions and tools specifically dedicated to this goal.

The structure of the paper is as follows. Section II introduces the concepts of the project and how they translate into technological requirements. In the third section, each enabler developed in the project is described along with its means of demonstration. Finally, Section IV looks at the impact of the project. 5G networks are intended to be a mix of heterogeneous radio access technologies (RATs) and a variety of services/applications defined by the needs of vertical players (e.g. energy, transport, healthcare, content delivery, public safety/emergencies, etc.). Possible application domains comprise autonomous cars, massive wireless sensors, virtual/augmented reality (VR/AR), industrial automation, etc. These new services/applications are characterized by very different end-to-end requirements and each application entity has customized quality of experience (QoE) specifications based on context and user feedback. For example, VR/AR applications need to achieve high data throughput with minimal latency; Supporting large numbers of wireless sensors requires the ability to create and manage a multitude of links; Automotive and industrial automation applications need to be supported by a highly reliable and very low latency link.

These emerging services/applications will be enabled by a mix of tightly coupled existing and new RATs, encompassing cellular access such as 5G New Radio (NR), 4G LTE and WiFi, as well as non-terrestrial access such as satellite. Therefore, the tight integration and inter-working of various RATs, combined with the design of appropriate resource sharing algorithms, can enable the end-to-end needs of each application instance to be met while maintaining network efficiency, security, and scalability with the integration of 5G and non-5G RATs. This is referred to as an integrated multi-RAT network. In order to make such a network a reality, a number of concepts were identified in the project. These concepts are illustrated in Fig. 1, details and challenges are provided below.

A. Broadband and reliable access to 5G services for users

The first concept is 5G access everywhere. To this end, the feasibility of NR-based satellite direct access in the millimeter wave (mmWave) band to provide broadband and reliable 5G services will be demonstrated. Besides verifying the defined 5G satellite access architecture, which is currently being developed in the standardization organization 3GPP, works, the various challenges arising from the mobility of satellite and the propagation delay have to be solved appropriately. As for the terrestrial physical (PHY) layer, it needs to be designed to cope with mobility and should allow spectrum sharing with satellites.

B. Resource sharing

Due to the differences between terrestrial and non-terrestrial RATs, an important point should be to carefully select the most appropriate RAT layers and RAT network nodes in which the resource sharing functionalities should be embedded. In this regard, two concepts have been identified: resource sharing capabilities at Packet Data Convergence Protocol (PDCP) and higher layers referred to as multi-connectivity, and resource sharing capabilities at the PHY/Medium Access Control (MAC) layer, referred to as spectrum sharing.

1) Multi-Connectivity: MC enables a User Equipment (UE) to connect to multiple bases stations simultaneously and thus user-plane data aggregation. MC can be realized between the same access links, between different access links, or even between different access technologies. In 3GPP LTE, a dual connectivity feature has been integrated in Release 13, allowing a UE to connect to two eNodeBs. This dual connectivity framework can be extended to inter-RAT aggregation, e.g., LTE-WLAN interworking. In the PHY layer and higher layers, dual connectivity affects synchronization, power control, and scheduling. Support for MC between 5G and LTE air interfaces has also been studied. The tight integration of 5G and LTE air interfaces is expected to meet several 5G requirements, including faster mobility and resource management [START_REF] Silva | Tight Integration of New 5G Air Interface and LTE to Fulfill 5G Requirements[END_REF]. A study was also conducted on inter-RAT MC between, for example, 2G/3G, LTE, and 5G networks. Identified new functionalities related to inter-RAT MC include among others RAT selection, operating mode selection, traffic management, control and data flow routing [START_REF] Ravanshid | Multi-connectivity functional architectures in 5G[END_REF]. MC was also studied in ETSI TR 103 272 (Satellite Earth Stations and Systems ; Hybrid FSS satellite/terrestrial network architecture for high speed broadband access) and in TR 103 351 (Satellite Earth Stations and Systems ; Multi-link routing scheme in hybrid access network with heterogeneous links). The challenge is to manage unbalanced access links in terms of Quality of Service (QoS) including bandwidth, latency, error rate and jitter.

2) Spectrum sharing: To enable spectrum sharing, dynamic technologies and algorithms should be analyzed, designed and implemented. These include advanced MAC layer functionalities to handle the band overlap between different systems using the same or adjacent frequency bands and advanced PHY layer functionalities to manage interference, with the aim of ensuring a robust service thanks to advanced signal processing techniques.

C. Mapping to enablers

The above-mentioned concepts were mapped to technological enablers, see Table I. The goal of the project was to implement and verify these enablers in order to validate the concepts. The table shows how the enablers were demonstrated, either through software simulations, through implementation in local testbeds or in the final trial platform. Section III describes the work done for each enabler.

III. ENABLERS AND THEIR DEMONSTRATION

A. Satellite 5G direct access 5G-ALLSTAR provided direct satellite access to the 5G NR UE by developing a satellite-friendly modem based on OpenAirInterface (OAI). OAI is an implementation of the 3GPP mobile communications standards. It currently supports LTE UE, evolved NodeB (eNB) and Evolved Packet Core. The development of the 5G NR standards, starting with 3GPP Release 15, is ongoing. The main challenge for implementing the satellite-friendly modem was adapting OAI's NR procedures to the long-delay scenarios, e.g. for geostationary (GEO) satellites. For this purpose, the following features were developed during the project: (i) Extension of the K2 slot offset (i.e., the offset between the downlink (DL) slot for uplink (UL) scheduling and the corresponding UL data slot) to at least double the one-way delay between 5G The satellite modems were implemented on the same hardware (HW) for both UE and gNB, based on the Ettus USRP X300 Software Defined Radio platform equipped with a CBX-120 daughter board. The frequency range is tunable from 1200 MHz to 6000 MHz, channels bandwidth is available up to 120 MHz, and both Time and Frequency Division Duplex modes of operation are allowed.

Over-the-air tests were conducted using a GEO satellite. The UE performed the initial connection setup to the gNB using a specifically adapted random access method for 5G over satellite. After successful connection establishment, the 5G UL and DL transmission signals were decoded with QPSK and 16-QAM modulations. In addtion, a smoothly functioning 5G timing advance procedure was tested to keep the UE synchronized to the gNB [10].

B. Terrestrial 5G access 1) Waveform for spectrum sharing: In the European testbed, Block-Filtered (BF)-OFDM was chosen as the wave-form for the terrestrial link. This waveform was designed by CEA in 2016 [START_REF] Demmer | Block-Filtered OFDM: a novel waveform for future wireless technologies[END_REF]. The following characteristics made it a good candidate for the 5G-ALLSTAR concept: it is fully compatible with a 5G NR receiver, the out-of-band rejections are high (allowing efficient spectrum sharing) and it can be configured in real-time per sub-band (hence supporting multiservice scenarios). The challenge in 5G-ALLSTAR was to provide a broadband (up to 400 MHz) HW implementation of BF-OFDM on a Xilinx Radio Frequency System-On-Chip (RF-SOC) platform from the ground up. In achieve this, a number of obstacles were overcome: (i) the support of multiple configurations and parameters, (ii) the high bandwidth in terms of board clock frequency and (iii) the intrinsic complexity of the waveform. The provided solutions are described in [START_REF] Doré | FPGA Implementation of a Wideband Multi-Gb/s 5G BF-OFDM Transceiver[END_REF]. The transceiver has proven to be fully functional with tunable parameters such as bandwidth, numerology, Modulation and Coding Scheme (MCS) and active sub-bands.

The smooth transmission of a low-latency VR tennis game plus an 8K video stream through the terrestrial testbed was demonstrated over-the-air in the 26 GHz band with a tx-rx distance of around 3 m using 10 dBi horn antennas [START_REF]Integration and system level testing of proof-of-concept phase 2[END_REF].

2) mmWave vehicular communications air interface: Our Korean partners have developed a mmWave 5G NR vehicular communication system that provides a mobile broadband access point on board a vehicle for a large number of passengers such as a bus. The wireless backhaul air interface basically conforms to the specifications of 5G NR Release 15. Considering the mobility of the vehicle, the physical layer is designed to support a high subcarrier spacing of 60 kHz and a dense allocation of the Demodulation Reference Signal in the time domain to compensate for the high Doppler effect [START_REF]Integration and system level testing for Korean testbeds of 5G cellular and satellite access networks[END_REF]. Maintaining backhaul connectivity between the vehicle and the network is made more difficult in the considered highway and urban road target deployment scenarios, since the vehicle's direction of travel can change, for example during overtaking maneuvers or when driving around a curve. To address these challenges, the system is further designed to support an open-loop beam switching technique dedicated for vehicular communications. The details of beam switching are discussed further in Section III-E. In addition, several techniques such as 2x2 Multiple Input Multiple Output transmission with linearlypolarized antennas and fast handover have been implemented.

To validate the feasibility and effectiveness of the mmWave vehicular communication system, a prototype system was implemented, and field tests were conducted in urban and highway environments. The tests results showed that the system with the developed technologies is capable to provide broadband wireless connectivity between the vehicle UE and the gNB, enabling the realization of the target service [START_REF]Integration and system level testing of proof-of-concept phase 2[END_REF]. However, during the field testing, it was observed that there is performance degradation depending on vehicle locations, mainly due to line-of-sight signal blockage, which is more significant in the high-frequency mmWave band. Therefore, to complement blocking problem of the mmWave cellular system and to ensure service continuity, the support of MC between cellular and satellite networks is also realized, the details of which are described in Section III-D.

C. Traffic load balancing

Resource sharing at the PDCP and higher layers are made possible by MC functionalities. The 5G-ALLSTAR project analyzed, designed and simulated multi-RAT load-balancing algorithms that decide about traffic steering between the available RATs. These algorithms are:

• Cognitive (i.e. context-aware), since they are expected to take their decisions on the basis of the present performance in terms of the Key Performance Indicators -defined for the various use cases -measured in the candidate RATs; thus proper sensing (i.e. monitoring) functionalities able to provide real-time measurements on the present RAT status should be foreseen. • Personalized, since they need to make their decisions to meet the specific end-to-end Service/Application Requirements as well as the personalized end-to-end Quality of Service/Quality of Experience requirements of the running applications (the latter are reduced by an ad-hoc Personalization System). • Dynamic, since they are expected to change their decisions in real-time depending on the current RAT state and the dynamics of the traffic generated by the running applications they support. Load balancing algorithm decisions are made by adopting sophisticated methodologies such as optimization techniques and learning algorithms [START_REF] Priscoli | Capacity-Constrained Wardrop Equilibria and Application to Multi-Connectivity in 5G Networks[END_REF] [START_REF] Giuseppi | Wardrop equilibrium in discrete-time selfish routing with time-varying bounded delays[END_REF]; These decisions concern link aggregation (i.e., the use of multiple RATs to support a specific flow, e.g., to enhance the throughput), the split of each flow among the selected RATs in appropriate percentages, the triggering of vertical (inter-RAT) handovers induced by interference and/or traffic, the QoS requirements to be satisfied by each underlying RAT for each specific flow, etc.

Adaptive Video Streaming clients and servers were used to validate the algorithms [START_REF]Integration and system level testing for European testbed of 5G cellular and satellite access networks[END_REF]; Two virtual machines (VMs) have been set up (one for the server and one for the client). The Server VM was connected to the Client VM via two (virtual) paths emulating satellite paths (with high delay) and terrestrial paths. Developing such functionality required addressing several issues, including adapting the open standard Multipath TCP (MPTCP) mechanism. To prove that a Multipath implementation makes it possible to determine the quota of data to be sent over multiple links in user space (satellite and 5G), a modified version of MPTCP's round-robin scheduler was implemented in the project. It has been shown that the Traffic Flow Controller dynamically changes and the custom MPTCP satellite / terrestrial quotas in real-time, as expected, and that the video streaming is able to reach highest quality level.

D. Traffic Control

The MC functionality of the Korean testbed is based on an Internet Protocol (IP) layer traffic aggregation of heterogeneous networks consisting of a satellite network and a cellular network. An active GEO satellite (i.e., Koreasat-6) is used for the MC tests, using a digital video broadcasting waveform common to commercial satellite service networks. The cellular network adopts the 3GPP Release-15 standards as the basis for implementation. The cellular and satellite networks are integrated with an IP layer traffic controller (TC) located at the UE side.

The MC functionality operates on a vehicle UE associated with the TC. Note that the TC module is located at the UE side to provide uninterrupted connectivity with the vehicle UE. In TC configuration, all the packets are duplicated from an application server to the TC over both satellite and cellular links, and one stream is selected from two output streams of the TC. With the redundancy from duplication, the overall reliability in transferring data is improved, facilitating service continuity in video streaming, for example. To further improve data throughput or reduce latency, in addition to the TC functionality at the UE side, sophisticated algorithms for load balancing or packet splitting at the network side can also be considered.

Another feature of the Korean MC concept concerns the asymmetry in data throughput between the cellular and satellite networks. The cellular access network can allocate a total bandwidth of 300 MHz to a vehicle UE, whereas the satellite access network can only allocate up to 2 MHz, which is proportional to the available budget for the commercial service. With the above difference in allocated bandwidths, the cellular access path can support high-definition video streaming services like 4K, whereas the satellite access path has some video quality and latency limitation. Therefore, the service scenarios of this use case have taken this reality into account.

The MC concept was validated in trials with a mini-bus connected simultaneously to a 5G gNB and an operating satellite [START_REF]Integration and system level testing of proof-of-concept phase 1[END_REF]. Voice over IP (VoIP) and video conferencing services have been tested and satisfactory results have been shown.

E. Beamforming

The project investigated a dual beamforming (BF) technique that can mitigate interference between the satellite and cellular networks. The goal was to introduce a way to improve the communication quality of the cellular network while limiting the overall interference from the cellular base stations to the satellite Earth station to a certain level. Considering a realistic satellite-cellular sharing scenario with both satellite and cellular links operating in the DL, the proposed BF technique attempts to maximize communication performance while the amount of maximum allowable interference at the satellite Earth station is limited to a predefined level. More specifically, a combination of Zero Forcing (ZF) BF using the null space of the interference channel and Maximum Ratio Transmission (MRT) BF is provided, which can maximize the channel gain of the cellular link. The proposed dual BF has been shown to outperform BF schemes based on either MRT BF alone or ZF BF alone [START_REF] Noh | Apparatus and method for acquiring sidelink synchronization in wireless communication system[END_REF] [START_REF] Jung | Method and apparatus for interference mitigation based on beamforming[END_REF].

As a simpler form of BF functionality, a UE-centric beam switching scheme was implemented in the Korean testbed. The beam switching functionality is embedded in a vehicle UE within a cellular network. Every 240 ms, based on the measurement results, the best beam is selected from three beam set candidates for communication. In addition, beam switching not only makes it possible to find the best beam that delivers the strongest received signal power, but also reduces the effects of severe interference by not selecting the transmit/receive antenna with high interference. Since the beam switching operation is based on the measurement of the synchronization signal block (i.e., primary/secondary synchronization signal and broadcast signal), the beam switching module has the ability to discriminate between the desired signal and interference.

F. Radio Resource Management

With 5G-ALLSTAR, a concept was researched that is based on a dynamic coupling of both cellular and satellite access at Radio Resource Management (RRM) in order to ensure safe coexistence of both systems. This required investigating the parameters (e.g. spectrum partitioning, flux density, transmission power, antenna characteristics) to be exchanged between the RRM schemes of both access technologies concurrently accessing the same spectrum. The RRM has been defined considering the most relevant interference scenarios related to the typical satellite system deployment foreseen for 5G system (3GPP TR38.811). Great care has been taken to integrate joint RRM schemes into the 5G system/radio access network architecture and comply as much as possible with the relevant protocols/interfaces specified by 3GPP (e.g. F1, X2 interfaces). A possible extension of Enhanced Inter-Cell Interference Coordination, an interference control technology, to Hetnets that takes satellite components into account was considered to mitigate interference from inter-access (cellular/satellite) technologies and the need to modify the relevant protocols was evaluated.

An NS-3 based simulator was developed to evaluate different schemes [START_REF]Interference mitigation techniques[END_REF]. Interference on the terrestrial and non terrestrial entities were modelled. Conclusions were drawn and recommendations for implementation in testbeds were provided.

IV. IMPACT A. Socio-technological and socio-economic impacts

Part of 5G-ALLSTAR's efforts have been devoted to 5G business assessment and exploitation towards vertical stake-holders. We have first provided an overall summary of the current state of 5G market in 2020. We have found that mainstream adoption of 5G is still very limited; even industry presents itself in its early stages. The gaps and challenges structuring the current supply and market, some of which may be related to possible applications from 5G-ALLSTAR innovations, have been identified. A second time, we discussed the emerging configurations based on both a desk analysis of the available literature and studies, and direct interviews conducted with key informants while implementing qualitative methods. Two rounds of interviews were conducted by our panel of experts, selected based on criteria such as relevant experience in the field, expertise, knowledge of 5G and business models and an insight into trends within 5G for Business to Business and consumer markets. Our first round of interviews included general, open ended, exploratory questions that were further improved upon within the second round based on responses received to the first set of questions. This allowed us to understand 5G networks and frame our discussion around key dimensions of the business models in terms of balancing risks and promises while managing tensions and synergies to provide a holistic view of what the upcoming 5G business models in industry is to be expected. For data analysis, we coded our interviews with NVivo. These codes were reassembled to develop concepts that led to the identification of the main topics of discussion elaborated in the results. Finally, based on the Joint Analysis literature, available cases, and interviews, we discussed the opportunities for 5G-ALLSTAR technologies in the identified vertical markets (transport, public safety, rural area), and helped to define viable strategies with regard to the different aspects of business models. The interested reader is referred to [START_REF]Business assessment for vertical markets empowerment[END_REF].

B. Standardization

5G-ALLSTAR included standardization activities to incorporate technologies developed within the project into the 5G system definition, mainly at 3GPP. Indeed, one of the goals of the project was to contribute to the definition of the 5G system as part of Release 16 and beyond by including 5G satellite access, mobile wireless backhaul and multiple access/connectivity. The plan was to leverage ongoing standardization for eMBB and 5G satellite access. Additional standardization activities in bodies such as ETSI, IEEE and ITU, have been considered in complement to 3GPP activities. Submissions were made to selected working groups to support product adoption and interoperability developed within the project, e.g. R1-1908811 and R1-1908810 focus on physical layer techniques and sidelink synchronization for NR V2X.

V. CONCLUSION

The tools discussed in this paper were presented during a final intercontinental proof-of-concept demonstration, see Fig. 2. Players ➀➁ and spectators ➉ enjoyed a VR tennis game in France and Korea while being filmed ➂➇. Both sites benefited from terrestrial / satellite dual-connectivity (emulated satellite in France). Details on the demonstration can be found in

Fig. 1 :

 1 Fig. 1: 5G-ALLSTAR concept architecture II. PROJECT CONCEPTS

  Base Station (gNB) and UE; (ii) Introduction of an offset for UE Physical Uplink Shared Channel (PUSCH) transmission; (iii) Introduction of an offset for the transmission slot of DL Control Information scheduled PUSCH; (iv) Additional slot number equal to at least twice the one-way delay between gNB and UE; (v) Extension of the random access response window; (vi) Extension of the UL future Transmission Time Interval (TTI) request and the UL Virtual Resource Block (VRB) mapping; (vii) Increasing the Timing Advance update period; and (viii) disabling Hybrid Automatic Repeat Request processes.

TABLE I :

 I Project concepts, enabling technologies and means of demonstration

	Concept	Enabling technology	Sim.	Testbed	Trial
	Provide broadband and reliable direct to UE 5G services.	Satellite friendly 5G NR PHY & MAC 5G NR terrestrial PHY
	Implement multi-connectivity support and integrate 5G Terrestrial	Traffic Load Balancing
	and Satellite access systems.	Traffic Control
		Dual beamforming
	Enable Terrestrial and Satellite access to share the same spectrum.	High OoB rejection waveform
		Radio Resource Management

NOTE: Sim., Testbed and Trial respectively stand for demonstration by software simulations, by implementation in a local testbed and by trials.

 [START_REF]Integration and system level testing of proof-of-concept phase 2[END_REF]. This scenario enabled the validation of low latency (VR tennis game) and high-throughput (8K video streams ➅➆) 5G transmission in real-world conditions, leveraging the best of both networks.

Thus, over a period of forty months, the 5G-ALLSTAR project has designed, developed and demonstrated technological building blocks that have validated the original concept of satellite-terrestrial multi-connectivity for 5G and beyond. This work paves the way for full seamless and transparent three-dimensional coverage of the globe.


• Adherent cells in 2D culture (Fibroblasts)

• Cells positives for the surface determinants CD73, CD90 and CD105

• Cells negative for hematopoïetic markers :CD34-, CD45-, CD14-, CD79a-, HLA-DR-

• Multipotency in vitro and in vivo at least for osteoblastic, chondroblastic and adipocytic lineages (mesodermal)

Adult pluripotent stem cells: do they exist ?

The discovery of mesenchymal stem cells (MSCs)

One unique multipotent stem cells with enlarged potency

• Originally discovered in the Bone Marrow

• Can be isolated from cord blood, placenta, circulating blood, bone marrow, dental pulp, cartilage, adipose tissue, skeletal muscle, blood vessel (pericytes) etc

• MSCs seem to be elsewhere in the organism but in extremely low proportion (0,01% -0,001% in BM MNCs)

Adult pluripotent stem cells: do they exist ?

The discovery of mesenchymal stem cells (MSCs)

One unique multipotent stem cells with enlarged potency

•

  Formation of teratomas (injection into SCID mice) : tissues derived of all three embryonic germ layers • In vitro differentiation • No evidence for chimera Human embryonic stem cells are equivalent to mouse EpiSCs (epiblast stem cells) Identity and stem cell potential marker expression : FACS, qRT-PCR, immunofluorescence… self-renewal / differentiation (in vitro/in vivo) Impact on the differentiation 3. Amplyfying virus for vaccine and screening antiviral vitro differentiation: Male primordial germ cells