rational :

Most fundamental results in information theory and communication theory rely on applied probability. In the context of multi-user networks with new QoS metrics such as ultra reliable and low latency communications (URLLC), evaluating fundamental limits and properties of decentralized networks is an open problem. To make progress in this field, it is necessary to better understand some fundamental properties in applied probability.

In this context, we made progress in 2021 in three directions :

CDF of a sum of random vectors:

The calculation of cumulative distribution functions (CDFs) of sums of random vectors is omnipresent in the realm of information theory. For instance, the joint decoding error probability in multi-user channels often boils down to the calculation of CDFs of random vectors. In the case of the memoryless Gaussian multiple access channel, under certain conditions on the channel inputs, the dependence testing bound corresponds to the CDF of a sum of independent and identically distributed (IID) random vectors. Unfortunately, the calculation of CDFs of random vectors requires elaborated numerical methods which often lead to unknown errors. From this perspective, approximations to these CDFs, e.g., Gaussian approximations and saddlepoint approximations have gained remarkable popularity. In the case of Gaussian approximations, multi-dimensional Berry-Esseen-type theorems provide upper bounds on the approximation errors. These bounds are particularly precise around the mean. Alternatively saddlepoint approximations are known to be more precise than Gaussian approximations far apart from the mean. Unfortunately, this claim is often justified only by numerical analysis as formal upper bounds on the error induced by saddlepoint approximations are rather inexistent. The PhD of Dadja Anade, co-supervised by Jean-Marie Gorce, Philippe Mary (INSA Rennes) and Samir Perlaza (Inria, NEO, Sophia Antipolis), contributed in this direction by introducing a real-valued function that approximates the CDF of a finite sum of real-valued IID random vectors. Both Gaussian and saddlepoint approximations are shown to be special cases of the proposed approximation, which is referred to as the exponentially tilted Gaussian approximation. The approximation error is upper bounded and both upper and lower bounds on the CDF are obtained 33, 45, 48.

Dependence between random variables through extreme values:

In heavy-tailed data, such as data drawn from regularly varying models, extreme values can occur relatively often. As a consequence, in the context of hypothesis testing, extreme values can provide valuable information in identifying dependence between two data sets. In this paper, the error exponent of a dependence test is studied when only processed data recording whether or not the value of the data exceeds a given value is available. An asymptotic approximation of the error exponent is obtained, establishing a link with the upper tail dependence, which is a key quantity in extreme value theory. While the upper tail dependence has been well characterized for elliptically distributed models, much less is known in the non-elliptical setting. To this end, a family of nonelliptical distributions with regularly varying tails arising from shot noise is studied, and an analytical expression for the upper tail dependence derived 35.

Convergence guarantee of stochastic asynchronous optimization for a general class of objectives :

Asynchronous distributed algorithms are a popular way to reduce synchronization costs in large-scale optimization, and in particular for neural network training. However, for non-smooth and nonconvex objectives, few convergence guarantees exist beyond cases where closed-form proximal operator solutions are available. As training most popular deep neural networks corresponds to optimizing nonsmooth and nonconvex objectives, there is a pressing need for such convergence guarantees. In this work, we analyze for the first time the convergence of stochastic asynchronous optimization for this general class of objectives. In particular, we focus on stochastic subgradient methods allowing for block variable partitioning, where the shared model is asynchronously updated by concurrent processes. To this end, we use a probabilistic model which captures key features of real asynchronous scheduling between concurrent processes. Under this model, we establish convergence with probability one to an invariant set for stochastic subgradient methods with momentum. From a practical perspective, one issue with the family of algorithms that we consider is that they are not efficiently supported by machine learning frameworks, which mostly focus on distributed data-parallel strategies. To address this, we propose a new implementation strategy for shared-memory based training of deep neural networks for a partitioned but shared model in single- and multi-GPU settings. Based on this implementation, we achieve on average about 1.2x speed-up in comparison to state-of-the-art training methods for popular image classification tasks, without compromising accuracy 39.

Capacity for a channel with additive multivariate α-stable noise :

A wide range of communication systems are corrupted by non-Gaussian noise, ranging from wireless to power line. In some cases, including interference in uncoordinated OFDMbased wireless networks, the noise is both impulsive and multivariate. At present, little is known about the information capacity and corresponding optimal input distributions. In this paper, we derive upper and lower bounds of the information capacity by exploiting non-isotropic inputs. For the special case of sub-Gaussian α-stable noise models, a numerical study reveals that isotropic Gaussian inputs can remain a viable choice, although the performance depends heavily on the dependence structure of the noise 26.

On second-order capacity of multi-delay constrained transmission of short packets :

A standard assumption in the design of ultra-reliable low-latency communication systems is that the duration between message arrivals is larger than the number of channel uses before the decoding deadline. Nevertheless, this assumption fails when messages rapidly arrive and reliability constraints require that the number of channel uses exceeds the time between arrivals. In this paper, we study channel coding in this setting by jointly encoding messages as they arrive while decoding the messages separately, allowing for heterogeneous decoding deadlines. For a scheme based on power sharing, we analyze the probability of error in the finite blocklength regime. We show that significant performance improvements can be obtained for short packets by using our scheme instead of standard approaches based on time sharing 41, 51.

Capacity of the massive access channel with a continuum approach :

Superposition coding (SC) has been known to be capacity achieving for the Gaussian memoryless broadcast channel for more than 30 years. However, SC regained interest in the context of non orthogonal multiple access (NOMA) in 5G. From an information theory point of view, SC is capacity achieving in the broadcast Gaussian channel, even when the number of users tends to infinity. But using SC has two drawbacks: decoders complexity increases drastically with the number of simultaneous receivers, and the latency is unbounded since SC is optimal only in the asymptotic regime. To evaluate these effects quantitatively in terms of fundamental limits, we introduce a finite time transmission constraint imposed at the base station and we evaluate fundamental trade-offs between the maximal number of superposed users, the coding block-length and the block error probability. The energy efficiency loss due to these constraints is evaluated analytically and by simulation. Orthogonal sharing appears to outperform SC for hard delay constraints (equivalent to short block-length) and in low spectral efficiency regime (below one bit per channel use). These results are obtained by the association of stochastic geometry and finite block-length information theory 29.

Topological interference management (TIM) in the intermediate SNR regime :

In the PhD of Hassan Kallam 46, we propose a new interference topology construction approach, for the TIM problem of wireless networks, that extends TIM to the non-asymptotic SNR regime. We introduce a new framework that is able to control the interference topology more accurately and with more flexibility. With this approach, we are able to cope with the finite SNR regime and to evaluate the impact of the interference threshold used to build the interference topology.

Capacity with mixed delay traffic:

We analyse the multiplexing gain (MG) achievable over Wyner’s symmetric network with random user activity and random arrival of mixed-delay traffic. The mixed-delay traffic is composed of delay-tolerant traffic and delay-sensitive traffic where only the former can benefit from transmitter and receiver cooperation since the latter is subject to stringent decoding delays. The total number of cooperation rounds at transmitter and receiver sides is limited to D rounds. We derive inner and outer bounds on the MG region. In the limit as $D\to \infty$, the bounds coincide and the results show that transmitting delay-sensitive messages does not cause any penalty on the sum MG. For finite D our bounds are still close and prove that the penalty caused by delay-sensitive transmissions is small 42.

rational :

Machine learning (ML) starts to be widely used to enhance the performance of wireless transmissions. However, it is still unclear if such methods are truly competitive with respect to conventional methods in realistic scenarios and under practical constraints. These constraints are adaptability, complexity, but also signal processing constraints such as PAPR (Peak to Amplitude Power Ratio) or ACLR (Adjacent Channel Leakage Ratio). Our studies are described below:

P2P Scenario - Learning OFDM Waveforms with PAPR and ACLR Constraints :

An attractive research direction for future communication systems is the design of new waveforms that can both support high throughputs and present advantageous signal characteristics. Although most modern systems use orthogonal frequency-division multiplexing (OFDM) for its efficient equalization, this waveform suffers from multiple limitations such as a high adjacent channel leakage ratio (ACLR) and high peak-to-average power ratio (PAPR). In this paper, we propose a learning-based method to design OFDM-based waveforms that satisfy selected constraints while maximizing an achievable information rate. To that aim, we model the transmitter and the receiver as convolutional neural networks (CNNs) that respectively implement a high-dimensional modulation scheme and perform the detection of the transmitted bits. This leads to an optimization problem that is solved using the augmented Lagrangian method. Evaluation results show that the end-to-end system is able to satisfy target PAPR and ACLR constraints and allows significant throughput gains compared to a tone reservation (TR) baseline. An additional advantage is that no dedicated pilots are needed 36, 49.

MU-MIMO Scenario - Learning receivers in OFDM Systems :

In addition to enabling accurate signal reconstruction on realistic channel models, MU-MIMO receive algorithms must allow for easy adaptation to a varying number of users without the need for retraining. In contrast to existing work, we propose an ML-enhanced MU-MIMO receiver that builds on top of a conventional linear minimum mean squared error (LMMSE) architecture. It preserves the interpretability and scalability of the LMMSE receiver, while improving its accuracy in two ways. First, convolutional neural networks (CNNs) are used to compute an approximation of the second-order statistics of the channel estimation error which are required for accurate equalization. Second, a CNN-based demapper jointly processes a large number of orthogonal frequency-division multiplexing (OFDM) symbols and subcarriers, which allows it to compute better log likelihood ratios (LLRs) by compensating for channel aging. The resulting architecture can be used in the up- and downlink and is trained in an end-to-end manner, removing the need for hard-to-get perfect channel state information (CSI) during the training phase. Simulation results demonstrate consistent performance improvements over the baseline which are especially pronounced in high mobility scenarios 30, 37.

NOMA-MIMO Scenario - Learning the decoding ordering of multiple users :

The user clustering problem in an uplink MIMO Non-Orthogonal Multiple Access (NOMA) scheme is considered here. The receiver is assumed to operate in two sequential stages that employ Linear Minimum Mean Squared Error (LMMSE) receivers. At the first stage, the receiver is designed to recover the transmission from a cluster of selected users/nodes. The contribution of these users is then subtracted from the received signal and the remaining user transmissions are then linearly recovered. The determination of which users should be detected during the first stage is formulated as a deep learning based multiple classification problem. In order to guarantee that the selection is robust to fast fading, the input to the neural network is based on second order channel statistics. Furthermore, the training process is simplified by using a large system approximation of the resulting sum-rates. Simulation results indicate that the proposed deep learning-based solution is able to achieve a significant rate advantage with respect to other lazy approaches, such as fixed or random cluster assignments 34

2-user MAC Scenario - Joint Learning of constellations :

Multiple access channel (MAC) is the theoretical basis for NOMA, that is a key technology for future cellular communications, expected to increase the number of devices served with a limited spectrum and to facilitate low latency communications. Yet the non-orthogonal operation leads to challenging interference conditions that require dedicated solutions, and this is particularly true for the constellations used to encode transmitted messages. The work developed in the last chapter of Cyrille Morin 's PhD uses deep learning to learn constellations tailored to the two-user MAC Gaussian channel, allowing for better performance than previously designed constellations, or the traditional orthogonal approach, stepping beyond the time-division multiple access (TDMA) capacity region. In the process, it also showcases the importance of performance comparison between orthogonal and non orthogonal approaches, and of studying the trade-offs between the two users, instead of focusing on aggregated metricsv47.

rational :

A classical setup in NOMA massive access for IoT is that the nodes may request to transmit a packet at any time. A pure random access may leed to collisions or interference, which can be taken into account by developping robust detection, distributed optimization strategies or efficient distributed coding . Further, reducing signaling is of primary interest since in IoT, information to be sent may be small. In this general configuration we contributed to five studies as described below: Note that this setup is also the reference scenario chose to explore the use of Quantum algoithms (see section 8.2)

Joint sensor identification and channel estimation in narrowband communication systems:

In this work, we develop algorithms for sensor identification and channel estimation in narrowband communication systems which is a necessary step in a standard NB-IoT protocol for instance. In our model we integrate a fault probability, which depends on physical variables and may be statistically correlated. The first step is to introduce a statistical model relating observations at the access point to the channel, activity of each sensor, and the probability each machine is faulty. Based on our new model, we derive an identification and channel estimation algorithm by exploiting GAMP by developing a loopy BP (LBP) algorithm for the model, and then applying GAMP for the variables associated with the communication channel. A key feature of the algorithm is that it explicitly accounts for uncertainty and correlation in the probability sensors are active, as opposed to existing approaches where the activity probability is fixed and sensor transmissions are uncorrelated. In addition, our model accounts for the impact of physical variables (such as temperature) on the probability of a fault. We model the probability of a fault conditioned on temperature observations at the access point via the beta distribution, a highly flexible family of models. As such, we call the algorithm β -HGAMP. Numerical results demonstrate that β -HGAMP outperforms existing algorithms based on GAMP and GS-HGAMP 23.

Resource optimization of random access for transmitters with correlated activation :

For a range of scenarios arising in sensor networks, control and edge computing, communication is event-triggered; that is, in response to the environment of the communicating devices. A key feature of device activity in this setting is correlation, which is particularly relevant for sensing of physical phenomena such as earthquakes or flooding. Such correlation introduces a new challenge in the design of resource allocation and scheduling for random access that aim to maximize throughput or expected sum-rate, which do not admit a closed-form expression. In this work, we develop stochastic resource optimization algorithms to design a random access scheme that provably converge with probability one to locally optimal solutions of the throughput and the sum-rate. A key feature of the stochastic optimization algorithm is that the number of parameters that need to be estimated grows at most linearly in the number of devices. We show via simulations that our algorithms outperform existing approaches in terms of the expected sum-rate by up to $30\%$ for a moderate number of available slots 32, 44.

Emergence of Wireless MAC Protocols with Multi-Agent Reinforcement Learning :

This scenario is not exactly a grant-free access since a signaling channel exists. However, the protocol is not known in advance and we let the nodes learn the protocol through a learning phase based on decentralized reinforcement learning. We thus ropose a new framework, exploiting the multi-agent deep deterministic policy gradient (MADDPG) algorithm, to enable a base station (BS) and user equipment (UE) to come up with a medium access control (MAC) protocol in a multiple access scenario. In this framework, the BS and UEs are reinforcement learning (RL) agents that need to learn to cooperate in order to deliver data. The network nodes can exchange control messages to collaborate and deliver data across the network, but without any prior agreement on the meaning of the control messages. In such a framework, the agents have to learn not only the channel access policy, but also the signaling policy. The collaboration between agents is shown to be important, by comparing the proposed algorithm to ablated versions where either the communication between agents or the central critic is removed. The comparison with a contention-free baseline shows that our framework achieves a superior performance in terms of goodput and can effectively be used to learn a new protocol 50.

Optimal receiver in additive impulsive noise

In axis 1, we studied the capacity of channels with additive impulsive noise. In axis 3, we conducted experimentation to show evidence that this model may be relevant in random access networks. Here, we developed a new receiver adapted to the characteristics of such channel. We focus on an unsupervised estimation of an approximation of the log-likelihood ratio in the finite block length regime with unknown noise distribution. We first analyze what conditions lead to estimation failure, derive an analytical tool to assess the failure probability, and then propose to jointly use two mechanisms that significantly reduce the estimation errors. Our estimation is shown to be efficient and the proposed receiver exhibits a near-optimal performance under various noisy environment types, ranging from very impulsive to Gaussian. We also show that short LDPC codes do not achieve the performance bound predicted by the finite block length analysis. This advocate for further investigations in short block length channel coding research 40.

rational :

The work described in this section is complementary to the work described in section 7 and gives the scientific rational of the software developed in Maracas.

CorteXlab provides a unique worldwide testbed with free access to develop and tests new radio waveforms and MAC protocols without any standard limitation. The hardware is the unique limitation. Everything is software after I/Q signals processing. Our objetive is to develop collaborations to position CorteXlab as a necessary passage for reproducible research, especially with the growing interest in machine learning based approaches.

The contributions of the year are :

Lora multi-user framework :

We developed a complete GNU Radio, dynamic and customizable physical (PHY) layer for long range (LoRa) transceiver, usable with the FIT/CorteXlab radio testbed and derived from the original EPFL LoRa implementation. The created adaptation, through a standardized interface, allows end-users an easy connection to an external medium access control (MAC)/upper layer to experiment scenarios in a fully reproducible and isolated environment. It also provides several PHY layer key performance indicators and metrics such as signal to noise ratio (SNR), received signal energy, binary error rate (BER) and other, that can be used to gauge the performance of the ongoing communications as well as construct MAC layers able to use this information. Finally, the interface allows our plug&play PHY solution to be used with any existing or newly adapted MAC layer, without having to implement it in GNU Radio 43. A new software (Cortexlab_LORA_PHY) has been built.

ML based radio identification :

During the PhD of Cyrille Morin 47, we develop an experimentation to try to train a Convolutional Neural Network (CNN) classifier using raw IQ samples without preprocessing and from experimental datasets to ensure that all possible features in the signal can be used by the neural network. The network is made to cope with the channel variation effects natively, without having to introduce any form of additional information at the transmitter, by purposely introducing channel variations in the training dataset. The approach used here can be seen as a data augmentation technique, but with the augmentation of channel parameters being from physical changes in the experimentation room, not through a simulated channel model.

Radio link statistics in interference environment

With the internship of Mohamad Gritli (supervised by M. Egan and D. Gesbert), we evaluated the transmission pattern in uncoordinated IoT networks on CorteXlab.

Coded caching in single server multi-user scenario :

In 28, vwe proposed an extended coded caching scheme based on piggyback coding for single-server multi-user networks with decentralized caching. The proposed scheme is obtained by adapting Polar codes and extending the original coded caching scheme, which is based on index coding and a data assignment that can be implemented via minimum graph-colouring. Polar codes are adapted so that users can apply parts of their cache contents as the frozen bits for Polar decoding, and the coded caching is adapted so as to account for different user coding rates and to combine transmissions to cache-aided and cache-free users. Numerical simulations prove that our piggyback-coding based scheme achieves higher rates than previous schemes also in the finite block-length regime. Importantly we contributed in the design and the evaluation of real testbed measurements validating the practical implementation.

rational :

Despite the interest of CorteXlab, it may be very useful to also try and test real environments. We try to conduct such work in collaboration with Aalborg Unversity and IMT Lille-Douai 24. In IoT, as not all devices are coordinated, there are limited opportunities to mitigate interference. As such, it is crucial to characterize the interference in order to understand its impact on coding, waveform and receiver design. While a number of theoretical models have been developed for the interference statistics in communications for the IoT, there is very little experimental validation. In this work, we addressed this key gap in understanding by performing statistical analysis on recent measurements in the unlicensed 863 MHz to 870 MHz band in different regions of Aalborg, Denmark. In particular, we show that the measurement data suggests the distribution of the interference power is heavy tailed, confirming predictions from theoretical models which confirm our working assumptions (see related contributions in axes 1 and 2).

General overview and challenges :

Chemical reactions and diffusion are two basic mechanisms governing the dynamics of molecules in a fluid. As such, they play a critical role in molecular communication for channel modeling, design of detection rules, implementation of molecular circuits for computation, and modeling interactions with external biochemical systems. For finite numbers of information-carrying molecules, stochastic models naturally arise with the simplest example given by the Wiener process, often known as Brownian motion. Nevertheless, the Wiener process fails to be accurate when external forces, friction, and chemical reactions are present. Recently, there have been several contributions that tailor molecular communication systems to these more challenging channel conditions. In this paper, we first overview a general family of stochastic models of reaction and diffusion systems, including both Langevin diffusion and the reaction-diffusion master equation. These models form a basis for the use of these models as molecular communication channels, from which modulation and detection schemes can be developed. We survey recent results on the design of these schemes, with a focus on a recently developed approach which is robust to a wide range of channel models, known as equilibrium signaling. We then turn to the implementation of these detection schemes and related parameter estimation problems via stochastic molecular circuits, based on stochastic chemical reaction networks. Finally, interactions between molecular communication systems and stochastic biological systems as well as open problems are discussed. Our overarching goal is to highlight how the consideration of general stochastic models of reaction and diffusion can be utilized in order to widen the application of molecular communications both within engineered systems, and also as motivation for advances in the mathematical characterization of these models. 26

Estimation of equilibrium states of biochemical processes :

A basic problem in molecular biology is to estimate equilibrium states of biochemical processes. To this end, advanced spectroscopy methods have been developed in order to estimate chemical concentrations in situ or in vivo. However, such spectroscopy methods can require special conditions that do not allow direct observation of the biochemical process. A natural means of resolving this problem is to transmit chemical signals to another location within a lab-on-a-chip device; that is, employing molecular communication in order to perform spectroscopy in a different location. In this paper, we develop such a signaling strategy and estimation algorithms for equilibrium states of a biochemical process. In two biologically-inspired models, we then study via simulation the tradeoff between the rate of obtaining spectroscopy measurements and the estimation error, providing insights into requirements of spectroscopy devices for high throughput biological assays 22.

Equilibrium Signaling in Spatially Inhomogeneous Diffusion and External Forces :

Complex fluid media where molecules are susceptible to forces due, for example, to external magnetic fields, complicates the design of molecular communication systems. In particular, the equations governing the motion of each molecule in time do not typically admit tractable solutions, which makes receiver design challenging for standard communication schemes; e.g., based on concentration shift keying. In this paper, a new communication scheme is proposed, which leads to simple expressions for receiver statistics, even when spatially inhomogeneous diffusion and external forces are present. The proposed scheme exploits the equilibrium statistics of the system, which arise in a wide range of scenarios. This approach is illustrated in a bounded system with inhomogeneous diffusion and external forces determined by a quadratic potential 25.

On going work :

The field of quantum information science may revolutionize numerous information processing applications. Indeed, quantum information may lead to fast algorithms for complex computational problems and may help to design new communication protocols. Our research direction is to evaluate the potential of quantum information in the core of wireless communication protocols.

We started by exploring the use of quantum algorithms in the grant-free massive access in IoT explored in axis 2.

Quantum algorithms for multi-user detection in massive access :

To support multiple transmissions in an optical fiber, several techniques have been studied such as Optical Code Division Multiple Access (OCDMA). In particular, the incoherent OCDMA systems are appreciated for their simplicity and reduced cost. However, they suffer from Multiple Access Interference (MAI), which degrades the performances. In order to cope with this MAI, several detectors have been studied. Among them, the Maximum Likelihood (ML) detector is the optimal one but it suffers from high complexity as all possibilities have to be tested prior to decision. However, thanks to the recent quantum computing advances, the complexity problem can be circumvented. Indeed, quantum algorithms, such as Grover, exploit the superposition states in the quantum domain to accelerate the computation. Thus, in this paper, we propose to adapt the quantum Grover's algorithm in the context of MUD, in an OCDMA system using non-orthogonal codes. We propose a way to adapt the received noisy signal to the constraints defined by Grover's algorithm. We further evaluate the probability of success in detecting the active users for different noise levels. Aside from the complexity reduction, simulations show that our proposal has a high probability of detection when the received signal is not highly altered. We show the benefits of our proposal compared to the classical and the optimal ML detector 38.

10.1 International initiatives

10.2 European initiatives

10.3 National initiatives

11.1 Promoting scientific activities

11.2 Teaching - Supervision - Juries

12.1 Major publications

• 1articleG. C.George C. Alexandropoulos, P.Paul Ferrand, J.-M.Jean-Marie Gorce and C. B.Constantinos B. Papadias. Advanced coordinated beamforming for the downlink of future LTE cellular networks.IEEE Communications Magazine547Arxiv: 16 pages, 6 figures, accepted to IEEE Communications MagazineJuly 2016, 54 - 60
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• 2articleS.Selma Belhadj Amor, S.Samir Perlaza, I.Ioannis Krikidis and H. V.H. Vincent Poor. Feedback Enhances Simultaneous Wireless Information and Energy Transmission in Multiple Access Channels.IEEE Transactions on Information Theory638August 2017, 5244 - 5265
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• 3articleM.Mauro De Freitas, M.Malcolm Egan, L.Laurent Clavier, A.Alban Goupil, G. W.Gareth W. Peters and N.Nourddine Azzaoui. Capacity Bounds for Additive Symmetric $$ -Stable Noise Channels.IEEE Transactions on Information Theory638August 2017, 5115-5123
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• 4articleM.Malcolm Egan, L.Laurent Clavier, C.Ce Zheng, M.Mauro De Freitas and J.-M.Jean-Marie Gorce. Dynamic Interference for Uplink SCMA in Large-Scale Wireless Networks without Coordination.EURASIP Journal on Wireless Communications and Networking20181August 2018, 1-14
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• 5articleM.Malcolm Egan, J.Jan Drchal, J.Jan Mrkos and M.Michal Jakob. Towards Data-Driven On-Demand Transport.EAI Endorsed Transactions on Industrial Networks and Intelligent Systems514June 2018, 1-10
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• 6articleM.Malcolm Egan, V.Valeria Loscrì, T. Q.Trung Q Duong and M. D.Marco Di Renzo. Strategies for Coexistence in Molecular Communication.IEEE Transactions on NanoBioscience181January 2019, 51-60
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• 7articleM.Malcolm Egan, T. C.Trang C Mai, T. Q.Trung Q Duong and M.Marco Di Renzo. Coexistence in Molecular Communications.Nano Communication Networks16February 2018, 37-44
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• 8inproceedingsM.Malcolm Egan, S.Samir Perlaza and V.Vyacheslav Kungurtsev. Capacity sensitivity in additive non-gaussian noise channels.2017 IEEE International Symposium on Information Theory (ISIT)IEEE2017, 416--420
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• 9articleI.Iñaki Esnaola, S.Samir Perlaza, H. V.H. Vincent Poor and O.Oliver Kosut. Maximum Distortion Attacks in Electricity Grids.IEEE Transactions on Smart Grid742016, 2007-2015
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• 10articleY.Yasser Fadlallah, A. M.Antonia M. Tulino, D.Dario Barone, G.Giuseppe Vettigli, J.Jaime Llorca and J.-M.Jean-Marie Gorce. Coding for Caching in 5G Networks.IEEE Communications Magazine552February 2017, 106 - 113
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• 11articleC.Cristian Genes, I.Iñaki Esnaola, S.Samir Perlaza, L. F.Luis F Ochoa and D.Daniel Coca. Robust Recovery of Missing Data in Electricity Distribution Systems.IEEE Transactions on Smart Grid2018
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• 12inproceedingsJ.-M.Jean-Marie Gorce, Y.Yasser Fadlallah, J.-M.Jean-Marc Kelif, H. V.H Vincent Poor and A.Azeddine Gati. Fundamental limits of a dense iot cell in the uplink.Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt), 2017 15th International Symposium onIEEE2017, 1--6
• 13articleC.Claire Goursaud and J.-M.Jean-Marie Gorce. Dedicated networks for IoT : PHY / MAC state of the art and challenges.EAI endorsed transactions on Internet of ThingsOctober 2015
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• 14articleA.Arturo Guizar, C.Claire Goursaud and J.-M.Jean-Marie Gorce. Performance of IR-UWB cross-layer ranging protocols under on-body channel models with body area networks.Annals of Telecommunications - annales des télécommunicationshttp://link.springer.com/article/10.1007/s12243-016-0500-4March 2016, 453–46
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• 15articleN.Nizar Khalfet and S. M.Samir M. Perlaza. Simultaneous Information and Energy Transmission in the Two-User Gaussian Interference Channel.IEEE Journal on Selected Areas in Communications371January 2019, 156-170
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• 16articleT. C.Trang C Mai, M.Malcolm Egan, T. Q.Trung Q Duong and M.Marco Di Renzo. Event Detection in Molecular Communication Networks with Anomalous Diffusion.IEEE Communications Letters216February 2017, 1249 - 1252
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• 17articleY.Yuqi Mo, M.-T.Minh-Tien Do, C.Claire Goursaud and J.-M.Jean-Marie Gorce. Up-Link Capacity Derivation for Ultra-Narrow-Band IoT Wireless Networks.International Journal of Wireless Information Networks243June 2017, 300-316
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• 18inproceedingsS.Samir Perlaza, A.Ali Tajer and H. V.H Vincent Poor. Simultaneous Energy and Information Transmission: A Finite Block-Length Analysis.IEEE International Workshop on Signal Processing Advances in Wireless Communications2018
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• 19articleV.Victor Quintero, S.Samir Perlaza, I.Iñaki Esnaola and J.-M.Jean-Marie Gorce. Approximate Capacity Region of the Two-User Gaussian Interference Channel with Noisy Channel-Output Feedback.IEEE Transactions on Information Theory647Part of this work was presented at the IEEE International Workshop on Information Theory (ITW), Cambridge, United Kingdom, September 2016 and IEEE International Workshop on Information Theory (ITW), Jeju Island, Korea, October, 2015. Parts of this work appear in INRIA Technical Report Number 0456, 2015, and INRIA Research Report Number 8861.July 2018, 5326-5358
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• 20articleV.Victor Quintero, S.Samir Perlaza, I.Iñaki Esnaola and J.-M.Jean-Marie Gorce. When Does Output Feedback Enlarge the Capacity of the Interference Channel?IEEE Transactions on Communications662Part of this work was presented at the 11th EAI International Conference on Cognitive Radio Oriented Wireless Networks (CROWNCOM), Grenoble, France, May 30-Jun 1 2016September 2017, 615-628
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• 21articleD.Dimitrios Tsilimantos, J.-M.Jean-Marie Gorce, K.Katia Jaffrès-Runser and H. V.H. Vincent Poor. Spectral and Energy Efficiency Trade-Offs in Cellular Networks.IEEE Transactions on Wireless Communications151January 2016, 54-66
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12.2 Publications of the year

12.3 Cited publications

• 52articleS.Sebastian Dörner, S.Sebastian Cammerer, J.Jakob Hoydis and S.Stephan ten Brink. Deep learning based communication over the air.IEEE Journal of Selected Topics in Signal Processing1212018, 132--143
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• 53articleM. G.Mohammad G Khoshkholgh, K.Keivan Navaie, K. G.Kang G Shin, V.VCM Leung and H.Halim Yanikomeroglu. Caching or No Caching in Dense HetNets?arXiv preprint arXiv:1901.110682019
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• 54articleS.Songze Li, M. A.Mohammad Ali Maddah-Ali, Q.Qian Yu and A. S.A Salman Avestimehr. A fundamental tradeoff between computation and communication in distributed computing.IEEE Transactions on Information Theory6412018, 109--128
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• 55articleW.Wei Liu, S.Shuyang Xue, J.Jiandong Li and L.Lajos Hanzo. Topological Interference Management for Wireless Networks.IEEE Access62018, 76942--76955
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• 56articleY.Yuyi Mao, C.Changsheng You, J.Jun Zhang, K.Kaibin Huang and K. B.Khaled B Letaief. A survey on mobile edge computing: The communication perspective.IEEE Communications Surveys & Tutorials1942017, 2322--2358
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• 57articleY.Yury Polyanskiy, H. V.H Vincent Poor and S.Sergio Verdú. Channel coding rate in the finite blocklength regime.IEEE Transactions on Information Theory5652010, 2307
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• 58articleJ.J. Sachs, L. A.L. A. A. Andersson, J.J. Araújo, C.C. Curescu, J.J. Lundsjö, G.G. Rune, E.E. Steinbach and G.G. Wikström. Adaptive 5G Low-Latency Communication for Tactile InternEt Services.Proceedings of the IEEE1072Feb 2019, 325-349
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• 59articleV. Y.Vincent YF Tan. Asymptotic estimates in information theory with non-vanishing error probabilities.Foundations and Trends®in Communications and Information Theory112014, 1--184
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