R Horvath

J-F Roux

J-L Coutaz

J Poëtte

B Cabon

C Graham

Ultrafast InGaAs photoswitch for RF signal processing

Keywords: Photoconductive sampling, Ion Implanted InGaAs, ultrafast electronics, metal-semiconductor-metal devices, Radio Over Fiber, photonic assisted data processing, heterodyne photomixer

Optical processing of RF signals is demonstrated in this communication using photoswitches made from Nitrogen Implanted InGaAs. The sampling device shows an ultrafast picosecond response time while activated by ultra-short optical pulses or modulated optical beam centered at the wavelength of 1.55 µm. The optoelectronic device is embedded in a microwave coplanar waveguide which has a high electrical bandwidth allowing to process signals in the 1-67 GHz band. We investigate the potentiality of this component to be used either in photonic assisted sampling for future Analog to Digital Converters or in photonic assisted heterodyne detection of RF modulated carriers.

I. INTRODUCTION

microwave photonics is a technology area which includes generation, processing, analog-to-digital conversion (ADC), of microwave signals using optoelectronic technological bricks such as laser, modulators, detectors and fibers. It is at the heart of future Fiber and Wireless telecommunication networks. Indeed, recent development of high performances optoelectronic components has permitted to reach the so-called THz communication band and wireless communications above 100 GHz has been demonstrated, allowing higher data rate transmission [START_REF] Seeds | TeraHertz Photonics for Wireless Communications[END_REF]. In such a technological framework, the use of complex well-proven modulation schemes already used at lower frequency is challenging to reach multi-GBits/s data rate. However, such modulation requires additional signal processing at the receiver location such as heterodyne detection together with numerical data post-processing which is usually handled by specific Digital Signal Processing [START_REF] Lecoche | Transmission Quality Measurement of Two Types of 60 GHz Millimeter-Wave Generation and Distribution Systems[END_REF]. In order to gain performances and offer cost-effective or robust solutions it might be interesting to propose photonic assisted processing of the received RF signal modulated by the complex data stream. For instance, the use of ultralow jitter laser sources might increase the performances of the receiver by one order of magnitude as compared to full-electronics approach for ADC of GHz RF signals [START_REF] Khilo | Photonic ADC: overcoming the bottleneck of electronic jitter[END_REF]. In such high bandwidth applications, one key function is the mixing of RF and optical signals. It requires that the mixing process offers very high bandwidth together with linear responses to both the optical and electrical signals. For that it has been shown that simple Metal-Semiconductor-Metal (MSM) photoswitches (PSW) made from Low-Temperature-Grown (LTG) GaAs could reach these requirements. Hence, this component has been used as ultrafast optoelectronic sampler for ADC [START_REF] Nathawad | A 40-GHzbandwidth, 4 bit, time-interleaved A/D converter using photoconductive sampling[END_REF][START_REF] Roux | RF Frequency Response of Photoconductive Samplers[END_REF] and mixer showing applications in the RF band as well as in the THz one [START_REF] Kuppam | Study of Ultrafast Semiconductor Photoswitches for CW RF Signal Sampling and Modulation[END_REF][START_REF] Peytavit | Highly efficient terahertz detection by optical mixing in a GaAs photoconductor[END_REF]. Its principle rely on the increase of the electrical conductance of the semiconductor through absorption of the optical beam, making this device an optically controlled resistance. As compared to phototransistors that can also be used for such mixing, PSW do not require any DC bias to operate, and consequently may strongly reduce the DC power consumption of the mixer. Unfortunately, this kind of GaAs based component is not fully compatible with the 1.55 µm telecommunication wavelength as GaAs is mostly transparent at this photon energy. In order to solve this problem, one may use InGaAs absorbing layers. Indeed up-and downconversions of an input RF signal with frequency spreading up to 20 GHz with an optical local oscillator (LO) consisting in a modulated 1.55 µm optical beam has been demonstrated [START_REF] Song | Microwave Photonic Mixer Utilizing an InGaAs Photoconductor for Radio over Fiber Applications[END_REF]. Unfortunately, due to the long carrier lifetime in the InGaAs, the LO frequency was limited to 300 MHz, making this component not suitable for high data rate optoelectronic processing. Nevertheless, it is possible to strongly reduce the carrier lifetime in such semiconductor by introducing a large number of carrier trapping centers thanks to ion implantation or irradiation [START_REF] Carmody | Ion-implanted In0.53Ga0.47As for ultrafast optoelectronic applications[END_REF]. In this work, we investigate the use of Nitrogen Implanted InGaAs PSW that shows an ultrafast carrier response time of less than 2 ps. We demonstrate that it can be used for optoelectronic mixing and sampling of RF signal in the 1-67 GHz band using lasers operating at 1.55 µm: femtosecond fiber laser or self-modulating mode-locked laser diode at 60 GHz. These preliminary results open the way to more advanced studies in RF wireless communications systems in which high bit rate data streams will be sampled and/ or demodulated by optoelectronics means using such a simple, M This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 642355 FiWin5G. low-cost and robust component.

II. DEVICE DESCRIPTION

The sampling device has been preliminary studied in [START_REF] Graham | Nitrogen ion implanted InP based photo-switch[END_REF] where it has been shown that it possess picosecond response time and good dark resistance. In this previous work the device has been used to demonstrate simple switching experiment at frequency up to 15 GHz. The component consists in a coplanar waveguide (CPW) which central line (width 25 µm) is interrupted by a 25 µm gap with interdigitated fingers as shown in the inset of Fig. 1. The fingers may be of different numbers (3 to 5) and their separation is consequently varying from 3 to 5 µm. The CPW is about 750 µm long and is made from gold electrodes deposited on a Fe:InP substrate. At the location of the 25 x 25 µm² gap, a 300 nm thick mesa of InGaAs was deposited onto the Fe:InP substrate. The device is then irradiated with Nitrogen atoms that create a large number of defects in the InGaAs layer and then penetrate deeply in the Fe:InP substrate. Considering the implantation parameters the concentration of EL2 type defects is estimated to be around 5 10 17 cm -3 ensuring a picosecond response time. In the following sections all the measurements were done "on wafer" using RF electrical coplanar probes.

III. DEVICE CHARACTERIZATION

As mentioned above, the electrical characteristics of the devices (I-V curve and dark resistance) have already been measured in [START_REF] Graham | Nitrogen ion implanted InP based photo-switch[END_REF] and we have checked that most of the samples have kept similar characteristics. We then investigate in more details the high bandwidth properties of the PSW in terms of electrical and optoelectronical bandwidth. Before that we want to remind the basic operation of such an ultrafast PSW when illuminated by an optical beam. In the dark state, the lumped electrical equivalent model of the PSW is a capacitance C in parallel with a dark resistance Rdark (see the inset of Fig. 2). When illuminated by an optical beam with photon energy above the bandgap of the InGaAs layer, the conductance increase due to the photo-generation of free carriers in the absorbing layer. In a first approximation, the conductance varies linearly with the absorbed optical power. Its value depends on the geometry of the switch and also on the dynamics of the photogenerated carriers and their electrical properties such as mobility. In [5 and 6] we have studied the behavior of an ultrafast photoswitch illuminated either by femtosecond optical pulses or by amplitude modulated optical beams. In both cases, we have demonstrated that the temporal behavior of the device is mainly governs by the carrier lifetime of the electrons that can be roughly approximate by an exponential law e -t/e where  e is the carrier lifetime of the photogenerated electrons.

In order to investigate the dynamics of the optoelectronic response of the samples, we have performed optoelectronic autocorrelation [START_REF] Delord | Study of optoelectronic sampler linearity for analog-to-digital conversion of RF signals[END_REF]. This experiment consists in illuminating the photoswitch with two femtosecond pulses that are delayed by t one from each other thanks to an optical delay line while the PSW is DC biased and loaded by a 50  resistor. The delay, t, is continuously varied and we record the average voltage observed at the load resistor. Because the PSW acts as a voltage divider with the load resistor the overall circuit shows a non-linear response to the optical pump excitation. Consequently, as compared to the case where the two exciting pulses are separated by a time much larger than the PSW response time (t >>  e ), the autocorrelation signal is decreasing as long as the time delay in between the two pulses is reduced: the autocorrelation signal is minimal at t = 0. Moreover, the temporal behavior of the optoelectronic autocorrelation trace gives a direct insight about the PSW temporal dynamics if the duration of the exciting optical pulses is much shorter than  e . Here we perform the experiment with a fiber femtosecond laser operating at 1.55 µm (MENLO C-Fiber) with a repetition rate of 100 MHz. The two delayed optical pulse trains are propagating toward the switch through 3 meter of SMF 28 fiber which linear group velocity dispersion is compensated by a section of DCF 38 fiber (THORLABS DCF 38). We checked that the duration of the optical pulses at the output of the fibers is limited to less than 100 fs by measuring the optical autocorrelation trace thanks to Second Harmonic Generation in a BBO non-linear crystal. This optical autocorrelation trace is plotted in Fig. 1 (continuous grey line) it gives also the zero delay (t= 0) time of the experimental set-up. Finally we record the optoelectronic autocorrelation signal for average optical power of 1.5 and 2.5 mW for each of the two delayed beams. A typical result is plotted in Fig. 1 (dashed grey line) it consists in a double side exponential function and shows a very sharp peak around zero delay which is a signature of some optical interferences between the two optical pulses. Ignoring this coherence peak, the optoelectronical autocorrelation trace is fitted with the formula: A 1 exp(-t/)+ A 2 exp(-t/ 2 ). We found the best fit for A1=0.6, A2=0.2 and = 1.2 ps while = 20 ps (black line). These time constants confirm the picosecond response time of these nitrogen implanted InGaAs PSW [START_REF] Graham | Nitrogen ion implanted InP based photo-switch[END_REF]. Here we show that the 4.4 ps response time that was observed in [START_REF] Graham | Nitrogen ion implanted InP based photo-switch[END_REF] with an ultrafast oscilloscope was certainly limited by the bandwidth of the electronic sampling heads. From the 1.2 ps response time, we expect a cutoff frequency of the device larger than 100 GHz. The longer 20 ps time constant that we observe may either be attributed to a contribution of holes or to an electron de-trapping time; its lower amplitude reduces its impact on the overall optoelectronic bandwidth of the PSW.

In a second experiment, we have checked the electrical bandwidth of the device by measuring its S parameters in the dark state using a 0.04 -65 GHz Vector Network Analyzer (VNA). The experimental frequency evolution of the S 12 parameter is plotted in Fig. 2 together with the theoretical curve obtained considering the basic lumped element equivalent model of the gap. We found an equivalent capacitance of 5.7 fF and a dark resistance of 5 k. This low capacitance, also insure an ultrafast RC response time shorter than 300 fs for a 50  load impedance.

IV. PHOTOCONDUCTIVE SAMPLING OF RF SIGNALS

After characterization of the fabricated structures we have used them to demonstrate their ability of sampling RF signals.

Here we have chosen an asynchronous sampling experiment in which the RF signal is free running while the sampling frequency is fixed by the repetition rate of the femtosecond laser (F laser = 100 MHz). The electrical input of the PSW is directly excited by the AC output of an Agilent RF Synthesizer E8257 D working in the 250 kHz -67 GHz band with enhanced power mode (up to +15 dBm). In this experiment, the PSW is not DC biased but it is optically activated by the train of fs pulses delivered by the femtosecond laser already described above. The RF output signal of the PSW is characterized with a spectrum analyzer (Anritsu MS2667C of 30 GHz bandwidth). As shown in [START_REF] Roux | RF Frequency Response of Photoconductive Samplers[END_REF] when excited by an RF signal of frequency F RF , the illuminated PSW delivers a train of electrical pulses at the repetition rate F laser , which amplitude depends on the amplitude of the RF signal, the optical power and the conversion efficiency of the switch. In the frequency domain, the electrical output consists in a Dirac-comb centred on the RF angular frequency F RF with peaks separated by the frequency F laser of the laser pulses train:

  ( ) ( ) n n RF laser n S f S f f f n f        
The factor S n (f) is the amplitude of the n th harmonics of the frequency comb. It depends on the conversion efficiency of the switch which is decreasing when the RF frequency is above the cutoff frequency of the device. For RF input signals in the range 1 to 67 GHz, we measured the power of the n th harmonic using a 80 dB amplification stage with a 1 GHz bandwidth. So, we choose the n th harmonic which falls in the vicinity of 15 MHz. For example, if the input RF signal frequency is 30.015 GHz, the output of the PSW consists in a Dirac Comb centered around 30.015 GHz with harmonics every 100 MHz. The 300 th harmonic of the sampled signal is positioned at 15 MHz and is thus amplified by 40 dB. The results, compensated with the losses of the RF cables are plotted in Fig. 3. We observe that the amplitude of the sampled signal shows a small frequency roll-off. Actually, depending on the experimental conditions we observe only 4 to 6 dB of decrease in between the amplitude measured at 1 GHz and that measured at 67 GHz. This confirm the large bandwidth of the device. Moreover, by varying the input RF power from -15 dBm to 15 dBm we checked that the sampler output amplitude depends linearly on the input power. On the other hand we observe that the amplitude of the RF output increase by approximately 10 dB when the optical power is increased by 7 dB (from 0.88 to 4.3 mW). This means that the RF output power varies approximately with the 1.5 power of the optical power, although this dependence should be, in principle, quadratic for low illuminating power.

V. HETERODYNE DEMODULATION OF AMPLITUDE MODULATED RF SIGNALS

Another potential application of such ultrafast PSW is modulation or demodulation of RF signals with optical signals using the PSW as a hybrid (optical / electronic) mixer [START_REF] Kuppam | Study of Ultrafast Semiconductor Photoswitches for CW RF Signal Sampling and Modulation[END_REF][START_REF] Peytavit | Highly efficient terahertz detection by optical mixing in a GaAs photoconductor[END_REF].

Here we have investigated the potentiality of the InGaAs PSW to perform this operation in an experiment which is very similar to the previous one. Here, the optical signal consists in a 1.55 µm CW beam modulated at a frequency F LO and the RF input signal is at a frequency F RF . These signals are mixed within the PSW, the flow of photocarriers that are generated by the absorption of the 1.55 µm optical beam being accelerated by the RF electrical field applied to the PSW. This mixing results in an RF output signal at the difference frequency F IF =F RF -F LO . Here again the RF frequency varies from 1 to 65 GHz while the RF power varies from -15 to + 15 dBm. As a modulated laser beam, we used an InAs/InP-based quantum dash mode locked laser diode self-oscillating at 58.625 GHz [START_REF] Brendel | Chromatic Dispersion in 60 GHz Radio-Over-Fiber Networks Based on Mode-Locked Lasers[END_REF]. In order to compensate for the laser dispersion we use 70 m of SMF28 monomode fiber and a variable optical attenuator allows to vary the average optical power used to excite the PSW. The RF output of the PSW is detected thanks to a RF Spectrum Analyzer (RFSA) (Anritsu MS2668C 9 kHz -40GHz). For RF input frequency in the range 19 -67 GHz, the IF frequency falls within the allowed detection band. In Fig. 4 we have plotted the amplitude of this IF signal for an input RF power of 15 dBm. For an optical power of 5 mW, the IF RF power is about -65 dBm leading to an estimated losses P IF /P RF of 80 dB. We note that these large losses are within the same order of magnitude than the ones presented by Unbiased Ultrafast Photodiodes used as Optoelectronic mixer [START_REF] Rouvalis | Optoelectronic detection of millimetre-wave signals with travelling-wave uni-travelling carrier photodiodes[END_REF] and might been strongly reduce by smart engineering of the photoconductive device [START_REF] Peytavit | Highly efficient terahertz detection by optical mixing in a GaAs photoconductor[END_REF]. Similarly to the ultrafast photodiodes from [START_REF] Rouvalis | Optoelectronic detection of millimetre-wave signals with travelling-wave uni-travelling carrier photodiodes[END_REF], our PSW presents an ultra large bandwidth of more than 65 GHz as confirm by its constant response over the whole frequency range except in the vicinity of 58 GHz where a systematic experimental decrease, which is under investigation, is observed. Finally, we have experimentally confirm the linear response of the photomixer versus the RF input power and we have demonstrated that the losses of the mixer might be reduced by increasing the impinging optical power (inset of Fig. 4).

VI. CONCLUSION

In this work we have presented preliminary results of photoconductive sampling and mixing of RF signals using Nitrogen implanted InGaAs based CPW structures. The device is characterized by 5 kOhms dark resistance and low equivalent capacitance. Its optoelectronic response shows a very fast recovery time of 1.2 ps that allows us to demonstrate sampling of 67 GHz signals. Moreover we have performed heterodyne mixing in between an RF signal of frequency up to 67 GHz and an optical beam self-modulated at 58.625 GHz. These first demonstrations of principle are opening the way to demodulation and photonic assisted processing of high bit rate data streams carried either by the RF or by the optical signal.

Fig. 1 .

 1 Fig. 1. Optoelectronic (dashed grey line) and optical (grey line) autocorrelation traces of a Nitrogen implanted InGaAs PSW (inset). A double exponential fitting curve with time constants of .2 and 20 ps is also shown (black line).

Fig. 2 .

 2 Fig. 2. S21 parameter of the PSW in the dark state. The inset shows the lumped elements equivalent circuit of the device.

Fig. 3 .

 3 Fig. 3. Output power of the sampled signal for different experimental conditions: average optical power of 4.3 or 0.88 mW and RF power of -15 dBm or + 15 dBm. The x axis represents the frequency of the RF input signal.

Fig. 4 .

 4 Fig. 4. Power of the IF signal for different input RF frequencies. The PSW is used as a heterodyne mixer in between a 14 dBm RF input signal and a 5 mW optical beam self-oscillating at 58.625 GHz. The inset shows the IF power dependence onto the optical power.

ACKNOWLEDGMENT

We would like to thank Mr S. Formont from THALES TSA for providing us RF amplifiers and filters and Mr N. Corrao, from IMEP-LAHC for his valuable help in preparing the RF experiments.


Illyyne Saffar
email: illyyne.saffar@nokia.com

Marie Line

Alberi Morel

Deep Kamal

Singh

Cesar Viho
email: cesar.viho@irisa.fr

Machine Learning with partially labeled Data for Indoor Outdoor Detection

Keywords: Environment classification, Machine Learning, Indoor Outdoor Detection, 3GPP radio measurement, crowdsourcing, real user activity

This paper demonstrates the feasibility of an hybrid/semi-supervised classification method for detecting the environment of an active mobile phone, based on both labeled and unlabeled cellular radio data. Precisely, we provide answers to the following question: what is the environment of the mobile user when it is/was experiencing a mobile service/application: indoor or outdoor? Implementing this method within the mobile network is interesting for mobile operators since it has low complexity, is less human intrusive (minimal intervention of mobile users) and more accurate. The semi-supervised classification algorithm learns to identify the environment using large and real collected 3GPP signals measurements. As compared to existing work, in addition to existing parameters used for classification, we propose to also use a radio metric called Timing Advance. It is computed within the mobile network. We empirically validate the innovative semi-supervised algorithm using new real-time radio measurements, with partial ground truth information, gathered daily, weekly, monthly, from indoor and outdoor locations and from multiple typical and diversified environments crossed by mobile users. The study confirms the effectiveness of the proposed scheme compared to the existing supervised classification methods including SVM and Deep Learning.

I. INTRODUCTION

Recent technological breakthroughs have extended the mobile phones' features, functions and capabilities, which are now used for more than just communicating or affording applications. Recently, mobile devices are being utilized to know the consuming habits of individuals and communities [START_REF] Bulut | Understanding User Behavior via Mobile Data Analysis[END_REF], [START_REF] Holzbauer | Impact of Socially Based Demand on the Efficiency of Caching Strategy[END_REF], [START_REF] Xu | Identifying Diverse Usage Behaviors of Smartphone Apps[END_REF]. Thus, our purpose is to inject this learned cognition into mobile 5G networks to help them grow smarter and be more efficient when faced to the increasing complexity of network management combined with numerous new applications and their heterogeneous needs.

As a first step to bring such additional knowledge to the network, we target Indoor/Outdoor Detection (IOD) in this paper. IOD refers to the estimation of the mobile users' environments, that is to infer whether the user is Indoor or Outdoor. IOD is a cornerstone of the user behavior contextualization, which in turn can be used for learning the user behavior, adapting mobile network resources, etc [START_REF] Mekki | HTTP adaptive streaming with indoors-outdoors detection in mobile networks[END_REF], [START_REF] Radu | A semi-supervised learning approach for robust indoor-outdoor detection with smartphones[END_REF]. The idea is to have more information on the user like knowing his environment type or his location.

IOD can be performed automatically and in real-time using machine learning techniques, which in turn need data for learning. Thus, data collection is the first phase of designing IOD solution based on machine learning. Recently, a new crowd-sourcing approach [START_REF] Marina | Impact of indoor-outdoor context on crowdsourcing based mobile coverage analysis[END_REF], [START_REF] Cainey | Modelling download throughput of LTE networks[END_REF] is becoming popular for collecting and analyzing real and large network measurement datasets coming from mobile phones or any other connected devices. This method exploits smartphones (with built-in cellular network interface) with their various measurement sensors. Additionally, data obtained from smartphones has the natural mobility vector of people carrying them. This ensures costeffective, continual and fine-grained spatio-temporal monitoring and analyses of mobile networks. For our work, we propose to investigate this concept of large and real crowdsourced measurements for IOD. We also propose to extend it to mobile networks to deal with the challenge of detecting the environmental context of mobile users from network side. The idea is to collect data, which is measured or derived within network, and then consider it as an input for the machine learning based classifier used for training, learning and then detection. The data measured by multiple UEs during their connection is sent to eNB, using standardized procedures.

Such solutions are interesting for mobile network operators that wish to exploit cognition of user behavior to optimize/customize their service delivery with minimal intervention of the users. Furthermore, such measurements, as an alternative to coverage modeling or drive tests [START_REF] Marina | Impact of indoor-outdoor context on crowdsourcing based mobile coverage analysis[END_REF], capture reality well, reveal real life of a mobile user while at the same time being less expensive. This method can then be implemented by the operators in their networks, as a generic solution, independent of the implementations of particular manufacturers. Consequently, it allows the mobile network to exploit direct measurements at user side to deduce contextual factors such as the user environment.

In 4G/5G cellular networks, such solutions are technically feasible since enormous amount of mobile measurement data is collected by the mobile terminal. This data is regularly sent to the network using standardized protocols and interfaces during each UE's connection to the cell (on a per-procedure basis and on a network defined event basis). This measurement data is referred to as LTE UE Measurement Data (LUMD) [START_REF] Ray | Localization of LTE measurement records with missing information[END_REF]. LUMD contains rich information on mobile performance and RF metrics such as signal strength (Reference Signal Receive Power or RSRP), signal plus interference and noise strength (Reference Signal Receive quality or RSRQ). It also includes the Channel Quality Indicator (CQI) that is a function of SINR.

In this work, we aim to achieve the following objectives:

• (1) infer the user environmental context, from certain LUMD metrics collected in crowdsourcing mode and the radio metric, Timing Advance, assessed by the network when the user is connected to a session. In fact, the environment considered is divided into two main types:

-Indoor: at home, in restaurant, in cafe/ at work or in other building types, etc. -Outdoor: pedestrian, running or in car moving with high speed.

• (2) consider the constraint that the inference shall be done at network side with minimal human interaction or intervention. To achieve ( 1) and ( 2), we design a method for training IOD automatic classifier based on a weakly or partially labeled crowdsourced dataset. Such dataset reduces human intervention to the lowest possible. Indeed, the labeled data, used for machine learning training, is either tagged manually or automatically. Manual data tagging can be expensive, complex and even unfeasible for mobile operators if they have to tag all the collected crowdsourced data.

In this paper, we are interested in Machine Learning (ML), one of the popular techniques, for automatic IOD. Among ML families, we consider supervised learning and more particularly semi-supervised learning which can be seen as a mix of supervised and unsupervised approaches. Supervised learning is more adapted for classification tasks. It uses labeled data to learn the mapping between data and the labels. Unsupervised learning looks for patterns and structures within the data for tasks such as clustering. The semi-supervised learning, which is an hybrid approach, is becoming popular with growing abundance of data in this era. It proposes a learning scheme based on partially or weakly labeled dataset in order to achieve a classification task or a function approximation task. In our case, semi-supervised learning allows the mobile operator to use labeled data from a few users and combine it with lot of unlabeled and easily available data collected from several users. This combination allows to learn all possible environment types related to the user behavior.

The rest of this paper is organized as follows. Section II describes the main IOD works in literature. In Section III, a comparative analysis of crowdsourcing and drive-test data collection modes is provided. In section IV, results with supervised classification and clustering algorithms are given. Section V and VI present a new Deep Learning-based semisupervised learning approach proposed for IOD from the network side. Section VI discuss the results.

II. RELATED WORK

In the literature, the IOD issue has not been largely studied: only few works address it. Proposed solutions are usually divided in to two categories [START_REF] Edelev | Knowledge-assisted location-adaptive technique for indooroutdoor detection in e-learning[END_REF]. IOD is either considered as a statistical issue where a weighted score or a threshold is defined to determine the mobile environment, or as a classification problem sorting mobile users between multiple classes. In most of these works, only two classes are considered (Indoor/Outdoor) but, in some works, three classes are selected (e.g. Indoor/Semi-Outdoor/Outdoor). The Figure 1 shows an illustration of the whole dependency of existing classes. In addition to such categorization, IOD problem can also be distinguished based on the location where IOD is performed, either at the mobile terminal side or at the mobile network side. In the following, we highlight some of the works dealing with the IOD issue, presenting them according to this classification.

In first category, [START_REF] Zhou | Iodetector: A generic service for indoor outdoor detection[END_REF] looks at a threshold of signals collected from some phone sensors related to: radio signals, cell signal strength, light intensity as well as the magnetic sensor to infer whether the mobile user is indoor or outdoor. However, this threshold is specific to the experimental settings where it is calculated. It is not generalizable to new environments. Thus, using just a threshold decreases the IOD accuracy. Similar to [START_REF] Zhou | Iodetector: A generic service for indoor outdoor detection[END_REF], the work in [START_REF] Radu | A semi-supervised learning approach for robust indoor-outdoor detection with smartphones[END_REF] also uses the same signals, but also considered sound intensity, battery temperature and the proximity sensor. For IOD, they propose a semi-supervised approach: a co-training solution. They use 2 classifiers in parallel with a weighted score of classification probability to improve the final performance of IOD. For every classifier, they select a different set of sensors to learn different perspectives and patterns. This work shows high performance (more than 90% of accuracy) in the detection of new instances in unknown environments. However, the impact of this work is limited since their database is not highly representative. Indeed, the used data set was only collected in three places (the campus area, city center, residential area) which are not enough to train a general IOD system.

The work in [START_REF] Mekki | HTTP adaptive streaming with indoors-outdoors detection in mobile networks[END_REF], proposes a video streaming optimization based on adaptation as a function of the user location in time. For that, IOD is computed via a Bayesian detector that combines measurements from two smartphone sensors to decide the user environment type.

In second category, in [START_REF] Ben Hadj | Optimization of Radio Measurements Exploitation in Wireless Mobile Networks[END_REF] authors optimize the use of radio measurements in wireless networks. Literally, they use radio signal measurements collected in different situations of mobility with varying speed (low, medium, high) namely (pedestrian, incar and unmoving). They dynamically estimate the signal attenuation. This in turn helps them to efficiently classify mobile user environment (pedestrian, incar, unmoving) and finally improves the handover process. Authors assume that once the signal power attenuation is estimated correctly, we can easily come to classify whether the mobile user is pedestrian, in car or unmoving. This is because the measured power signal for an unmoving user does not show too much variations unlike the incar or pedestrian cases. Nevertheless, this proposition is still at an early stage and it has not been thoroughly developed yet. In [START_REF] Ray | Localization of LTE measurement records with missing information[END_REF], the main issue is to localize the mobile user by estimating its longitude and latitude in a most possible accurate way. For this, they made the assumption that mobile users are outdoor, thus giving rise to the importance of IOD and the necessity to classify the user environment. For the classification task, they used RSRP and RSRQ signals and tested many algorithms: SVM, logistic regression and random forest. SVM was the retained solution since it performed best.

In this paper, we focus on the IOD automation within the network side using machine learning algorithms. They are trained using large real dataset while minimizing the mobile user interaction (minimal labels). We look at the performance in terms of F 1 -scores of supervised and semi-supervised IOD methods. Goal is to evaluate the minimal amount of labeled data required for obtaining good IOD performance.

III. COLLECTED DATA FOR IOD

In this section, we analyze the statistical differences by focusing on the empirical cumulative distribution function (CDF) between indoor and outdoor environments, using a large and real data-set collected at multiple places, many environments. We illustrate the impact of the two environments on the empirical CDFs, according to where the data is collected.

A. Data Description

Our large data set consists in Time, 3 LUMD radio signals, the metric Timing Advance (TA) and the label when it is known. Thus, it has a vector of 6 features with the label:

• Time: time of signal record • RSRP: the average received power of the Reference Signal (RS) between -140 dBm to -44 dBm [START_REF]Evolved Universal Terrestrial Radio Access (E-UTRA); Requirements for support of radio resource management[END_REF], sent by eNB. • RSRQ: the ratio between RSRP and RSSI (Received Signal Strength Indicator) between -19.5dB and -3dB [START_REF]Evolved Universal Terrestrial Radio Access (E-UTRA); Requirements for support of radio resource management[END_REF], that represents the total power of the received signal (including the transmitted signal, the noise and the interference). • CQI: indicator reported by UE to eNB that gives the most appropriate modulation scheme and coding scheme to be used for transmission [START_REF]Evolved Universal Terrestrial Radio Access (E-UTRA); Physical layer procedures[END_REF].

• TA: used to control Uplink signal transmission timing.

It is indicated by eNB to UE via a Timing Advance command [START_REF]Evolved Universal Terrestrial Radio Access (E-UTRA); Medium Access Control (MAC) protocol specification[END_REF].

The set of these signals has been collected during 9 months, 24h/7 (From October 2017 until June 2018), with an average of 1 measurement per 15 seconds while the mobile phone session is active and 1 measurement per 2 minutes otherwise.

The dataset is made of 40% of labeled data and 60% of unlabelled data. The 9 months collection has been performed in many different environments like mountain, beach, forest, companies, cafes, streets, bars, parks, restaurants, lakes, etc... It was also performed in many cities and places like countryside, villages, small cities, metropolis, and different countries, but for this paper we are only studying the data collected in France (Figure 2). This long collection period allows us to have data reflecting all weather types: Heavy Rain, Foggy, Sunny, Snowy, Windy, Rainy,... i.e. almost the 4 seasons. Therefore with this campaign of data measurement we try to be as close as possible to the complexity and the variety of a mobile user moving in real world. In crowdsourcing mode, the collected data consists of signals measured by the mobile phone and sent to the eNB. Our dataset described in the previous subsection has been collected using this mode. Figures 3 shows the empirical cumulative distribution functions (CDFs) of RSRP and CQI obtained with the dataset. The significant offset between the indoor and the outdoor curves, results from substantial difference and attenuation variation in radio signal propagation. It is mainly due to reflection, diffraction, dispersion and attenuation experienced in indoor environment. However, we note that there is some overlap between the ranges of RSRP and CQI values. Also the extreme values seen in the two indoor and outdoor CDFs (located in tails) get similar and the division between the two gets blurred. The behaviour at the juncture of extreme values can be explained by the ambiguous characteristics of the environment when a user is at high speed (Train, car...) or when he is in a semi indoor environments (like balconies, semi-open building, near a window.., etc. We argue that these points are ambiguous and will pose a good challenge for supervised classification, since they can be indifferently classed indoor or outdoor at the same time. An alternate data collection mode, widely used to collect data, is the drive-test mode. However, this mode imposes limits on capturing the reality through the data collected in this mode. Such data collection campaigns are run for limited hours per day during short period (couple of weeks) and at some specific places. To model this way of collecting data, referred as drivetest mode, we extract a portion data (EPD) from the whole dataset. We aimed by this selected EPD data to be as close as possible to the type of places where the drive-test was performed by one of the top 3 American operators in New York City in [START_REF] Ray | Localization of LTE measurement records with missing information[END_REF]. Therefore to build EPD we consider data only in metropolis (Paris and southern suburbs see figure 5). Indeed, Paris as metropolis, has a dense and specific architecture which allows better comparison with NYC. Concerning indoor data, we selected instances where the user was strictly indoor and, thus, not in "semi-indoor" positions like semiopen building or balconies,...etc. For outdoor data, we chose the instances where the user was either pedestrian or in vehicle in different city streets (limited speed). Thus, to mimic drivetest we consequently ignored data coming from environments like subway/ countryside/ forest/ beaches/ Mountains/ .../ etc. We did this to enable a fair comparison between the two modes. Figure 4 shows well separated RSRP empirical cdfs between the classes indoor and outdoor. The superimposed points of both the cdfs we judge conflicting have disappeared. The overlap between both the cdfs, which previously led to ambiguity, has disappeared. This is due to the significant distance between the indoor and the outdoor curves. In the case of CQI cdfs we notice a similar phenomenon. This analysis allows us to argue that supervised classification will run better on labeled dataset collected in drive-test mode as compared to obtained through crowdsourcing mode. P recision.Recall P recision + Recall where precision is the number of correct positive results divided by the number of all positive results returned by the classifier, and recall is the number of correct positive results divided by the number of all relevant samples. F 1 -score is one of the most used metrics in case of unbalanced data classes. Indeed, the statistics of our data show that the data proportion between indoor and outdoor classes is unbalanced 65% Indoor vs. 35% Outdoor. This reflects the reality since people, in general, spend more time at home or in indoor environments than in outdoor environments. For the experiments, we divided the dataset as follows: 70% for training, 30% for validation and test. We evaluate the impact of both input pairs (RSRP, RSRQ), which is the reference input for IOD in the literature, vs. (RSRP, CQI), in three cases:

• Training and evaluation on labeled EPD collected in drive-test like mode (see Table I),

• Training on labeled EPD and evaluation on the rest of the labeled data of crowdsourcing mode, thus operating with unknown environments (see Table II) and, • Training and evaluation on labeled data collected in crowdsourcing mode (see Table III). As shown in the table I, running either classification (SVM, Random Forest, Neural Network) or clustering (k-means) algorithms on EPD, obtained from drive-test like mode, shows an excellent performance with an F 1-score of 99%, which is close to the reference result found in literature [START_REF] Ray | Localization of LTE measurement records with missing information[END_REF]. However, when the algorithm trained on EPD is used to perform IOD directly on crowdsourced data, a dramatic performance deterioration is observed as seen in table II. The best algorithm is SVM, which gives an F 1 -score of 61.7%. But, this is not an acceptable performance for IOD. In third case, the performance of supervised classifier where training as well as evaluation is performed on the crowdsourced labeled data, is shown only for the case of SVM, which performed best. Table III shows a noticeable enhancement of F 1 -score to 83.71%. This is a moderately acceptable performance. We are still far from the reference in the literature. For the target, we can assume that an error of 5-8% is tolerable for the IOD system. Indeed, while dimensioning of mobile networks, an error up to 10% is qualified as an admissible error rate. Additionally, Tables I, and III show that (RSRP, CQI) as input provides similar results as (RSRP, RSRQ) when used for classifying EPD or crowdsourcing data. The results are even slightly better in case of table II with (RSRP, CQI). RSRQ and SINR (note that CQI is based on SINR) are both radio measurements that depend on signal and interference strength. The results shows that the information contained in CQI is also useful for IOD and thus, (RSRP, CQI) is also a good candidate for IOD. trained only on EPD and then tested on the crowdsourced data drops in terms of F 1 -score and accuracy. This is due to the presence of ambiguous points combined with unknown environments not included in the drive-test data. Consequently, learning the user environment, only based on drive-test data, is thus not enough to learn the complexities of users' real life.

We continue the study with SVM as the reference supervised classifier. The inputs (RSRP, CQI) are more appropriate for doing IOD from network infrastructure point of view since these signals are sent more regularly to eNB than RSRQ. Lastly, we propose to add a new signal, called Timing Advance (TA), to enhance the IOD performance. The idea is to exploit the information of distance between eNB and the mobile users embedded in TA parameter. This would help the supervised classifier to classify the ambiguous points (e.g. measurement points with low RSRP, but close to eNB, etc.). So, in case of (RSRP, CQI, TA), the SVM performance reaches an F 1 -score of 89.11% and an Accuracy of 90.17% (Table III). As a result, with the addition of TA, IOD using SVM performs better leading to a gain of 6%. TA notably contributes to solve the classification issue of some ambiguous points.

V. HYBRID/SEMI-SUPERVISED APPROACH

To avoid performance degradation when facing new unknown environments, it is preferable to train the IOD classifier using a more diversified dataset. From a data collection point of view, it is more of an interest for the operator to collect massive partially tagged data. Indeed, first, during online labelling it alleviates the network charge by limiting the amount of UL signalling (all labels) sent to eNB and, second, reduces the complexity and the time for tagging data. Thus, the idea is to use the available tagged data, which is costly to obtain, and combine it with untagged data, which is easy to obtain, for classifier training. However, one of the main questions is: how much percentage of tagged data is needed for satisfactory performance of the intelligent IOD system?

We suggest a semi-supervised learning system (HSSL) that can learn additional new environments, without the need to involve more users to gather the ground truth (the indoor/outdoor tag). As in [START_REF] Radu | A semi-supervised learning approach for robust indoor-outdoor detection with smartphones[END_REF], [START_REF] Zhu | Introduction to semisupervised learning[END_REF], [START_REF] Zhang | Robust network traffic classification[END_REF], our approach uses both tagged and untagged data in order to improve the IOD classifier training, while maintaining the same good performances for a given ratio of tagged and untagged data. The proposed system is composed of 2 main modules as shown in Figure 6. The role of first module is to label the untagged data. It uses a unsupervised clustering algorithm, called "Bayesian Gaussian Mixture" (BGM) which is fast and efficient. The second module uses this tagged data output to learn the user environment via a supervised learning classifier that can be SVM or also Deep Learning algorithm.

Recently Deep Learning (DL) approaches have emerged which show improvements as compared to classical approaches such as SVM [START_REF] Lecun | Deep learning[END_REF], [START_REF] Goodfellow | Deep learning[END_REF]. Moreover, from an operator point of view, IOD is a complicated task since millions of users are considered during a longer period which heavily increases the dataset size. Therefore we propose to also investigate Deep Learning (DL) over the huge crowdsourced dataset. After configuring DL using a Grid Search to find the best parameters that best optimize the IOD classifier, we conduct a comparative study between SVM, HSSL (using SVM), DL and HSSL (using DL). In this approach, the first module detects 2 clusters. Once detected, they are employed to label the untagged data. An optimizer module then processes the data before sending it to the second module. It corrects and minimizes the labeling errors resulting from clustering. For this, we assume that a user can not change his environment twice in 30 seconds. The idea can be explained from the following example. Imagine that we have three consecutive points, very near in time, in the dataset. If, for example, the first point is mapped as indoor, the second is mapped as outdoor and the next point, very near in time, is again mapped as indoor, then we assume that there is an error in mapping. This is because a user cannot change its environment two times so quickly. Thus, the optimizer module detects and corrects such errors.

Let E t the environment type of the user at the moment t and the different measurement times t -1,t and t + 1. If the difference between (t -1,t + 1) is equal or less than 60s then E t-1 = E t = E t+1 . The optimizer parses the data tagged with the cluster verifying this assumption and then correcting the BGM prediction if necessary, see Algorithm

VI. RESULTS AND DISCUSSION

This section evaluates the performance of HSSL on the crowdsourced data. It provides an answer to the question on what is the ideal amount of tagged data required with the untagged data so that the performance of the intelligent IOD system achieves a F 1 -score higher than 90%.

We have used both scikit-learn [START_REF] Pedregosa | Scikit-learn: Machine learning in Python[END_REF] and keras [21] in python for the HSSL implementation. The DL module is a feed forward neuronal network (fully connected) with 8 hidden Layers using ReLU as the activation function. Actually, ReLU is the most widely used activation function while designing neural networks today. The main advantage of using ReLU over other activation functions is that it does not activate all the neurons at the same time. It leads to a sparse network that is efficient and easy for computation. As for the last layer (the output layer) we used a sigmoid activation function since we look for a binary classification either 0 or 1 (for indoor/outdoor environments).

The HSSL evaluation is first done in 2 validation steps:

• (i) The performance of first module (BGM + Optimizer) provides an F 1 -score of 85.99%. • (ii) The performance of second module (supervised learning using SVM or DL) shows an F 1 -score of 89.11% with SVM and of 92.81% with DL. Once confirmed that both modules have convincing performance, we evaluate the whole HSSL system. The system receives both labeled and unlabeled data as inputs. The evaluation goal is to find out for what percentage of labeled data (and unlabeled data), the performance of HSSL goes above the target F 1 -score of 90%. For this, we aim to compare HSSL (including SVM or DL) with SVM and DL, alone, when trained over same amount of tagged data (with the only difference that HSSL in addition also uses untagged data). Figure 7 shows performance of HSSL (DL or SVM) and of supervised SVM and DL for different percentages of labeled data. We observe that IOD performs better using DL than using SVM in both cases. We also observe that only DL and HSSL(DL) achieve both the tolerable error of 5 -8% for IOD system in mobile network. However, we note that HSSL is slightly better than DL for almost all percentages of labeled data. HSSL(DL) reach the maximal F 1 -score of 93% for the distribution of 65% labeled data and 35% unlabeled data.

Also, it can be seen that for an operator a dataset composed only of 10% of tagged data, approx. 1 month of collected data out of total 9 months, is enough to learn the user environment.

To conclude, the proposed HSSL system trained with a partially tagged data set, is able to make a good distinction of the user environment. We also note that supervised DL is better than SVM. This is because DL uses several layers of neurons and is able to capture more mappings. HSSL(DL) showed only slightly better performance as compared to DL. This is because we studied simple IOD with detection of only 2 classes. In future, we plan to study detection of more environments such as in-car, pedestrian, etc., instead of just outdoor or indoor. We will compare HSSL(DL) with DL with more classes, environments and yet more users and data. The hypothesis will be that adding unlabeled data might improve the HSSL performance more. Nevertheless, HSSL with SVM trains faster with a duration of 32.95s on a machine having 12 CPUs and 32 Go of RAM. Training with DL is slower: about 23.35 minutes using the same machine. SVM and HSSL with SVM converge quicker than DL. In future, we will try training using a GPU.

VII. CONCLUSION

In this paper, we investigated the problem of IOD performed at network side using 3GPP signals and Timing Advance data collected inside the infrastructure. We first showed that using a drive test dataset is insufficient to mimic the real world complexity and reveal the real user behavior. By diversifying the environments more (using a highly representative crowdsourced dataset) during the training phase, we showed that the more environments we have for the training phase, the better the supervised classifier performs. We also showed that adding a new parameter, Timing Advance, can improve IOD performance.

To address the fundamental issue of the model adaptation to new and diversified environments without making it hard and expensive for the operators (specially due to the labelling task) we proposed a new hybrid/semi supervised learning (HSSL) system. The HSSL system presents satisfactory performance even when facing unknown environments.

We plan to extend our work on IOD in future and address the IOD issue by considering systems that take the time variations into account. Thus, probably using other algorithms with time correlation, like LSTM, would boost the HSSL system and would probably decrease the required portion of labeled data to obtain F 1 -score of 95%.

Fig. 1 .

 1 Fig. 1. Example of IOD classification scheme: in 3 main classes

Fig. 2 .

 2 Fig. 2. Data collection Points in France: multiple environments and places

Fig. 3 .

 3 Fig. 3. Empirical CDF for measured RSRP (left) and CQI (right) in crowdsourcing mode: multiple environments and places -Indoor (red) and Outddor (green).

Fig. 4 .

 4 Fig. 4. Empirical CDF for measured RSRP (left) and CQI (right) in drivetest type mode: specific environments and places -Indoor (red) and Outddor (green).

Fig. 5 .F 1 -

 51 Fig. 5. The Data collection Points of EPD in drive-test like mode: Paris and southern suburbs

Fig. 6 .

 6 Fig. 6. IOD Learning Scheme for Weakly Labelled data: an hybrid/Semisupervised Machine Learning approach

Fig. 7 .

 7 Fig. 7. Evaluation of the HSSL System: F 1 -score vs. percent of labeled data.

  1. The clustering

	Data: output of the cluster : tagged data
	Result: Optimization and tags correction
	for E t in clustering Tagged DataSet do
	if Diff(t -1,t + 1)≤ 60s then
	if E t-1 = E t+1 And E t-1 = E t then
	E t ← E t+1
	end
	end
	end
	Algorithm 1: Time Optimizer
	and the optimizer together form the first module of the HSSL
	system which deals with labeling of the unknown data tags.
	The input of first module consists of untagged data. The output
	is considered as the first input of the second module dealing
	with the supervised classifier. This output is a vector of 4

measurements [RSRP, CQI, TA, Class*], where Class* is the estimated labels by the first module. The second input of the second module is composed of the labels (the ground truth) forming a measurement vector of size 4 [RSRP, CQI, TA, Class].

ACKNOWLEDGMENT

We would like to thank Xavier Lagrange and Jean-Marie Bonin of INRIA for insightful discussions on IOD issue and user behavior contextualization. We would like also to thank Jakob Hoydis of NOKIA Bell Labs for many helpful discussions in machine learning and Deep Learning topic.


Hamza Hallak Elwan

Ramin Khayatzadeh

Julien Poette

Beatrice Cabon

Impact of Relative Intensity Noise on 60 GHz Radio-over-Fiber Wireless Transmission Systems

This paper investigates a 60 GHz radio-over-fiber (RoF) communication system employing two different techniques to generate millimeter-wave (mm-wave) signals. The relative intensity noise (RIN) transferred during optical heterodyning of mm-wave signal is theoretically studied and experimentally investigated. Laser RIN induces noise at resultant electrical mmwave signal and is directly generated from initial RIN at low frequency. Therefore, RIN impairs the performance of the RoF mm-wave system. The model of RIN is also presented and is in very close agreement with the experiment results. Furthermore, wireless transmission experiments to demonstrate the intensity noise effect are carried out and are compliant with the standards at mm-wave. Wireless transmission up to 3 m can be achieved using a transmit power of +4.5 dBm. Index Terms-Error vector magnitude (EVM), distributed feedback (DFB) laser, millimeter-wave (mm-wave), passively mode locked laser diode (PMLLD), phase noise, radio-over-fiber (RoF), relative intensity noise (RIN).

I. INTRODUCTION

F UTURE wireless applications demand a large sufficient bandwidth to satisfy greater data rates [START_REF] Kleine-Ostmann | A review on terahertz communications research[END_REF]. Millimeterwave (mm-wave) frequency band and beyond has been proposed as a solution to overcome the saturation of spectral resources [START_REF] Guo | 60 GHz millimeterwave radio: principle, technology and new results[END_REF]. The unlicensed frequency band of 7 GHz from 57 to 64 GHz is allocated for wireless communications which rapidly expands 60 GHz technology for ultra-high speed systems [START_REF] Yong | 60 GHz technology for Gbps WLAN and WPAN: from theory to practice[END_REF]. The huge attenuation of the atmosphere and the electronic challenge of solid state signal source in this continuous frequency band emerge wireless propagation difficulties [START_REF] Smulders | Exploiting the 60 GHz band for local wireless multimedia access: prospects and future directions[END_REF]- [START_REF] Mann | Practical challenges for the commercialization of terahertz electronics[END_REF]. Therefore, microwave photonic technologies have intensely been proposed for mm-wave signal generation, and radio-over-fiber (RoF) has been utilized to distribute mmwave signals [START_REF] Jia | Key enabling technologies for optical-wireless networks: opticzl millimeter-wave generation, wavelength reuse, and architecture[END_REF], [START_REF] Stohr | 60 GHz radio-over-fiber technologies for broadband wireless services[END_REF]. The optical signals carrying data are transmitted from the central station (CS) to the base station (BS) by an optical fiber. Optical modes can be mixed on a high-speed photodiode (PD) at the BS to generate mm-wave signal, and heterodyning advantage is that mode beating avoids any external electrical oscillator [START_REF] Brendel | Low-cost analog fiber optic links for in-house distribution of millimeter-wave signals[END_REF], for example by beating two independent running lasers [START_REF] Stohr | 60 GHz radio-over-fiber technologies for broadband wireless services[END_REF]. In this approach, the resultant signal exhibits a large phase noise induced by laser linewidth, wavelength fluctuations and mode correlation [START_REF] Johansson | Generation and transmission of millimeter-wave data modulated optical signals using an optical injection phase-lock loop[END_REF]. Then, monolithic dual distributed feedback (DFB) lasers are developed for obtaining a narrow linewidth beat note with reduced phase noise [START_REF] Van Dijk | Monolithic dual wavelength DFB lasers for narrow linewidth heterodyne beat-note generation[END_REF]. Another approach uses quantum

The authors are with Institut de Microélectronique Electromagnétisme et Photonique -LAboratoire d'Hyperfréquences et de Caractérisation, IMEP-LAHC, Minatec, 3 Parvis Louis Néel CS 50257 38016 Grenoble Cedex 1, France e-mail: (hamza.hallak-elwan@imep.grenoble-inp.fr). dash mode-locked laser (QD-MLL) to demonstrate mm-wave beat note linewidth around 100 KHz [START_REF] Brendel | Low-cost analog fiber optic links for in-house distribution of millimeter-wave signals[END_REF].

Relative intensity noise (RIN) of laser is an indicator of the laser intensity stability and is usually measured from DC to some GHz in [START_REF] Poette | Highly-sensitive measurement technique of relative intensity noise and laser characterization[END_REF]. For mm-wave RoF communication systems, RIN has been explored in [START_REF] Hallak Elwan | Relative intensity noise in optical heterodyning applied to millimeter wave systems[END_REF], and the impact of intensity noise on generated mm-wave signals has be observed on error vector magnitude (EVM) [START_REF] Khayatzadeh | Impact of phase noise in 60 GHz radio-over-fiber communication system based on passively mode locked laser[END_REF]- [START_REF] Khayatzedeh | 100 GHz RoF System Based on Two Free Running Lasers and Noncoherent Receiver[END_REF]. The photonic mm-wave generation with low phase noise is used by optical carrier suppression (OCS) or optical modulator, but the high insertion loss is experienced and the large RF drive power is required. Thus, in this paper, we demonstrate the RIN effect close to the mm-wave carrier using two techniques for optical heterodyne generation with low and high phase noise values of mm-wave signal. The Erbium-doped fiber amplifier (EDFA) is not implemented to avoid the amplified spontaneous emission (ASE) noise. The first technique uses two independent distributed feedback (DFB) lasers without any locking scheme which is the simplest and cheapest way. In this case, phase noise is larger than in other solutions and its measurement is presented using the technique in [START_REF] Khayatzadeh | Accurate millimeter-wave laser phase noise measurement technique[END_REF] to de-correlate RIN and phase noise impacts on resultant mm-wave signal. The second technique is based on a passively mode-locked laser diode (PMLLD) at 60.64 GHz to generate mm-wave signal with advantage of lower phase noise due to the correlation between optical modes of the PMLLD. At the receiver-end, a coherent receiver using a mixer is used for electrical down-conversion. Both theoretical and experimental studies confirm that the RIN phenomenon in mm-wave frequency band is generated from the initial RIN at low frequency. It is worth mentioning that the results of the presented study are applicable to any kind of optical frequency generation. Depending on the heterodyne process, it is shown that RIN at mm-wave signal can be larger than the initial RIN at low frequency and causes spectral degradation in optical heterodyne signals.

The results presented in [START_REF] Hallak Elwan | Relative intensity noise in optical heterodyning applied to millimeter wave systems[END_REF] are extended to examine the impact of intensity noise on EVM for 60 GHz RoF wireless system using 60 GHz antennas and envelop detector (ED) as incoherent receiver. Data rates of 397 Mbps and 794 Mbps with binary phase shifting keying (BPSK) modulation format are applied as well as a data rate of 1588 Mbps with quadrature phase shifting keying (QPSK) modulation format to comply with communication standards at 60 GHz [18], [19].

This paper is organized as follows: section II investigates the theory and experimental setup of RIN. In section III, experimental measurements of RIN are analyzed in [START_REF] Brendel | Low-cost analog fiber optic links for in-house distribution of millimeter-wave signals[END_REF][START_REF] Johansson | Generation and transmission of millimeter-wave data modulated optical signals using an optical injection phase-lock loop[END_REF][START_REF] Van Dijk | Monolithic dual wavelength DFB lasers for narrow linewidth heterodyne beat-note generation[END_REF][START_REF] Poette | Highly-sensitive measurement technique of relative intensity noise and laser characterization[END_REF][START_REF] Hallak Elwan | Relative intensity noise in optical heterodyning applied to millimeter wave systems[END_REF][START_REF] Khayatzadeh | Impact of phase noise in 60 GHz radio-over-fiber communication system based on passively mode locked laser[END_REF][START_REF] Khayatzadeh | Impact of amplitude noise in millimeter-wave radio-over-fiber systems[END_REF][START_REF] Khayatzedeh | 100 GHz RoF System Based on Two Free Running Lasers and Noncoherent Receiver[END_REF][START_REF] Khayatzadeh | Accurate millimeter-wave laser phase noise measurement technique[END_REF][18][19] GHz] and in [50-62] GHz frequency ranges. A 60 GHz RoF wireless system is discussed in section IV, and finally the conclusion is presented in section V.

II. THEORETICAL INVESTIGATION AND EXPERIMENTAL SETUP OF RIN

Generating mm-wave signal by optical heterodyning requires at least two optical signals. The optical field E i (t) can be expressed as:

E i (t) = A i 1 + δ i (t) exp j 2πf i t + φ i (t) (1)
where A i , δ i , f i , and φ i are the amplitude, amplitude noise, frequency, and phase noise of the optical field of mode (i), respectively. For each laser, RIN expression was described in [START_REF] Govind | Fiber-optic communication systems[END_REF], and for a standard semiconductor laser, the generic expression of RIN that can be extracted from laser rate equations is:

RIN i (ω) = |δ opt (ω)| 2 P opt 2 = P SD(ω) P elec = 4δ 2 i (ω) (2)
where δ opt (ω) is the optical power variation spectrum, P opt is the optical power, PSD is the power spectral density of the intensity noise, P elec is DC electrical power, and δ i (ω) = F F T (δ i (t)).

A. RIN Generated by Two DFB Lasers

The generic schematic diagram of experimental setup for two separate optical sources (first technique) is shown in Fig. 1. Two independent DFB lasers, DFB 1 and DFB 2 operating in the C-band, are used to generate a beat note carrier which is adjusted and controlled by the temperature and the bias current of the two lasers. The polarization controller (PC) is utilized for optimizing the beating power, and a (50/50) coupler is used to mix the two optical signals on a PD of 70 GHz bandwidth. The combined signal after a coupler is transmitted through a conventional single mode optical fiber of 1.5 m length to a high-speed PD at the receiver-end. In this case, the beating results from only two modes which are not phase locked, and consequently the fiber length will just impact on the received optical power. The photodetected current I P D (t) is expressed as:

I P D (t) = (I 1 + I 2 ) + I 1 RIN 1 (t) + I 2 RIN 2 (t) + 2 I 1 I 2 × cos 2π(f 2 -f 1 )t + φ 2 (t) -φ 1 (t) + I 1 I 2 RIN 1 (t) + RIN 2 (t) × cos 2π(f 2 -f 1 )t + φ 2 (t) -φ 1 (t) (3)
where I i is the DC photodetected current of the optical signal (i, i = 1 or 2), and RIN i is relative intensity noise RIN (t) = F F T -1 (RIN (ω)). Second order terms related to noise-to-noise beatings have been neglected.

The first term of (3) represents the total average current, the second term represents the noise current of two optical signals from DC to some GHz corresponding to the initial relative intensity noise (RIN ini ), the third term is the beat note current created during their simultaneous detection where the beat frequency is equal to difference between frequencies of the two optical modes (f 2 -f 1 ), and the last term represents noise current of the two optical signals corresponding to RIN close to beat note (RIN beat ). The frequency (f 2 -f 1 ) exhibits a phase noise (φ 2 (t) -φ 1 (t)) which amplitude varies due to the correlation between these modes, that depends on how the two optical modes are produced. In the last term of (3), it can be noticed that RIN amplitude at beat note depends on the product of modes amplitudes in opposition to RIN amplitude at low frequency represented by the second term. This nonlinear process at beat note could lead to an increase of intensity noise during heterodyne process with respect to initial intensity noise at low frequency. This increase is noticeable especially when the optical powers of the two lasers are different.

After substituting (3) in (2), and as there is no correlation between modes when considering two independent DFB lasers, the auto-correlation function of RIN 1 (t) and RIN 2 (t) is equal to 0. Therefore, the (RIN ini ) at low frequency and (RIN beat ) close to the beat signal are then obtained as follows:

RIN ini (f ) = I 2 1 RIN 1 (f ) + I 2 2 RIN 2 (f ) (I 1 + I 2 ) 2 (4) RIN beat (f ) = I 1 I 2 RIN 1 (f ) + RIN 2 (f ) (I 1 + I 2 ) 2 (5)
As can be seen in the last term of (3), the product of the mode intensity noise and the carrier in the time domain leads to the intensity noise conversion in the frequency domain directly to the beat frequency. In case the optical signals have the same amplitudes (I 1 = I 2 ), then RIN ini = RIN beat . While a power difference exists, for example (I 2 > I 1 ), the RIN i of the less powerful mode (RIN 1 ) in this case, is increased by a factor I 2 /I 1 in (5) as compared to RIN i in (4), and thus, RIN beat is larger than RIN ini by a factor I 2 /I 1 . This phenomenon has not been published by other authors to the best of our knowledge and is evidenced in section III with both experimental and model results.

For more accurate recognition between RIN ini and RIN beat , and for avoiding down-conversion stage, we first present results in [9 kHz-19 GHz] frequency band. For measuring RIN in [9 KHz-19 GHz] frequency range, path 1 of Fig. 1 is used. After PD, a bias tee is added to suppress the DC component and to extract the photocurrent value for RIN determination. A 19 GHz low noise amplifier (LNA) having a 40 dB gain is employed. For analyzing RIN in the mm-wave frequency band, path 2 is implemented, and therefore the mm-wave signal is amplified using a 35 dB gain LNA of (50-62) GHz bandwidth. Then, the amplified signal is down-converted by using a mixer (50-65) GHz of 6 dB losses and a local oscillator (LO). Finally, an electrical spectrum analyzer (ESA) of 21 GHz bandwidth is monitored to extract power spectral density (PSD) of measured RIN.

B. RIN Generated by PMLLD

The optical field E(t) of any multi-mode laser i.e., PMLLD, possessing (M) modes equally spaced can be defined as:

E(t) = M i=1 A i 1+δ i (t) exp j 2π f 0 +if r )t+φ i (t) (6)
where A i , δ i , and φ i are the amplitude, amplitude noise, and phase noise of i th mode, respectively. f 0 and f RF are the frequency of the first mode and the beat note frequency which corresponds to the free spectral range (FSR). Fig. 1 shows the generic schematic diagram of experimental setup based on PMLLD (second technique) for two frequency bands. In some cases, the sensitivity of the system can be improved using a lock-in amplifier to measure the intensity noise [START_REF] Poette | Highly-sensitive measurement technique of relative intensity noise and laser characterization[END_REF].

A digital signal generator a Mach-Zehnder modulator (MZM), and a synchronization is necessary between ESA and lock-in amplifier. The output current of the PD I P D (t) can be described as:

I P D (t) = M i=1 I i + M i=1 I i RIN i (t) + 2
i,j:i =j

I i I j × cos 2π(f j -f i )t + φ j (t)- φ i (t) + i,j:i =j I i I j RIN i (t) + RIN j (t) × cos 2π(f j -f i )t + φ j (t) -φ i (t) (7)
The RIN ini at low frequency and RIN beat at fundamental beat note can be expressed as:

RIN ini (f ) =     M i=1 I i RIN i (f ) M i=1 I i     2 (8) RIN beat (f ) =     M -1 i=1 √ IiIi+1 √ RINi(f )+ √ RINi+1(f ) M i=1 Ii     2 (9)
All beating noise expressed in the last term of (7) which are not used in [START_REF] Brendel | Low-cost analog fiber optic links for in-house distribution of millimeter-wave signals[END_REF] contribute on noise at other frequencies corresponding to harmonics of the beat note. Equations ( 4) and ( 5) can be seen as specific cases of equations ( 8) and (9) when only 2 modes are considered. Terms of ( 7) for (M) modes have the same expressions as terms in (3) for two modes. RIN expressions of ( 8) and ( 9) are also the same as (4) and ( 5) respectively. The main and important difference between the two mm-wave generation techniques cases is the correlation between modes is not considered null for the case using PMLLD.

III. EXPERIMENTAL MEASUREMENTS OF RIN

The RIN results are here measured in [9 KHz-19 GHz] frequency band as well as in [50-62] GHz.

A. Experimental Results in [9 KHz-19 GHz] Frequency Band

The fundamental features of the technique using two different DFB lasers (Fig. 1 For verifying (4) and ( 5), the bias current of the first laser DFB 1 is set to 15 mA (P opt = -2.1 dBm) while the bias current of the second laser DFB 2 is 90 mA (P opt = +3.5 dBm). Thus, RIN 1 and RIN 2 levels are very different for the first and second laser, respectively. The experimental results of initial RIN ini and beat note RIN beat are given in curve (a) of Fig. 2. On these results, the thermal noise impact, the shot noise and the frequency response of the global system have been removed. In Fig. 2, the initial RIN ini from 9 KHz to 7 GHz and RIN close to the generated beat note RIN beat on each side of the heterodyne signal can be observed. The large phase noise obtained by this technique is provided in curve (b) to prove that impairments are not due to phase noise but come from laser intensity noise solely. The beat note linewidth is estimated to be 30 MHz in this case. From ( 4) and ( 5), the difference between the maximal values of initial noise RIN ini and noise close to the beat note RIN beat is calculated to be equal to 6.7 dB. From experimental measurement of Fig. 2, it can also be observed that the beat note RIN beat is higher than the initial RIN ini by a factor I 2 /I 1 = 6 dB, which is in good agreement with the predicted value of 6.7 dB. The RIN of less powerful mode (RIN 1 ) in ( 5) is then increased by a factor I 2 /I 1 compared to RIN 1 in (4) as expected.

The initial RIN ini has been modeled, curve (1) of Fig. 2, from RIN expression of laser [START_REF] Govind | Fiber-optic communication systems[END_REF]. The phase noise of beat signal corresponds to the convolution of the two optical individual spectrums. The noise shape can be modeled by a Lorentz distribution while other random variations at the wavelength difference are considered as Gaussian fluctuations, resulting in a Voigt profile (curve ( 2)). The beat note RIN beat model in curve (3) refers to the last term in (3), representing the phase noise of the beating signal multiplied in the time domain by the sum of the individual laser intensity noise. Then, the model results of beat signal phase noise and RIN beat are summed and shown in curve (4). Based on the aforementioned, experimental results are well confirmed by model results and give an evidence of RIN generation from heterodyne process at beat note, as presented in (3), (4), and ( 5).

In Fig. 3, we intend to show that RIN beat impairs mm-wave signal, even at high frequency. Since the relative intensity noise (RIN 1 ) of DFB 1 has the main contribution on the initial RIN ini , two different bias currents are set for DFB 1 : 13 mA (P opt = -4.1 dBm) and 17 mA (P opt = -0.6 dBm), respectively. The second laser DFB 2 is still biased at 90 mA (P opt = +3.5 dBm). The temperature of both lasers is set to keep the beat note at 14 GHz, and results are shown in Fig. 3. When the laser DFB 1 is biased at 13 mA, the laser relaxation frequency is equal to 3.3 GHz that can be seen on the curve (1) of Fig. 3. During the heterodyne process, it can be noted that RIN beat close to the beating frequency exhibits a maximum noise at an offset from the beat note equal to the relaxation frequency. When changing the laser bias to 17 mA, the relaxation frequency is equal to 4.6 GHz (curve (2)), corresponding to the offset between the beat note and the maximum RIN beat . In this case, the SNR is increased, but if the Erbium-doped fiber amplifier (EDFA) is implemented to increase the optical power, the SNR is still fixed as in Eq. ( 23) in [START_REF] Khayatzadeh | Impact of amplitude noise in millimeter-wave radio-over-fiber systems[END_REF].

From this figure, it is inferred that RIN beat close to beat signal is directly generated from initial RIN ini and has the same profile. The difference between initial RIN ini and RIN beat at beat note for the above two cases is also observed because the lowest powered mode will be increased through the heterodyne process by I 2 /I 1 . Fig. 3 depicts different RIN ini values while keeping identical phase noise for both measurements. Then, the results clearly indicate that the beat signal for offset frequency higher than 1 GHz is largely impacted by RIN beat which spread over a very large bandwidth, much higher than phase noise. This further confirms that RIN phenomenon at generated beat note is distinct from phase noise of heterodyne signal.

B. Experimental Results in [50-62] GHz Frequency Band

This subsection concerns optical mm-wave generation using 2 DFBs and PMLLD. The schematic diagram of the experimental setup is described in path 2 of Fig. 1. A mixer and an LO frequency fixed at 51.2 GHz are employed for downconversion, to adjust received signals to the ESA bandwidth. The mixing process does not influence the quality of the involved signals as the LO phase noise is much lower than the signal to be characterized (-140 dBc/Hz for frequency offset above 10 MHz) [START_REF] Agilent | D PSG Microwave Analog Signal Generator Datasheet[END_REF].

In the first technique using 2 DFBs for mm-wave generation, the temperature of lasers is controlled to stabilize the signal at 55 GHz, and the bias current of DFB 1 is 13 mA (P opt = -4.1 dBm), while the bias current of DFB 2 is set to 90 mA (P opt = +3.5 dBm). Fig. 4 shows the experimental and model results, illustrating RIN beat close to beat note (curve (a)) and also phase noise (curve (b)) at mm-wave frequency. The models of phase noise, and RIN beat in curves ( 1) and ( 2), respectively, are in very good agreement with the experimental results. The RIN beat in Fig. 4 is comparably similar to the result of RIN beat without down-conversion stage in Fig. 3 for bias current of 13 mA. The factor I 2 /I 1 being larger than 1 as described in ( 4) and ( 5), consequently RIN beat close to the beat frequency is higher than RIN ini at low frequency.

In the second technique using PMLLD for mm-wave generation, PMLLD having an FSR of 60.64 GHz is biased at 180 mA (P opt = +9.5 dBm), and the temperature is fixed at 25 • C. Here, the lock-in amplifier allows measurements of a lower RIN level. Fig. 5 represents the beat note in [50-62] GHz range before down-conversion and only depicts one side band RIN beat of mm-wave signal because of the frequency cutoff at 62 GHz. According to the correlation between optical modes of PMLLD, the beat note linewidth of mm-wave signal is ≈ 1 MHz, which is much lower than the first technique using 2 DFBs. The initial RIN ini at low frequency and its model are presented in the black curve and thick grey curve in the left insert of Fig. 5, respectively. It should be mentioned that Fig. 5. shows an estimation of the RIN beat (black curve) at mm-wave based on low frequency measurements and using the presented model. The peak value of beat note RIN beat is -150 dB/Hz at 50 GHz which is the same as RIN ini peak measured at the low frequency. This can be explained by the distribution of optical power between (M) modes which are equal, thus the factor I j /I i equals 1 as described in ( 8) and ( 9). The relaxation frequency extracted from RIN ini and RIN beat is approximately 10 GHz, so it can be confirmed that RIN beat is generated from RIN ini . When comparing the first and second techniques, both based on same physical phenomenon, i.e. heterodyning, RIN beat close to beat signal has the same origin. Since the fiber length is increased, chromatic dispersion will induce a time delay between different modes, and this can be interpreted by a de-correlation between optical modes. Consequently the noise induced by RIN will also be de-correlated. Therefore, this delay degrades the compensation of intensity noise which can not fully be compensated when detecting several modes. The frequency limit at which the compensation is possible depends on the fiber length, and the wavelength difference between beatings and the number of beatings should also be considered.

IV. 60 GHZ ROF WIRELESS TRANSMISSION In this section, we have studied the impact of intensity noise on broadband 60 GHz transmission complying with 60 GHz communication standards.

A. 60 GHz RoF System Setup

The experimental setup presented in Fig. 6 to perform a transmission of digital modulated data on a 60 GHz carrier in order to analyze the intensity noise impact on EVM. The central station employs the previous mentioned optical sources, 2 DFB lasers or PMLLD. A PC is added to match the polarization state of the modulator waveguide. A MZM is used as an external modulator biased at the quadrature point for applying data in compliance with standard modulation formats. Signals come from an arbitrary waveform generator (AWG), and the variable optical attenuator is employed to vary the optical power transmitted to a conventional single mode optical fiber of 1.5 meter. At the base station, the 60 GHz signal is produced by optical heterodyning on a high-speed PD and then amplified to a maximum level of +4.5 dBm using a 35 dB RF amplifier. Then, the wireless link is implemented by 60 GHz standard horn antennas of 20 dBi gain. A 30 dB gain broadband amplifier is used at the receiver to compensate the free space losses. The data-modulated 60 GHz signal is then recovered using ED as down-conversion stage. Since the ED only detects field intensity, the noise measured is due to the intensity noise [START_REF] Khayatzadeh | Impact of phase noise in 60 GHz radio-over-fiber communication system based on passively mode locked laser[END_REF], [START_REF] Khayatzadeh | Impact of amplitude noise in millimeter-wave radio-over-fiber systems[END_REF]. The ED does not require any LO nor locking scheme which makes this receiver cost-efficient. The down-converted signal and data are transmitted to a high-speed digital sampling oscilloscope (DSO) 54855A from Agilent, having a 6 GHz bandwidth for sampling process. On-line EVM values are obtained using vector signal analyzer (VSA) installed on the DSO.

B. Experimental Results

The EVM measurements are carried out with achieving 60 GHz communication standards for a wireless distance of 25 cm where the free space power loss is approximately 16.5 dB. This distance can be increased by using a higher transmit power and high-gain antennas. According to a bandwidth limitation of 2 GHz of the Schottky diode used, data using BPSK 397 Mbps, BPSK 794 Mbps, and QPSK 1588 Mbps modulations are applied on a 1 GHz subcarrier at 60 GHz.

For BPSK modulation using 2 DFBs or PMLLD, EVM characterizations have been performed as a function of the received RF power. BPSK 397 and 794 Mbps data rates can be shown in Fig. 7 and Fig. 8, respectively, and constellation diagrams are presented as well in inserts. The received RF power was varied by adjusting the optical power into the PD using the optical attenuator. In Fig. 7, EVM values at bit rate of 397 Mbps are as low as 10.8 % for 2 DFBs (circle points) and 11.8 % for PMLLD (square points) for the same received RF power of -20.6 dBm. The EVM value of the lowest received RF power within standard limits is defined by the receiver sensitivity. In this experiment, the values of the receiver sensitivity for 33.4 % standard limit are -42.7 dBm and -41.2 dBm for 2 DFBs and PMLLD, respectively. The wireless distance between the two antennas at these receiver sensitivity values can be extended to approximately 3 m without any additional amplifier. As can be extracted from the constellation diagrams, the error-floors of EVM are due to the impact of intensity noise. Furthermore, the PSD measurements of the intensity noise in [START_REF] Khayatzadeh | Impact of amplitude noise in millimeter-wave radio-over-fiber systems[END_REF] show that the intensity noise of PMLLD is higher than 2 DFB lasers, and therefore the EVM value with PMLLD is higher than that of 2 DFBs at the same received RF power.

The EVM for a transmission of BPSK signal at data rate of 794 Mbps is illustrated in Fig. 8. The lowest EVM values achieved are 14.2 % and 15.8 % for 2 DFBs (circle points) and PMLLD (square points)at the same received RF power of -15.3 dBm, respectively. The receiver sensitivity is compliant with the standard limit of 23.7 % at -29.7 dBm for 2 DFBs and -28.3 dBm for PMLLD, while the maximum distance between two antennas at these levels of sensitivity is approximately 1.5 m, with avoiding the use of additional amplifiers. Here, since the signal bandwidth is increased, the error-floors are higher than in the case using BPSK data rate of 397 Mbps. For a QPSK signal of 1588 Mbps data rate applied on the transmitter side, Fig. 9 shows the EVM trends versus received RF power. For 2 DFBs (circle points), EVM as low as 14.5 % is obtained at the received RF power of -15.1 dBm whereas for PMLLD (square points), EVM increases to 15.9 % at the same received RF power. For the standard limit of 23.1 %, the receiver sensitivity is -29.3 dBm and -27.3 dBm for 2 DFBs and PMLLD, respectively. At these values of receiver sensitivity, the transmission distance can be extended to approximately 1.5 m with no other amplifier implemented. The constellation diagrams of QPSK modulation show that there is no phase noise impact measurable, and thus the EVM error-floors are induced by the intensity noise solely.

Finally, for reflecting the ambiguity noise in the system, BER can be calculated in [reference], and after the calculations are performed, the phase ambiguity does not influence in the system. The previous three modulation format used match the requirements of the communication standards for 60 GHz RoF wireless transmission, using 2 DFBs or PMLLD.

V. CONCLUSION

This paper analyzes the RIN of optical heterodyne generated signals for mm-wave RoF applications and systems. A complete theoretical study and experimental measurements have been presented using two different techniques, based on two independent DFB lasers and PMLLD, where both coherent and incoherent receivers have been used. The generation of intensity noise and its impact close to beat note at mm-wave frequency have been demonstrated and investigated, while the phase noise results are provided as well for comparison. For measuring the real RIN beat contribution, the frequency response of the system has been measured and removed. The experimental results confirm that RIN beat close to the mmwave carrier is directly generated from the initial RIN ini at low frequency whatever the photonic generation process is and is clearly distinct from phase noise of the beat signal. It is also shown that, due to heterodyning, the RIN beat level can be higher than the RIN ini by a factor equal to the ratio between mode power. The models of classic definition of RIN laser RIN ini and RIN generated close to the beat note RIN beat are presented and agree very well with the experimental measurements. These results are independent from how the optical modes are generated, so they can directly be transposed to any kind of optical process for beat note frequency generation.

The EVM results have been demonstrated using the 60 GHz wireless transmission system to meet the 60 GHz communication standards, and the impairment of intensity noise is examined on the RoF performance.

Fig. 1 .

 1 Fig. 1. Experimental setup for optical heterodyning based on two independent lasers or PMLLD in [9 KHz-19 GHz] and [50-62] GHz.

  -path 1) are the ability to generate beat signals at different frequencies and the capability to demonstrate several cases of RIN close to beat note by varying the temperature and the relative optical power of the DFB lasers. Due to the relaxation frequency of the lasers of 4.1 GHz and the 19 GHz bandwidth limitation of the amplifier, a beat signal at 14 GHz has been chosen to highlight the impact of RIN in optical heterodyne process as shown on Fig. 2.

Fig. 2 .

 2 Fig.2. RIN and phase noise of a heterodyne signal at 14 GHz for generation using two independent DFB lasers.

Fig. 3 .

 3 Fig. 3. RIN of the beat note at 14 GHz for two different bias currents of the first laser DFB 1 .

Fig. 4 .

 4 Fig. 4. RIN and phase noise of the heterodyne signal at 55 GHz with mmwave generation based on 2 DFBs.

Fig. 5 .

 5 Fig. 5. RIN of the heterodyne signal at 60.64 GHz with mm-wave generation based on PMLLD.

Fig. 6 .

 6 Fig. 6. Experimental setup for 60 GHz RoF wireless system based on two independent lasers or PMLLD.

Fig. 7 .

 7 Fig. 7. EVM of the 397 Mbps BPSK signal as a function of received RF power.

Fig. 8 .

 8 Fig. 8. EVM of the 397 and 794 Mbps BPSK signal as a function of received RF power.

Fig. 9 .

 9 Fig. 9. EVM of the 1588 Mbps QPSK signal as a function of received RF power.

ACKNOWLEDGMENT

The authors wish to thank Nicolas Corrao for his a valuable discussion, and we are thankful for the III-V lab, a joint laboratory of "Alcatel Lucent Bell Labs", "Thales Research & Technology", and "CEA-LETI", Palaiseau, France, for providing the PMLLD chip.