Marco Giordani
email: giordani@dei.unipd.it

Andrea Zanella
email: zanella@dei.unipd.it

Takamasa Higuchi
email: ta-higuchi@us.toyota-itc.com

Onur Altintas

Michele Zorzi
email: zorzi@dei.unipd.it

Performance Study of LTE and mmWave in Vehicle-to-Network Communications

Keywords: V2N communications, millimeter wave (mmWave), LTE, connectivity performance

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I. INTRODUCTION

Emerging advanced automotive services aim at enhancing the road safety, reducing the environmental impact of the traffic and the fuel consumption, and supporting infotainment applications [START_REF] Gpp | Technical Specification Group Services and System Aspects; Enhancement of 3GPP support for V2X scenarios; Stage 1 (Release 15)[END_REF]. Such services can greatly benefit from Vehicle-to-Vehicle (V2V) and Vehicle-to-Network (V2N) communications, which would make it possible to realize cooperative perception and maneuvering, e.g., by enabling the sharing of sensor data and driving decisions among vehicles and infrastructure nodes [START_REF] Hartenstein | VANET vehicular applications and inter-networking technologies[END_REF]. Currently, the principal wireless technology supporting V2V communications is the IEEE 802.11p standard, which offers data exchange at a nominal rate ranging from 6 to 27 Mbps within a range of a few hundreds of meters [START_REF] Kenney | Dedicated Short-Range Communications (DSRC) Standards in the United States[END_REF]. V2N communications, instead, presently use the Long Term Evolution (LTE) connectivity below 6 GHz, enabling a data rate of up to 100 Mbps in high mobility scenarios [START_REF] Araniti | LTE for vehicular networking: a survey[END_REF].

However, the data generation rate of vehicles can soon reach the order of terabytes per driving hour, exceeding the capacity of traditional technologies for vehicular communications [START_REF] Va | Millimeter wave vehicular communications: A survey[END_REF]. In this regard, the 3rd Generation Partnership Project (3GPP) has recently investigated new Radio Technologies (RTs) as enablers for the performance requirements of future automotive services, including the millimeter wave (mmWave) spectrum -roughly above 10 GHz [START_REF] Choi | Millimeter-Wave Vehicular Communication to Support Massive Automotive Sensing[END_REF]. Besides the large bandwidths available at such frequencies, the small size of antennas at mmWaves makes it practical to build very large antenna arrays and obtain high gains by beamforming (BF), thereby guaranteeing extremely high transmission speeds [START_REF] Rappaport | Millimeter wave wireless communications[END_REF]. On the other hand, the severe isotropic path loss and the harsh propagation characteristics of the mmWave channel, as well as the dynamic topology of the vehicular networks, have raised many concerns about the applicability of this technology to a vehicular context. Moreover, mmWave links are typically directional and require precise alignment of the transmitter and receiver beams to maintain connectivity, an operation that may degrade the connection performance and lead to network disconnections.

Motivated by these considerations, in this study we investigate the reliability and efficiency of V2N communication when using mmWave and LTE technologies. More specifically, we conducted an extensive simulation campaign to (i) compare the performance of the mmWave and the LTE technologies in terms of achievable data rate, communication stability and signal outage probability under realistic dynamic scenarios, and (ii) assess their benefits and potential shortcomings in relation with target V2N application requirements. In order to have realistic movement of the vehicles in an urban environment, we generate some mobility traces using Simulation of Urban MObility (SUMO) [START_REF] Krajzewicz | Recent development and applications of SUMO -Simulation of Urban MObility[END_REF], an open road traffic simulator designed to handle and model the traffic of large road networks. The results show that the support of high-frequency bands has the potential to guarantee extremely high throughput, and that LTE transmissions enable stable communications. We conclude that the parallel use of different RTs makes it possible to complement the limitations of each type of network and therefore represents a viable approach to guarantee both highcapacity and resilient V2N communications.

The rest of the paper is organized as follows. In Sec. II we review some of the most relevant contributions related to V2N communication, while in Sec. III we overview the strengths and weaknesses of the LTE and the mmWave paradigms as enabling technologies for vehicular communications. The system and mobility models and the simulation parameters are described in Sec. IV, while in Sec. V we present our main findings and simulation results. Finally, conclusions and suggestions for future work are provided in Sec. VI.

II. RELATED WORK

The application of mmWave technology in the automotive context is not new and some preliminary studies have already demonstrated the feasibility of designing mmWave-based protocols to support vehicular communications [START_REF] Kato | ITS wireless transmission technology. Technologies of millimeter-wave inter-vehicle communications: Propagation characteristics[END_REF], [START_REF] Yamamoto | Pathloss prediction models for intervehicle communication at 60 GHz[END_REF]. The research has also focused on the design of automotive radars operating in the 77 GHz band to enable driver assistance functionalities [START_REF] Hasch | Millimeter-Wave Technology for Automotive Radar Sensors in the 77 GHz Frequency Band[END_REF]. More recently, the authors in [START_REF] Shah | 5G for Vehicular Communications[END_REF], [START_REF] Lu | Connected Vehicles: Solutions and Challenges[END_REF] have explored the benefits of emerging technologies (e.g., 5G communications, network slicing, mobile edge computing) to support advanced automotive applications.

Despite this growing interest, the severe radio conditions resulting from the mobility of vehicles and their relative speed with respect to the road infrastructures, and the need to maintain beam alignment between the communication endpoints, pose significant challenges to the design of V2N systems operating at mmWaves [START_REF] Giordani | Millimeter wave communication in vehicular networks: Challenges and opportunities[END_REF]. A stochastic analysis of the connectivity probability in mmWave networks is presented in [START_REF] Giordani | Coverage and Connectivity Analysis of Millimeter Wave Vehicular Networks[END_REF], where the authors derive a probabilistic connectivity model that makes it possible to study the communication performance when varying the nodes speed, the beam alignment periodicity, the infrastructure density and the antenna geometry, under some simplifying assumptions. The problem of beam alignment is addressed in some other recent studies, e.g., [START_REF] Va | Beam design for beam switching based millimeter wave vehicle-to-infrastructure communications[END_REF], which proposes sophisticated strategies to reduce the beam alignment overhead under dynamic networks and unpredictable wireless channel evolution, and [START_REF] Garcia | Location-aided mm-wave channel estimation for vehicular communication[END_REF], where a location-aided hierarchical beamforming approach is proposed to achieve ultra-fast alignment and connection.

However, given the variety of the automotive services and the heterogeneity of their requirements, it is unlikely that V2N communications can be supported by a single radio access interface, rather the orchestration of multiple RTs, which complement each other's capabilities, might be the solution. Although the effectiveness of such mixed solutions has already been demonstrated (e.g., [START_REF] Sepulcre | Context-aware heterogeneous V2I communications[END_REF], [START_REF] Dressler | Inter-vehicle communication: Quo vadis[END_REF]), at the time of writing there is no prior work that quantitatively investigates the strengths and weaknesses of the enabling technologies and, in particular, of mmWave and LTE, which are currently considered for future V2N communications. This paper contributes to partially fill this gap.

III. RADIO TECHNOLOGIES TO SUPPORT V2N COMMUNICATIONS

The 3GPP has provided a basic set of V2N connection requirements to support classic services, mainly intended to avoid or mitigate vehicle accidents, but also to enable a wide range of other safety, environmental and societal benefits [START_REF] Gpp | Technical Specification Group Services and System Aspects; Study on enhancement of 3GPP Support for 5G V2X Services (Release 15)[END_REF]. These safety-related services have very stringent demands in terms of communication reliability and stability, while relatively low data rates are usually expected.

However, V2N communications will also enable more advanced services, e.g., by sharing sensory data to build collective context awareness and permitting the vehicles to enhance their perception beyond what provided by the onboard instrumentation. The digital horizon built by vehicles can be exploited to coordinate their maneuvers and support safe and automated driving applications, which are collectively referred to as cooperative driving/maneuvering.

Finally, V2N transmissions enable infotainment applications, which generically refer to a set of services that deliver a combination of information and entertainment. For media download applications (e.g., Internet browsing) the required throughput will depend on the content type, while latency is reasonably tolerated because the download can be generally completed within some flexible time frame. Video streaming, on the other hand, demands stable throughput (especially for high-quality video) with stringent latency. Other real-time services (e.g., on-line gaming) generate data streams with relatively low bitrate but require the dynamic maintenance of multicast communication, reliable connections and minimum end-to-end delays.

Based on the above considerations, in this section we overview the characteristics of candidate RTs currently being considered to support V2N communications, i.e., the LTE standard and the mmWave technology, and discuss their possible shortcomings in relation with the aforementioned application requirements. Table I provides a short summary of this discussion.

A. LTE Communications

The LTE standard [START_REF] Gpp | Requirements for Evolved UTRA and Evolved UTRAN (Release 7)[END_REF] operates in the sub-6 GHz spectrum. It can provide a round-trip-time within a cell theoretically lower than 10 ms. Moreover, the LTE infrastructure is widely deployed, the transmissions are omnidirectional and LTE has the potential to support multicast and broadcast data distribution [START_REF] Araniti | LTE for vehicular networking: a survey[END_REF]. Finally, LTE transmissions are scheduled, so that collisions are avoided and mutual interference is minimized.

However, there are several challenges that need to be addressed before LTE can be massively exploited in vehicular environments. First, access and transmission latencies increase with the number of vehicles in the cell, thus raising scalability issues. Second, despite the almost ubiquitous coverage of the LTE infrastructures, still the connection may not be always available (e.g., in tunnels or in rural areas). Third, LTE offers limited downlink capacity (i.e., around 300 Mbps in Release 8, though much lower rates are typical in high mobility scenarios), which may not be sufficient to support some categories of applications (e.g., advanced driving or infotainment). Finally, the V2N communications should share the LTE cell capacity with classic human-initiated services, which may result in a strong performance degradation in either (or both) traffic categories.

In conclusion, the LTE standard, despite its clear strengths, may not be able to support all future V2N requirements.

B. Millimeter Wave Communications

The mmWave spectrum between 10 GHz and 300 GHz will be a cornerstone of next-generation wireless networks (5G) [START_REF] Rangan | Millimeter-wave cellular wireless networks: Potentials and challenges[END_REF] and represents a promising candidate to support the demands of future automotive applications. As mentioned, in fact, the large bandwidth available at mmWave frequencies permits a much higher bitrate than traditional systems. Moreover, the small wavelengths make it practical to design very large antenna arrays to obtain high beamforming gains and spatial isolation that, in turn, decrease the interference among different vehicles and have the potential to reduce the risk of eavesdropping [START_REF] Rappaport | Millimeter wave wireless communications[END_REF] 1 .

On the other hand, there are still a number of challenges that need to be addressed before enabling mmWave V2N solutions. To begin with, mmWave signals do not penetrate most solid materials, so that shadowing and blockage phenomena may be quite frequent. Moreover, the path loss at mmWaves is very large and, hence, long-range omnidirectional radio transmissions are not possible, which makes data broadcasting more complex. Such a severe path loss is counteracted by adopting directional communication that, however, requires precise beam alignment between transmitter and receiver, whose maintenance may imply control overhead and latency. These challenges are further exacerbated in highly dynamic vehicular scenarios, as the beam alignment may be lost before a data exchange is completed [START_REF] Giordani | Coverage and Connectivity Analysis of Millimeter Wave Vehicular Networks[END_REF]. Although the alignment overhead can be reduced by the dissemination of context information, e.g., geographical position of vehicles and Road Side Units (RSUs), or by implementing digital beamforming architectures, the direct applicability of the mmWave technology to a vehicular context is still not clear and has become a research focus in the area of intelligent autonomous systems.

IV. SYSTEM MODEL AND SIMULATION PARAMETERS

In this section we present the system model we considered to evaluate the performance of the V2N system. The channel and mobility models are described in Secs. IV-A and IV-B, respectively, while the simulation parameters are illustrated in Sec. IV-C.

A. Channel Model

In V2N systems, we reasonably expect that other vehicles, pedestrians or urban buildings can block the link connecting the target vehicle and its serving RSU. It is therefore necessary to distinguish between Line of Sight (LOS) and Non Line of Sight (NLOS) nodes. LTE Model. For the LTE cells, we consider the 3GPP specifications [START_REF] Gpp | Study on Small Cell enhancements for E-UTRA and E-UTRAN; Higher layer aspects (Release 12)[END_REF] for an outdoor dense scenario. Accordingly, a vehicle at distance d from its LTE serving infrastructure will be in LOS with probability (2)

P LOS (d)=min 0.018 d , 1 1-exp -d 0.063 +exp -d 0.063 , (1
(3)

In addition, we consider a fast Rayleigh fading, which is modeled as a stochastic gain with unit power (in linear scale).

Millimeter Wave Model. As already mentioned, the short wavelengths of mmWave signals result in significant shadowing, reflection and diffusion [START_REF] Geng | Millimeter-Wave Propagation Channel Characterization for Short-Range Wireless Communications[END_REF], especially where the coverage frequently implies NLOS links. We therefore adopt the channel model described in [START_REF] Akdeniz | Millimeter Wave Channel Modeling and Cellular Capacity Evaluation[END_REF] that provides a realistic assessment of the mmWave network in a dense urban deployment. According to this model, the instantaneous probability to be in LOS and NLOS at distance d from the transmitter is equal to P LOS (d) = e -aLOSd and P NLOS (d) = 1 -P LOS (d), respectively, where a LOS = 0.0149 m -1 . The path loss is computed as:

P L LOS (d) = 61.4 + 2 log 10 (d) + ξ σ L ; P L NLOS (d) = 72 + 2.92 log 10 (d) + ξ σ N ; (4) (5)
where ξ σ L ∼ N (0, σ 2 L ) and ξ σ N ∼ N (0, σ 2 N ) characterize the shadowing (in dB) in LOS and NLOS conditions, respectively, with σ L = 5.8 dB and σ N = 8.7 dB at 28 GHz [START_REF] Akdeniz | Millimeter Wave Channel Modeling and Cellular Capacity Evaluation[END_REF].

B. Mobility Model

For our simulations, we use a real road map data imported from Openstreetmaps (OSM) [27], an open-source tool which combines wiki-like user generated data with free access information, allowing users to create editable maps of the world. We consider the OSM map of New York City and, more specifically, of the Madison Square Park district between W 16th and W 27th streets.

Moreover, in order to consider realistic mobility models and representative speed traces, we simulated the mobility of cars using SUMO [START_REF] Krajzewicz | Recent development and applications of SUMO -Simulation of Urban MObility[END_REF], a powerful, open-source traffic generator that supports the modeling of intermodal traffic systems including road vehicles and structures, public transports and pedestrians. The vehicles move through the street network (imported from OSM) according to a randomTrip mobility model, which generates trips with random origins and destinations, and speed values which depend on the interaction of the moving vehicle with the road and network elements (e.g., traffic lights or road intersections), as displayed in Fig. 1.

C. Simulation Parameters

The simulation parameters are based on realistic system design considerations and are summarized in Table II. The mmWave and LTE RSUs are deployed over an area of 1 km2 (which is deemed sufficient to avoid undesired boundary effects) according to two independent Poisson Point Processes (PPPs) with densities λ mmW and λ LTE , respectively. In particular, according to the 3GPP specifications [START_REF] Gpp | Study on Small Cell enhancements for E-UTRA and E-UTRAN; Higher layer aspects (Release 12)[END_REF], the LTE macro inter-site distance is 500 m, i.e., λ LTE = 4 RSU/km 2 , while λ mmW varies from 10 to 100 RSU/km 2 (the trade-off oscillates between better coverage and increased deployment cost). We assume the LTE systems operate in the 2 GHz legacy band, with 20 MHz of bandwidth and omnidirectional transmission. For the mmWave links, instead, we assume to operate at 28 GHz, with 1 GHz bandwidth and (in general) directional transmission. To this end, vehicles and RSUs are equipped with Uniform Planar Arrays (UPAs) of N and M elements, respectively, allowing to steer beams consisting of a main lobe of predefined width (which depends on N and M ) and a side lobe that covers the remainder of the antenna radiation pattern. 2 For the sake of completeness, we also consider omnidirectional mmWave transmissions at the vehicle side, which corresponds to N = 1. Notice that, in general, M ≥ N , since RSUs are usually less space-constrained than cars.

For the beam alignment, we consider the procedure described in [START_REF] Giordani | Coverage and Connectivity Analysis of Millimeter Wave Vehicular Networks[END_REF], according to which the nodes exchange channel quality reports at the beginning of every slot of duration T tr , so that vehicles and RSUs can regularly identify the optimal directions for their respective beams. In order to minimize the tracking overhead (which might be significant when considering very low values of T tr ), in case the alignment is lost within a slot, connectivity can be recovered only at the beginning of the subsequent slot, i.e., when a new beam alignment operation will be completed. In order to get insights on some limit performance, we will also consider the perfect beam alignment case where the beam alignment procedure is performed continuously (T tr = 0) and the beams are hence always perfectly aligned (e.g., thanks to the use of digital beamforming or other external means).

The statistical results are derived through a Monte Carlo approach, where multiple independent simulations are repeated to get different statistical quantities of interest. More specifically, we analyze the received signal strength between a single target transmitting vehicle and its serving infrastructure for different values of λ mmW . The signal strength is also related to the achievable data rate, which is calculated according to the Shannon formula. Given a bandwidth W (either W mmW or W LTE , according to the technology being considered), and the SNR Γ i,j between the transmitter i and its candidate receiver j, the average (Shannon) data rate is therefore computed as

R Sh = W log 2 (1 + Γ i,j ). (6)
V. COMPARATIVE RESULTS

In the following subsections we provide some numerical results to compare the connectivity performance of LTE and mmWave-based V2N transmissions, which will be evaluated in terms of achievable data rate, stability and outage probability.

In Fig. 2 we compare the two technologies in terms of average data rate experienced by the target vehicle assuming perfect beam alignment for the mmWave links and different antenna configurations (including omnidirectional transmissions at the vehicular node, N = 1). The data rate is computed according to Eq. ( 6). We observe that the very large bandwidth available to the mmWave systems (50 times larger than in LTE) provides much higher throughput, which may be required by some V2N applications (e.g., high-resolution video streaming). Moreover, even with an omnidirectional mmWave antenna at the vehicular node, the connection can still provide acceptable average bitrate, provided that the mmWave RSUs are sufficiently dense (to increase the LOS probability). Finally, we note that by further increasing λ mmW , the mutual interference among the RSUs will eventually impact the average bitrate, which will start decreasing. In Fig. 3 we plot the evolution of the transmit rate experienced by a moving vehicle in a specific SUMO simulation of duration T sim = 250 s, for two antenna configurations. It is apparent that the mmWave throughput depends on the array factors, i.e., on the beamforming gain. From Fig 3, it is also interesting to observe that the rate traces follow accurately the movements of the car along the streets. For example, the almost constant rate intervals (e.g., from 60 s to 90 s, from 100 s to 130 s, and from 140 s to 190 s) correspond to stopping periods at the red traffic lights, while the peaks correspond to the crossing of the coverage range of the mmWave RSUs, whose range is geometrically determined by their beamwidths. Finally, we observe that the mmWave rate presents clear fluctuations and variations, which are mainly due to transitions from LOS to NLOS, and vice versa, and to the small scale fading.

In Fig. 4 we can observe the effect on the transmission rate of different mmWave RSU deployment strategies. As expected, the rate decreases in sparser networks (i.e., with λ mmW = 20 RSU/km 2 ) since vehicles generally need to connect with farther RSUs. In these conditions, LTE can provide better performance (despite the low density of LTE RSUs, equal to λ LTE = 4 RSU/km 2 ). endpoints caused by user mobility. The vehicle may be suddenly in outage with respect to all the surrounding RSUs and, in some scenarios, this may prevent the timely dissemination of data. In this paper, we have compared the performance of LTE and the mmWave technologies in terms of achievable

A. Stability Performance

Following the analysis we proposed in [START_REF] Polese | Improved Handover through Dual Connectivity in 5G mmWave mobile networks[END_REF], we measure the stability performance of a link in terms of the coefficient of variation of the transmission rate, which is defined as

ρ var = STD(R) E[R] , (7)
where STD(R) and E[R] are the standard deviation and mean, respectively, of the throughput R experienced by a certain RT. High values of ρ var indicate significant channel instability, meaning that the rate would be affected by local variations and periodic degradations. Let ρ mmW var and ρ LTE var denote the stability indices [START_REF] Rappaport | Millimeter wave wireless communications[END_REF] for mmWave and LTE links, respectively. From Fig. 5 we observe that ρ mmW var > ρ LTE var for all values of λ mmW , which reflects the much higher variability of the performance guaranteed by mmWave links as a result of the scattering from nearby buildings, vehicles and terrain surfaces. These variations may actually hinder the adoption of mmWave links to support some types of V2N applications, especially those requiring long-term stable throughput (e.g., real-time services). However, the stability of the mmWave links increases in dense environments, as the probability of path loss outage decreases.

In case of directional mmWave transmissions, the misalignment between the transmitter and the receiver can also 6 significantly impact the link stability, as exemplified in Fig. 6 which shows the rate achieved by the mmWave link when changing the beam tracking period. As T tr increases, the rate presents significant variations which, in some cases, may lead to network disconnections. Conversely, the omnidirectional transmissions of LTE systems offer more stable connectivity and, under some circumstances, even higher throughput than mmWave strategies. The extent to which beam misalignment impacts the communication performance depends on several factors, including the beamwidth, as illustrated in Fig. 7. Wider beams provide more durable connections (even with very sporadic beam tracking), as they enlarge the area in which the vehicles can benefit from the coverage of their serving cells before disconnecting. In this context, however, the well-known stability versus data rate trade-off arises: the narrower the beams, the higher but less stable the transmission rates.

B. Outage probability

In Fig. 8 we evaluate the outage probability of the investigated V2N schemes, i.e., the probability that the received signal strength is below a predefined threshold. We first endpoints caused by user mobility. The vehicle may be suddenly in outage with respect to all the surrounding RSUs and, in some scenarios, this may prevent the timely dissemination of data. In this paper, we have compared the performance of LTE and the mmWave technologies in terms of achievable data rate, communication stability and outage probability. Our results showed that LTE systems provide limited data rates (i.e., up to a few hundreds of Mbps) but guarantee very robust and reliable transmissions thanks to the intrinsic stability of the stable connectivity and, under some circumstances, higher throughput than mmWave strategies.

The extent at which beam misalignment impacts the communication performance depends on several factors, including the beams width, as illustrated in Fig. 7. Wider beams provide more durable connections (even with very sporadic bream tracking), as they enlarge the area in which the vehicles can benefit from the coverage of their serving cells before disconnecting. In this context, however, the well-known stability versus data rate trade-off arises: the narrower the beams, the higher but less stable the transmission rates.

B. Outage probability

In Fig. 8 we evaluate the outage probability of the investigated V2I schemes, i.e., the probability that the received signal strength is below a predefined threshold. We first observe that the outage probability for mmWave transmissions decreases monotonically with the RSU density mmW (because of the closer distance between transmitter and receiver), and with the number of antenna elements at the nodes (because of the higher beamforming gain). Moreover, we notice that the outage probability of mmWave links degrades significantly when the vehicle has only one omnidirectional antenna (N = 1), while LTE provides more reliable communications, with lower outage probability.

VI. CONCLUSION AND FUTURE WORK

A limitation for the feasibility of mmWave-enabled V2I communication is the rapid dynamic of the mmWave channel and the high risk of misalignment between the communication endpoints caused by user mobility. The vehicle may be suddenly in outage with respect to all the surrounding RSUs and, in some scenarios, this may prevent the timely dissemination of data. In this paper, we have compared the performance of LTE and the mmWave technologies in terms of achievable data rate, communication stability and outage probability. Our results showed that LTE systems provide limited data rates (i.e., up to a few hundreds of Mbps) but guarantee very robust and reliable transmissions thanks to the intrinsic stability of the observe that the outage probability for mmWave transmissions decreases monotonically with the RSU density λ mmW (because of the closer distance between transmitter and receiver), and with the number of antenna elements at the nodes (because of the higher beamforming gain). Moreover, we notice that the outage probability of mmWave links degrades significantly when the vehicle has only one omnidirectional antenna (N = 1), while LTE provides more reliable communications, with lower outage probability.

VI. CONCLUSION AND FUTURE WORK

A limitation for the feasibility of mmWave-enabled V2N communication is the rapid dynamics of the mmWave channel and the high risk of misalignment between the communication endpoints caused by user mobility when using narrow beams. The vehicle may be suddenly in outage with respect to all the surrounding RSUs and, in some scenarios, this may prevent the timely dissemination of data. In this paper, we have compared the performance of LTE and mmWave technologies in terms of achievable data rate, communication stability and outage probability. Our results showed that LTE systems provide limited data rates (i.e., up to a few hundreds of Mbps) but guarantee stable and reliable transmissions thanks to the intrinsic stability of the low-frequency channels and the omnidirectional transmissions. Conversely, mmWave networks offer high-capacity but highly variable connectivity, though their performance can be improved by considering very directional transmissions and frequent re-alignment operations, and by increasing the infrastructure density.

From our analysis we conclude that a practical way to complement the limitations of each type of network is via heterogeneous networking, i.e., by combining multiple radio access technologies into a single solution that is more robust and efficient than any individual approach.

The performance of V2N communications can be improved by designing advanced multi-RT schemes able to instantaneously identify the best access solution to interconnect the vehicles and the infrastructure(s), or by envisioning synergistic combinations of the two approaches, e.g., exploiting LTE to help beam alignment of mmWave links, or using the two technologies in parallel to transmit layered content. These research challenges are left for future work.

  ISBN 978-3-903176-05-8 c 2018 IFIP

  ) and in NLOS with complementary probability P NLOS (d) = 1 -P LOS (d). The path loss for the two conditions, in dB, is given by P L LOS (d) = 103.4 + 24.2 log 10 (d) ; P L NLOS (d) = 131.1 + 42.8 log 10 (d) .

Fig. 1 :

 1 Fig.1: The car's instantaneous speed trace (km/h) overlaid to the considered NYC road map. The car stops four times between 24th, 23rd, 22nd streets and 7th avenue and between 20th street and 6th avenue due to red traffic lights.

λFig. 2 :

 2 Fig.2: Average achievable data rate when varying λ mmW for mmWave communications with perfect tracking and different antenna array configurations (empty markers). A fixed LTE configuration (full diamond markers) is considered.

Fig. 3 :Fig. 4 :Fig. 5 :

 345 Fig. 3: Example of time-varying rate for LTE (diamond markers) and mmWave communicatio perfect beam alignment, and mmW = 100 RSU/km 2 .

Fig. 6 :FigFig. 4 :

 64 Fig. 6: Exam of tracking RSU/km 2 , N

Fig. 3 :Fig. 3 :λFig. 5 :Fig. 6 :

 3356 Fig.3: Example of time-varying rate for LTE (diamond markers) and mmWave communications (solid plain lines), with different antenna array configurations, perfect beam alignment, and mmW = 100 RSU/km 2 .

Fig. 4 :Fig. 5 :

 45 Fig. 4: Example of LTE and mmWave rate over time, for different values of RSU densities ( mmW = 20 and 70 RSU/km 2 ), perfect beam alignment, and N = 4 and M = 4.

Fig. 6 :

 6 Fig. 6: Example of time-varying LTE and mmWave rates for different values of tracking period (Ttr = 0.1 s and Ttr = 1 s), and with mmW = 50 RSU/km 2 , N = 16 and M = 64.

Fig. 7 :

 7 Fig. 7: Connectivity stability for different antenna array configurations considering mmWave propagation, with tracking period Ttr = 1 s and mmW = 60 RSU/km 2 .

Fig. 4 :Fig. 5 :

 45 Fig. 4: Example of LTE and mmWave rate over time, for different values of RSU densities ( mmW = 20 and 70 RSU/km 2 ), perfect beam alignment, and N = 4 and M = 4.

Fig. 6 :N = 4 , M = 4 N = 16 , M = 64 Fig. 7 :

 64416647 Fig. 6: Example of time-varying LTE and mmWave rates for different values of tracking period (Ttr = 0.1 s and Ttr = 1 s), and with mmW = 40 RSU/km 2 , N = 16 and M = 64.

Fig. 7 :λFig. 8 :

 78 Fig. 7: Connection stability for different antenna array configurations considering mmWave propagation, with tracking period Ttr = 1 s and λ mmW = 100 2 .

TABLE I :

 I Description of the characteristics of radio interfaces currently being considered for V2N communications.

	RT	Pros	Cons
	LTE [4], [9] mmWaves [7]	• Low latency (RTT within a cell lower than 10 ms) • Wide deployment • Omnidirectional/broadcast transmissions • Scheduled transmissions • Very large bandwidth (>50 times more than LTE) • Beamforming gain • Spatial isolation • Inherent security and privacy	• Scalability issues • Non-ubiquitous coverage • Limited transmission data rate • Very large path loss • Signals do not penetrate through solid material • Need to set up aligned transmissions • Significant shadowing, reflection and scattering

TABLE II :

 II Main simulation parameters.

	Parameter	Value	Description
	PTX,mmW 30 dBm WmmW 1 GHz fc,mmW 28 GHz λmmW {10, .., 100} RSU/km 2 mmWave RSU density mmWave TX power mmWave bandwidth mmWave carrier frequency Ttr {0.1, 1} s Tracking periodicity N {1, 4, 16} Vehicle array size M {4, 16, 64} mmWave RSU array size
	PTX,LTE WLTE fc,DSRC λLTE	46 dBm 20 MHz 2 GHz 4 RSU/km 2	LTE TX power LTE bandwidth LTE carrier frequency LTE RSU density
		LTE channel parameters ∼ [24] mmWave channel parameters ∼ [26]

Although security in automotive systems will mainly rely on bit-level cryptographic techniques, mmWave transmissions leveraging physical layer security look promising to mitigate computational and communication overhead for application-level encryption[START_REF] Wang | Physical layer security in millimeter wave cellular networks[END_REF].

There exists a strong correlation among number of antenna elements, beam width, and beamforming gain: the more antenna elements in the system, the narrower the beams, the more directional the transmission, and the higher the gain that can be achieved by beamforming.


Ali Nikoukar

Mansour Abboud

Borna Samadi
email: bornasamadi@ut.ac.ir

Mesut Güneş
email: mesut.guenes@ovgu.de

Behnam Dezfouli
email: bdezfouli@scu.edu

Empirical Analysis and Modeling of Bluetooth Low-Energy (BLE) Advertisement Channels

Keywords: Bluetooth Low Energy (BLE), Log-Normal Shadowing Model (LNSM), multi-path fading, Bluetooth beacon, channel modeling, simulation

Bluetooth Low Energy (BLE) is a widely-used lowpower wireless standard in the Internet of Things (IoT) domain. This standard provides a set of advertisement channels, which are primarily used for device discovery, connection initiation, and information broadcast. Beacon transmission over these advertisement channels is the enabler of applications such as indoor positioning, product advertisement, and medical monitoring. Meanwhile, the performance and accuracy of these applications highly depend on the characteristics of communication over advertisement channels. Unfortunately, the existing literature does not offer an extensive characterization of these channels under various operational conditions. In this paper, we address this research gap through conducting extensive experiments in four different environments. We study the effect of environment and interference on noise floor and signal propagation, and we present a model for noise floor and extract the parameters of lognormal path loss model. The proposed models, in particular, can be directly used in simulation tools for modeling BLE wireless channels as well as applications such as indoor positioning.

I. INTRODUCTION

As the Internet of Things (IoT) strives to connect every object to the Internet [START_REF] Al-Fuqaha | Internet of things: A survey on enabling technologies, protocols, and applications[END_REF], a variety of wireless technologies are involved to obtain this goal. To fulfill the requirements of IoT, an applicable technology must be low-power, available, inexpensive, reliable, and provide mechanisms to support coexistence. To this end, Bluetooth Low Energy (BLE) is a widely-adopted standard that satisfies the aforementioned requirements.

Introducing iBeacon by Apple in 2013 [START_REF] Newman | Apple ibeacon technology briefing[END_REF] expanded the application domain of BLE through adding advertisement and localization capability. Nowadays, Indoor Positioning System (IPS) is one of the major applications of BLE. It is reported that 75% of top US retailers have already deployed beacons in their facilities, and 84% of international airports will be using BLE beacons by 2019 [START_REF]The Rise of Beacon Technology[END_REF]. Commercial products like StickNFind [START_REF]StickNFind[END_REF], TrackR [START_REF]TrackR[END_REF] and Estimote [START_REF]Estimote[END_REF] exploit BLE beacons. These products offer various types of services such as patient monitoring, navigation in shopping malls, broadcasting information in train stations or museums, and finding lost items. In 2016, the global market for indoor positioning service was $5.22 billion and it is expected to grow up to $40.99 billion by 2022, with an annual growth rate of 42.0% [START_REF]Indoor Location Market by Component Deployment Mode, Application, Vertical, and Region -Global Forecast to 2022[END_REF].

Although various wireless channel models exist for Bluetooth classic [START_REF] Zurbes | Radio network performance of bluetooth[END_REF], 802.11 [START_REF] Liu | Wifi-based indoor positioning for multifloor environment[END_REF]- [START_REF] Akl | Indoor propagation modeling at 2.4 ghz for ieee 802[END_REF] and 802.15.4 [START_REF] Dezfouli | Modeling low-power wireless communications[END_REF], [START_REF] Dezfouli | Cama: Efficient modeling of the capture effect for low-power wireless networks[END_REF] standards, the wireless channel of BLE has not been investigated thoroughly as it is a relatively new standard. In particular, the models proposed for Bluetooth classic are not applicable to BLE because of the differences in their physical layer implementation. For example, the channel bandwidth and modulation used by BLE and Bluetooth classic are different. As BLE is particularly suitable for IoT applications, its wireless channel characterization is important due to two reasons: First, applications such as IPS perform range estimation based on the signal strength received from beacon sources. Second, the accuracy of the wireless channel model used by simulation tools directly affects the performance of protocols developed.

In this paper, we focus on the characterization of BLE Advertisement Channels (ADV_CHs) and propose models for various environments. Specifically, the contribution of this paper is three-fold: First, this research is the first experimental evaluation of BLE's ADV_CHs in various environments considering a variety of influential factors, including interference, environmental size, and the existence of obstacles and reflective objects. Second, we study the effect of 802.11 interference on the stability of noise floor perceived by BLE devices on the three advertisement channels. Third, the obtained results are used to extract the parameters of Log-Normal Shadowing Model (LNSM) under various conditions. We also highlight the shortcomings of LNSM and identify areas of future research. The proposed models enable the research community to integrate accurate channel models of BLE's ADV_CHs in various applications (such as IPS) and simulation tools to achieve realistic performance evaluations.

The remainder of this paper is organized as follows. A brief overview of BLE's physical layer is presented in Section II. In Section III, the methodology of research including LNSM, noise floor definition, experimental setup, and experimental parameters, are explained. Section IV presents and analyzes the obtained results. Section V elaborates the background of the research on BLE ADV_CHs. Finally, we conclude the paper in Section VI.

II. WIRELESS COMMUNICATION USING BLE

High power consumption is one of the most well-known challenges of wireless technologies for IoT. Although Bluetooth was originally used for file sharing and audio streaming, in 2010, Bluetooth Special Interest Group (SIG) made a remarkable change in its protocol stack and introduced BLE. The new standard paved the way towards new IoT applications such as IPS.

Among other changes, BLE employs a new physical layer design, which results in lower energy consumption. In contrast with Bluetooth classic which uses 79 channels each 1MHz wide, BLE uses 40 channels each 2MHz wide. BLE employs Gaussian Frequency Shift Keying (GFSK) modulation, and the transmit power is within -20 to +20dBm. BLE classifies the channels into two groups: advertisement channels (ADV_CHs) and data channels, as Figure 1 shows. ADV_CHs are responsible to broadcast advertisement Protocol Data Units (PDUs), and data channels are only used for data exchange. Based on the broadcasted PDU over ADV_CHs, the device either establishes a connection and switches to the data channels, or continues a one-way information sharing. The unique channel assignment of ADV_CHs tries to minimize interference with 802.11. As Figure 1 shows, the placement of these channels minimizes interference with channel 1, 6, and 11 of 802.11b/g/n standard, which are the widely-used and nonoverlapping channels in the allocated frequency spectrum of this standard [START_REF] Chieochan | Channel assignment schemes for infrastructure-based 802.11 wlans: A survey[END_REF].

RF signals sent through ADV_CHs experience path loss and multipath effect, which result in signal attenuation and variations, respectively. Path loss and multipath are caused by factors such as obstacles, noise floor, and signal reflection off the walls and objects. Both path loss and multipath affect the performance of applications such as IPS. For example, IPS relies on the received signal strength value of the signals received to estimate the distance from the sources of beacon broadcast. If the estimation is not accurate, the triangulation algorithm fails to compute an accurate location. Accordingly, it is essential to characterize and model ADV_CHs to facilitate the design and development of reliable beacon-based applications.

III. METHODOLOGY: MODELING AND EXPERIMENTATION

In this Section, we explain the path loss model used as well as the experimentation methodology employed.

A. Log-Normal Shadowing Model (LNSM)

As mentioned earlier, signal path loss is an essential characteristic of wireless channels. To this end, various models have been proposed to compute the strength of the signal received from a transmitter sending d meters away. This value is usually referred to as received signal strength (RSS). The three well-known models of signal decay are [START_REF] Rappaport | Wireless communications: principles and practice[END_REF]: (i) freespace propagation model, (ii) two-ray model, and (iii) lognormal shadowing model (LNSM). The free-space propagation model simply represents received signal power in an obstaclefree environment. In the two-ray model, in addition to the lineof-sight (LoS) signal, the ground reflected signal is included as well. Both of these models represent signal strength as a fixed function of distance. This representation results in a circular communication range, which is referred to as the unit disk graph model. However, as existing studies indicate [START_REF] Dezfouli | Modeling low-power wireless communications[END_REF], RSS around a sender shows variations that are best modeled through a normal distribution. These variations are caused by the signals reflected off the walls and objects in the environment. LNSM represents path loss and signal variations as follows:

P L(d) = P L(d 0 ) + 10η log 10 d d 0 + N (0, σ ch ) (1)
where P L(d) represents path loss at distance d, P L(d 0 ) is the path loss at the reference distance d 0 , η is the path loss exponent that indicates the rate at which path loss increases versus distance d, and N (0, σ ch ) is a zero-mean Gaussian random variable with standard deviation σ ch . In other words, σ ch represents the standard deviation of signal power fluctuations caused by multi-path.

The parameters of LNSM depend on both environmental factors (e.g., obstacles, reflective surfaces, and humidity) and characteristics of RF transceiver (e.g., frequency band, transmit power, and modulation scheme). Consequently, empirical measurements are necessary to extract these parameters. However, as environmental factors do not stay unchanged over time, we need to estimate the variations of signal power over time. To this end, we applied the trust region reflective least squares [START_REF] Conn | Trust region methods[END_REF] curve fitting algorithm.

B. Noise Floor

When external factors are neglected, the noise floor of a transceiver is modeled through computing its noise figure, which is a function of temperature and noise bandwidth. On the other hand, interference shows temporal variations and affects the RSS perceived from a receiver's point of view. In fact, the signal to interference-and-noise ratio (SINR) perceived by a receiver is modeled as SIN R = S/(N + I), where S is the signal power, N is noise figure, and I is interference. In order to estimate RSS accurately, we need to measure both noise figure and interference in various environments. To this end, we used a receiver to collect RSS samples when no other BLE node is transmitting. RSS samples in an interference-free environment represent noise figure, and RSS samples in an environment with 802.11 devices represent noise figure plus interference level. In this paper, we refer to the term N + I as noise floor. After computing the noise floor of various environments, we estimate the actual signal power received from a sender through subtracting noise floor from the signal power received. Therefore, RSS dB = P r -N -I = P t -P L(d) -N -I, where P t and P r are transmission and reception power, respectively.

C. Environments

We have conducted extensive experiments in four different environments. Figure 2 shows these environments. As it can be observed, there are two outdoor environments and two indoor environments, as follows:

-Environment (a). I summarizes the characteristics of these environments based on our measurements. In this table "Number of Locations" refers to the number of experimentation points used in that environment.

D. Hardware Setup

We used nRF52840 SoC [START_REF]nRF52840 Objective Prodcut spesification v0[END_REF] as the BLE sender and receiver. This platform supports low-power wireless technologies such as BLE 5.0, ANT, IEEE 802.15.4, and 2.4GHz proprietary protocols. The SoC includes an ARM Cortex-M4 processor with 1MB flash memory and 256KB RAM. The BLE transceiver of this platform presents receiver sensitivity -95dBm and supports transmit powers in range -20 to +8dBm. Note that sensitivity does not represent the minimum signal level detectable; instead, it indicates that if the received power of an input signal is -95dBm, bit error rate 10 -3 is achieved during communication.

Our testbed setup includes three boards: sender, receiver, and sniffer. Each node is placed on a tripod with height 1 meter. The broadcaster and receiver exchange 1000 packets for each distance-channel configuration. Before exchanging packets between the broadcaster and receiver, the sniffer is used to measure the noise floor. To this end, before each experiment, in each environment a total of 40,000 samples were collected. The inter-sampling interval is 9µs. After measuring the noise floor, the sniffer sends a trigger message to the sender to start packet broadcast (Figure 3).

IV. EMPIRICAL RESULTS

In this Section, we present and discuss the empirical data collected for noise floor and RSS modeling.

A. Noise Floor

Although the parameters of LNSM are essential for RSS modeling, the effect of noise floor should be accounted as well because it directly contributes to a node's perception of reception power. Noise floor measurement results are summarized in Table II. In this table, µ refers to the mean noise floor measured, and σ represents the standard deviation of noise floor variations.

As it can be observed in Table II, the average noise floor is almost similar for all the environments, however, channel 37 and 38 show significantly higher variations in environment (c) and (d), which is due to 802.11 interference. Figure 4 demonstrates noise floor samples collected on channel 37, 38 and 39 in environment (d). This figure represents channel 39 as the most reliable and clear channel, and in contrast, both channel 37 and 38 show higher noise floor. Although channel 37 and 38 do not overlap with the central frequency of 802.11 channels, their frequency is very close to 802.11's channel 1 and 6, respectively. We clarify this issue through Figure 5. As the figure shows, for example, BLE's channel 37 overlaps with the side lobe of 802.11's channel 1. Although the power of the side mask is 20dBr (dB relative) less than the center frequency, it may cause significant interference on BLE's channel 37 depending on the transmission power and distance from the 802.11 interferers. The same problem happens for channel 38 when 802.11's channel 6 is in use. As Figure5(a) and (b) indicate, both channel 37 and 38 show an almost similar level of overlapping with 802.11 channels. However, according to Figure 4, channel 38's noise floor represents higher variations compared with channel 37. The reason is that, compared with channel 37 which overlaps with one 802.11 channel only, channel 38 overlaps with multiple 802.11 channels. For example, if 802.11 channel 1 and 6 are in use, then BLE's channel 38 overlaps with both, as Figure 5 (a) and (b) show. On the other hand, channel 39 overlaps with only one of the main channels of 802.11. In addition, the distance between the central frequency of BLE's channel 39 and 802.11's channel 11 is more than that of the distances for the other two BLE channels.

Noise floor variations directly impact the accuracy of applications that rely on RSS measurement, such as IPS. For example, when the variations of noise floor is high, more number of samples must be collected to estimate the actual RSS received from a sender.

B. Path Loss and Shadowing

The results of RSS measurement in outdoor and indoor environments are presented in Figure 6. The x-axis indicates the distance between sender and receiver, and the y-axis represents RSS from the receiver's point of view. Markers and error bars indicate the mean and standard deviation of RSS samples. The logarithmic lines demonstrate the best fit over the collected samples for each channel. Table III presents a summary of the parameters extracted from the experimental results. These values can be directly used by simulators or algorithms that rely on RSS-to-distance mapping.

Comparing Figure6 (a) and (b) with (c) and (d) indicates that the outdoor environments show a steady reduction in signal power versus distance, and the indoor environments show significantly higher variations around the fitted lines. This behavior is observable per distance as well as at a larger scale when the distance from the sender increases. Although this behaviour is partly due to the shorter measurement distance of the indoor environments, there are more important reasons that we discuss as follows.

Indoor environments cause higher shadowing effect due to the existence of reflective obstacles, walls and people movement, compared to the outdoor environments. For a given distance, the stronger shadowing effect results in a significantly higher number of reflected signals received at the receiver. Overlapping a higher number of signals, in turn, may cause either signal amplification or attenuation: signal amplification occurs when the phases of the overlapping signals are similar, otherwise, attenuation happens. This behaviour can be observed in Table III. For example, in channel 39, the σ of the open field park (i.e., 0.36 in environment (a)) is lower than that of the classroom (i.e., 4.3 in environment (c)). These results indicate that RSS-based positioning in outdoor environments is more reliable and requires a fewer number of RSS samples collected.

In addition to higher per-distance variations, at a larger scale, the indoor environments show higher variations as the distance from the sender increases. This behavior has been verified in [START_REF] Zanella | Rss-based ranging by multichannel rss averaging[END_REF] as well. Due to the existence of reflective objects, any small variation in the location of sender or receiver in the indoor environments results in a significant change of the wireless channel. For instance, a new node placement may result in a higher/lower number of signals received by the receiver, thereby, reducing/increasing path loss. Therefore, although it is expected to see lower variations of channel 39 versus distance compared to the other two channels (due to its lower 802.11 interference), the effect of node placement on wirless channel is significant and results in higher variations compared to the outdoor environments. Furthermore, in indoor environments, such as university buildings, people movement is one of the main parameters affecting path loss. In particular, we can observe the higher multipath of environment (d) compared with (c). This is because the corridor environment has a narrower width, and people movement temporarily blocks the LoS.

Another reason behind the non-steady decrease of RSS over distance is antenna anisotropy. Studies show that signal propagation around a transmitter, as well as the capability of an antenna to capture the signals arriving from different directions, are not isotropic. Hardware anisotropy has two main effects: The power of the signal sent and received depend on the direction of both sender and receiver. At some directions, the receiver antenna presents higher sensitivity and captures stronger signals. Similarly, the signal power propagated by the sender differs at various angles. For a LOS communication, these factors directly affect RSS measurement. For example, rotating the sender by x degree and receiver by y degree might result in the strongest LOS link possible. For indoor environments with multiple reflective objects, the effect of radio anisotropy is stronger and harder to predict because it depends on the characteristics of the wireless channel between the sender and receiver. In general, since node and people movements change the angles of signal arrivals and result in a new multipath channel, a non-logarithmic decrease in power can be observed. For example, if a higher number of multipath signals are received from a particular angle, then matching the highest sensitivity direction of the receiver's antenna with that angle results in a stronger link. Although the study of antenna anisotropy is beyond the scope of this paper, these observations indicate that accurate RSS-to-distance mapping requires the investigation of antenna propagation model as well as receiver's sensitivity at different directions.

V. RELATED WORK

The performance of ADV_CHs in large-scale deployments have been studied in [START_REF] Hernández-Solana | Proposal and evaluation of ble discovery process based on new features of bluetooth 5.0[END_REF] and [START_REF] Harris | Bluetooth low energy in dense iot environments[END_REF]. Unfortunately, these studies did not consider the effect of factors such as noise and path loss. The authors in [START_REF] Zafari | Enhancing ibeacon based microlocation with particle filtering[END_REF] showed that BLE-based microlocation can be used in an indoor environment with 0.27m accuracy using LNSM. However, the authors conducted the experiments in a single indoor environment and without considering the effect of multipath. Another study [START_REF] Faragher | Location fingerprinting with bluetooth low energy beacons[END_REF] analyzed the RSS variation of ADV_CHs individually in one environment where the distance between the nodes is fixed. The authors in [START_REF] Ionescu | Improving distance estimation in object localization with bluetooth low energy[END_REF] have used LNSM for object tracking in an environment 25 meters long. The authors in [START_REF] Ji | Analysis of positioning accuracy corresponding to the number of ble beacons in indoor positioning system[END_REF] have used mobile phones to estimate path loss in one environment. They also presented a comparison of path loss for BLE and 802.11 propagation. The main shortcoming of these works is the lack of studying the effect of interference, environmental characteristics and multipath. A recent study [START_REF] Wu | ibill: Using ibeacon and inertial sensors for accurate indoor localization in large open areas[END_REF] on modeling BLE's ADV_CHs for indoor positioning carried out a series of experiments in a library using iBeacon. Although the authors have used LNSM, they did not report the parameters of this model. This overview highlights the need for a comprehensive empirical evaluation of BLE's ADV_CHs in various environments.

VI. CONCLUSION

In this paper, we presented an extensive evaluation and characterization of ADV_CHs in BLE standard. We first studied the mean and variations of noise floor in four different environments, and in particular, we showed the effect of 802.11 interference on ADV_CHs. The findings of this research provide additional evidence for the significance of individual channel characterization due to the various levels of interference on each channel. Next, the signal propagation characteristics of these channels have been investigated, and we extracted the parameters of LNSM through empirical measurements. The proposed models can be directly used in simulation tools as well as BLE-based such as IPS. Despite the merits and popularity of LNSM, we have identified the scenarios where hardware and environmental factors reduce the reliability of this model.

Fig. 2 :

 2 Fig. 2: Experimental environments: (a) is an open field park with line-of-sight (LoS) transmission, (b) is a forest with blocked LoS, (c) is a classroom located in a university building, and (d) is a corridor at the same university building. Environment (c) and (d) include random people movement during experimentation.

  A park with LoS transmission, free of obstacles and operating wireless devices. -Environment (b). A forest with low foliage density, selected to observe the effect of trees and plants between the sender and receiver. The environment is free of interference signals. -Environment (c). A classroom located in a university building. The benches and chairs do not block Line-of-Sight (LoS) transmission. However, to represent a realworld scenario, we did not avoid people movement during the experimentation. Additionally, several 802.11 APs operate in this area. -Environment (d). A corridor located on the third floor of the university building. There are windows on the two sides of the corridor. Furthermore, along the left side of the corridor there are three rooms in which 802.11 APs are operating. Similar to the environment (c), random student movement happened during the experimentation. For simplicity, we refer to the environments by the labels shown in Figure 2. Table

Fig. 3 :

 3 Fig. 3: Communication scenario: (1): Sniffer captures 40,000 noise floor samples in a single ADV channel. (2): Sniffer sends a trigger packet to the transmitter and configures (using a wire link) the receiver to listen to a certain ADV channel. (3) Transmitter starts sending packets on the specified ADV channel and the receiver collects the packets. This process repeats for all the ADV channels.

Fig. 4 :

 4 Fig. 4: Noise floor of the classroom (environment (d)) on channel 37, 38 and 39. The interference of channel 38 is the highest due to its overlap with two 802.11 channels.

Fig. 5 :

 5 Fig. 5: The transmit mask of 802.11 channels and their overlap with BLE's ADV_CHs. BLE's channel 39 and 38 are the least and most affected channels by 802.11 interference, respectively.

Fig. 6 :

 6 Fig. 6: Empirical measurement of RSSI versus distance. Transmission power is 0dBm. The labels used correspond with those used in Figure 2. For example, label "(a)" in this figure represents the results collected in environment "(a)" of Figure 2.

  Fig. 1: The 2.4GHz frequency band shared by BLE and 802.11. The red channels are the advertisement channels used by BLE beacons.

					Wi-Fi channel 1							Wi-Fi channel 6							Wi-Fi channel 11
	Frequency	2402 MHz	2404 MHz	2406 MHz	2408 MHz	2410 MHz	2412 MHz	2414 MHz	2416 MHz	2418 MHz	2420 MHz	2422 MHz	2424 MHz	2426 MHz	2428 MHz	2430 MHz	2432 MHz	2434 MHz	2436 MHz	2438 MHz	2440 MHz	2442 MHz	2444 MHz	2446 MHz	2448 MHz	2450 MHz	2452 MHz	2454 MHz	2456 MHz	2458 MHz	2460 MHz	2462 MHz	2464 MHz	2466 MHz	2468 MHz	2470 MHz	2472 MHz	2474 MHz	2476 MHz	2478 MHz	2480 MHz
		37	00	01	02	03	04	05	06	07	08	09	10	38	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25	26	27	28	29	30	31 32 33	34 35 36	39
	ISBN 978-3-903176-05-8 © 2018 IFIP

Table . I

 . : Environmental Parameters

	Parameter			Environment
			(a)	(b)		(c)	(d)
	Ambient Temperature [°C]	11.0	14.8	21.0	21.1
	Relative Humidity [%]	58.1	53.4	48.9	49.0
	Area [m 2 ]		Open	Open	18×12 31×1.8
	Number of Locations		11	11		15	15
	Packet Size		9Byte 9Byte 9Byte	9Byte
	Beacons/Distance		1000	1000	1000	1000
				Ch 37
				Ch 38
		Trigger packet	Ch 39
			Channel 37
			Channel 38
			Channel 39
		Distance (m)
	n	n-1	…	3	2	1

Table .

 . II: Noise Floor Characteristics of Various Environments

	Parameter	Channel		Environment
			(a)	(b)	(c)	(d)
		37	-101.1 -100.7 -101.0 -101.5
	µ	38	-101.3 -101.1 -101.2 -101.5
		39	-101.7 -101.6 -101.3 -101.7
		37	0.74	0.98	2.26	1.12
	σ	38	0.74	0.88	2.16	2.54
		39	0.77	0.89	1.96	1.28
		BLE
		Channel 37
		2402MHz	Center Frequency
				Transmit spectrum mask
				802.11b/g/n (Channel 1)
			22MHz width	-20dBr
			40MHz width	-28dBr
			60MHz width
					-40dBr
	2382MHz	2401MHz	2412MHz	2423MHz		2442MHz
		BLE
		Channel 38 (2426MHz)	Center Frequency
				Transmit spectrum mask
				802.11b/g/n (Channel 6)
			22MHz width	-20dBr
			40MHz width	-28dBr
			60MHz width		-40dBr
	2407MHz	2426MHz	2437MHz	2448MHz		2467MHz
					BLE
	Transmit spectrum mask	Center Frequency	Channel 39 2480MHz
	802.11b/g/n (Channel 11)
			22MHz width	-20dBr
			40MHz width	-28dBr
			60MHz width
					-40dBr
	2432MHz	2451MHz	2462MHz	2472MHz		2492MHz

Table .

 . III: Summary of the LNSM Parameters

	Parameter	Channel		Environment
			(a)	(b)	(c)	(d)
		37	2.07 2.17 2.47 2.14
	Path Loss (η)	38	2.15 2.01 2.87 2.20
		39	2.05 1.87 2.13 1.98
		37	0.39 0.41 4.20 4.60
	σ ch	38 39	0.23 0.29 4.60 3.67 0.36 0.20 4.30 4.48
		37	34	34	33	34
	P L(1)	38	31	33	31	30
		39	31	33	36	31
	P L(1): Path loss at reference distance d0, where d0 = 1