In the context of IoT, a critical problem is to be able to serve many users, having each a low probability of transmission. The problem appears in the downlink as well as in the uplink but the problems in terms of signaling are different. This research axis is lead by Jean-MarieGorce , but all Maracas members have contributions around this scenario. This topic is covered in axes 1, 2 and 3. We present below the results of the year.

In 44, we propose a framework for maximizing the number of machine-type devices connected in the uplink of a Narrowband Internet of Things (NB-IoT) network using non-orthogonal multiple access (NOMA). The system is based on the fast-uplink grant (FUG), where the base station (BS) schedules the access for active devices requesting connection. This problem is a mixedinteger non-convex problem and real-time solutions using general solvers are computationally prohibitive. The proposed scheduling solution comprises efficient device clustering and optimum power allocation using a bipartite graph matching approach, termed connection throughput maximizing full matching with pruning (CTMBM). Different from the other solutions of state-of-the-art, our proposed scheme considers scheduling over multiple transmission time intervals while considering the transmission deadlines and quality of service (QoS) for the devices. Additionally, we provide a method for priority scheduling of a subset of devices. We compare our solution to the state-of-the-art schemes and analyze the achieved gains through Monte-Carlo computer simulations.

An important part of the work elaborated with LelioChetot and MalcolmEgan , concerns the study of non independent and identical distributions of messages. We studied both transmission and reception strategies.

In 38, we studied the context of Industrial IoT (IIoT) as it is one of the major verticals targeted by the next generations of wireless networks. In order to provide industrial plants with features relying on wireless communications, the grant-free RA (GFRA) protocol appears to be a promising means for supporting massive ultra-reliable connectivity; at the same time, it is a critical bottleneck that requires an access point (AP) to be able to jointly perform active user detection and channel estimation (AUDaCE) to fulfill its main mission of allowing industrial wireless devices to access the core network. This mission is even harder when the GFRA requests are correlated because of event-driven activity triggers. In this work, we propose a new tractable gaussian correlated activity (GCA) model for this scenario. The corresponding AUDaCE problem is then studied in the Bayesian compressed sensing (BCS) framework. An hybrid instance of the generalized AMP (GAMP) algorithm is derived and its capability to perform AUDaCE is numerically assessed by extensive Monte-Carlo simulations. The numerical results show gains of 2.5dB in channel estimation gain for twice less detection errors w.r.t. state-of-theart algorithms.

But in grant-free random access, another key question is how devices should utilize resources without coordination. One standard solution to this problem are strategies where devices randomly select time-slots based on an optimized stochastic allocation rule. However, the optimization of this allocation rule requires accurate knowledge of which devices have been active in previous frames. As user identification algorithms are subject to errors, the expected throughput of the optimized allocation can be highly suboptimal. In 40, 48, 51, we propose algorithms for optimization of device time-slot allocations that mitigate the impact of user identification errors. We show that when the activity distribution with and without errors is known, then our algorithm converges with probability one to a stationary point. When the activity distributions are not available, we introduce new theoretically-motivated heuristics which significantly improve the expected throughput over existing algorithms and approach the performance when errors are not present.

This line of research is lead by Jean-MarieGorce and contributes to axis 1 and axis 2. The objective is to extend information theory to explore the fundamental limits of communications under heterogeneous time constraints.

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 (in the information theory wording, a c.u. corresponds to one transmission symbol), before the decoding deadline. Nevertheless, this assumption fails when messages arrive rapidly and reliability constraints require that the number of channel uses exceed the time between arrivals. In 52, we consider a broadcast setting in which a transmitter wishes to send two different messages to two receivers over Gaussian channels. Messages have different arrival times and decoding deadlines such that their transmission windows overlap. For this setting, we propose a coding scheme that exploits Marton's coding strategy. We derive rigorous bounds on the achievable rate regions. Those bounds can be easily employed in point-to-point settings with one or multiple parallel channels. In the point-to-point setting with one or multiple parallel channels, the proposed achievability scheme outperforms the Normal Approximation, especially when the number of channel uses is smaller than 200. In the broadcast setting, our scheme agrees with Marton's strategy for sufficiently large numbers of channel uses and shows significant performance improvements over standard approaches based on time sharing for transmission of short packets.

In 53 we explore how random user activities and heterogeneous delay traffic can be taken into account to design efficient coding schemes and information-theoretic converse results. The heterogeneous 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. Each transmitter is active with probability $\rho \in [0,1]$. We consider two different models for the arrival of the mixed-delay traffic. We derive inner and outer bounds on the fundamental per-user multiplexing gain (MG) region of the symmetric Wyner network as well as inner bounds on the fundamental MG region of the hexagonal model. Our inner and outer bounds are generally very close and coincide in special cases.

This line of research is lead by MalcolmEgan and covers axis 1 and axis 2. This research is in line with join communication and computing, in the context of goaol oriented communications. Since a receiver aims at computing a function, the objective is to learn simultaneously the encoding procedures and the classification function.

With the proliferation of cheap sensors and the ubiquity of cloud and edge computing, predictive/condition-based maintenance is expected to play an important role in smart homes and buildings. Nevertheless, a key difficulty is ensuring that sensors provide data of sufficient quality in order to reliably detect building (e.g., heating system) degradation in systems or comfort. At the same time, sensor utilization should be limited as much as possible in order to minimize power consumption and increase the lifetimes of batteries. A solution to this problem requires careful codesign of sensor communication and data analytics. In 27, 37, we introduce a formulation of this codesign problem, which is based on an optimization problem to jointly design how often data is collected and compression levels in order to balance the quality of fault detection with the quantity of transmitted data. To solve the optimization problem, we apply a differentiable search algorithm based on a variant of stochastic gradient descent for discrete optimization problems. We apply our codesign framework and solve the resulting optimization problem using data obtained from a building comfort experiment known as the Twin House Experiment. We also provide an extension of our algorithm to a dynamic variant of the codesign framework, where comfort levels and power consumption penalties are time varying. Numerical results show that our algorithm rapidly finds an efficient tradeoff between classifier accuracy and sensor power consumption.

This line of research is lead by MaximeGuillaud and covers axis 1 and axis 2. Although the papers described below are more theoretic and cover general data analysis problems, their application is important in various communication problems such as massive access or channel charting.

In 33, asymmetric order$-d$ spiked tensor models with Gaussian noise are studied, relying on random matrix theory (RMT). Using the variational definition of the singular vectors and values of (Lim, 2005), we show that the analysis of the considered model boils down to the analysis of an equivalent spiked symmetric block-wise random matrix. Our approach allows the exact characterization of the almost sure asymptotic singular value and alignments of the corresponding singular vectors with the true spike components. In contrast to other works that rely mostly on tools from statistical physics to study random tensors, our results rely solely on classical RMT tools such as Stein's lemma. Finally, classical RMT results concerning spiked random matrices are recovered as a particular case. Then, 45, 35 study the deflation algorithm when applied to estimate a low-rank symmetric spike contained in a large tensor corrupted by additive Gaussian noise. Specifically, we provide a precise characterization of the large-dimensional performance of deflation in terms of the alignments of the vectors obtained by successive rank-1 approximation and of their estimated weights, assuming non-trivial (fixed) correlations among spike components. Our analysis allows an understanding of the deflation mechanism in the presence of noise and can be exploited for designing more efficient signal estimation methods. 46 provides a theoretical analysis of learning from data that have an underlying low-rank tensor structure in both supervised and unsupervised settings, under a simplified data model. For the supervised setting, we provide an analysis of a Ridge classifier (with high regularization parameter) with and without knowledge of the low-rank structure of the data. Our results quantify analytically the gain in misclassification errors achieved by exploiting the low-rank structure for denoising purposes, as opposed to treating data as mere vectors. We further provide a similar analysis in the context of clustering, thereby quantifying the exact performance gap between tensor methods and standard approaches which treat data as simple vectors.

All Maracas permanent members have some works related to the use of Deep Neural Networks (DNN). Indeed, using DNN for some difficult tasks to be done in communications is quite standard and is one of the keystones elements for 6G. We may use machine learning for channel charting, for terminal identification, for sensing, for coding, etc... In this section we present two line of research devoted to the design of NN based solutions and focusing and energy efficiency. The first part study the DNN architecture itself and the second proposed the use of spike neurons for wake-up radio.

This line of research is lead by ClaireGoursaud and covers axis 2 and axis 4. The objective is to bridge the gap between network protocol design and quantum algorithms, assuming that in the future, some equipment could host a quantum computer. We explore how such equipement could improve the complexity of some protocols.

In 28, the key challenges (loss due to distance, entanglement routing, multi‐commodities) for the coming quantum internet, relying on entanglement of quantum bits (for short, qubits) on top of an existing network, are analyzed. A unifying framework enabling to compare the various entanglement distribution, purification, and routing protocols published so far is presented. With regard to entanglement routing, the introduction of different time windows will be essential in order to cope efficiently with the main challenges like complex route calculation and fidelity estimation on the one hand, actual entanglement route selection and entangled photon generation on the other hand. For a roll‐out on top of existing transmission networks, all the research publications for the last 20 years start to cover pretty well the global scheme. Nevertheless, open questions remain, like the actual advantage of some task execution prior to the online quantum path selection, or the design of algorithms approximating the multi‐commodities flow optimization problem, or the issue of dealing with a processing time not much longer than the qubit life time.

This research is lead by MaximeGuillaud and covers axes 1, 2 and 3. As detailed in 30, channel charting is a recently proposed framework that applies dimensionality reduction to channel state information (CSI) in wireless systems with the goal of associating a pseudo-position to each mobile user in a low-dimensional space: the channel chart. Channel charting summarizes the entire CSI dataset in a self-supervised manner, which opens up a range of applications that are tied to user location. In this article, we introduce the theoretical underpinnings of channel charting and present an overview of recent algorithmic developments and experimental results obtained in the field. We furthermore discuss concrete application examples of channel charting to network- and user-related applications, and we provide a perspective on future developments and challenges as well as the role of channel charting in next-generation wireless networks.

CC can be applied at the infrastructure basestation side with the goal of extracting pseudo-position information for each user. In 36, we focus on the practically relevant streaming CSI data scenario, in which CSI is constantly estimated. To deal with storage limitations, we develop a novel streaming CC architecture that maintains a small core CSI dataset from which the channel charts are learned. Curation of the core CSI dataset is achieved using a min-maxsimilarity criterion. Numerical validation with measured CSI data demonstrates that our method approaches the accuracy obtained from the complete CSI dataset while using only a fraction of CSI storage and avoiding catastrophic forgetting of old CSI data.

We also applied CC for Ultra-Reliable Low-Latency Communications (URLLC) since it relies on accurate knowledge of channel statistics. Exploiting the spatial consistency of channel statistics arises as a promising solution, allowing a base station to predict the propagation conditions and select the communication parameters for a new user from samples collected from previous users of the network. Based on this idea, 31 provides a timely framework to exploit long-range channel spatial correlation through so-called statistical radio maps, enabling URLLC communications with given statistical guarantees. The framework is exemplified by predicting the channel capacity distribution both in a location-based radio map and in a latent space rendered by a channel chart, the latter being a localization-free approach based on channel state information. It is also shown how to use the maps to select the transmission rate in a new location that achieves a target level of reliability.

This research is lead by MalcolmEgan . It cover axes 1 and 4 and the objective is to elaborate an information and communication theory appropriate for molecular communications. A more detailed definition is provided in 29: within many chemical and biological systems, both synthetic and natural, communication via chemical messengers is widely viewed as a key feature. Often known as molecular communication, such communication has been a concern in the fields of synthetic biologists, nanotechnologists, communications engineers, and philosophers of science. However, interactions between these fields are currently limited. Nevertheless, the fact that the same basic phenomenon is studied by all of these fields raises the question of whether there are unexploited interdisciplinary synergies. In this paper, we summarize the perspectives of each field on molecular communications, highlight potential synergies, discuss ongoing challenges to exploit these synergies, and present future perspectives for interdisciplinary efforts in this area.

The Transfer of ‘information’ via molecules is a theme that resonates across the realm of nature, underlying collective behavior, homeostasis, and many disorders and diseases, and potentially holding the answers to some of the life’s most profound questions. The prospects of understanding and manipulating this natural modality of communication have attracted a significant research interest from information and communication theorists (ICT) over the past two decades. The aim is to provide novel means of understanding and engineering biological systems. These efforts have produced substantial body of literature that sets the groundwork for bio-inspired, artificial Molecular Communication (MC) systems. This ICT-based perspective has also contributed to the understanding of natural MC, with many of the results from these endeavors being published in tha special issue co-edited by MalcolmEgan32.

To model biochemical reaction systems with diffusion one can either use stochastic, microscopic reaction-diffusion master equations or deterministic, macroscopic reaction-diffusion system. The connection between these two models is not only theoretically important but also plays an essential role in applications. In 50, we consider the macroscopic limits of the chemical reaction-diffusion master equation for first-order chemical reaction systems in highly heterogeneous environments. More precisely, the diffusion coefficients as well as the reaction rates are spatially inhomogeneous and the reaction rates may also be discontinuous. By carefully discretizing these heterogeneities within a reaction-diffusion master equation model, we show that in the limit we recover the macroscopic reaction-diffusion system with inhomogeneous diffusion and reaction rates.

In order to fully exploit the potential of molecular communication (MC) for intra-body communication, practically implementable cellular receivers are an important long-term goal. A variety of receiver architectures based on chemical reaction networks (CRNs) and gene-regulatory networks (GRNs) has been introduced in the literature, because cells use these concepts to perform computations in nature. However, practical feasibility is still limited by stochastic fluctuations of chemical reactions and long computation times in GRNs. Therefore, in 39, we propose two receiver designs based on stochastic CRNs, i.e., CRNs that perform computations by exploiting the intrinsic fluctuations of chemical reactions with very low molecule counts. The first CRN builds on a recent result from chemistry that showed how Boltzmann machines (BMs), a commonly used machine learning model, can be implemented with CRNs. While this approach yields a fixed CRN once deployed, our second approach based on a manually designed CRN can be trained with pilot symbols even within the cell and thus adapt to changing channel conditions. We extend the literature by showing that practical robust detectors can achieve close-to-MAP performance even without explicit channel knowledge.