arXiv subjects

Jean-Marie Gorce

Publications and source records attributed to Jean-Marie Gorce.

At least 19 recordsLinked to original sources

arXiv2026

Decoding Delay Guarantees of Space Regulated Multiple Access Random Wireless Networks using Successive Interference Cancellation

This paper is focused on decoding delay guarantees in wireless networks, where messages have a given signal-to-interference-plus-noise ratio threshold $η_0$ to meet in order to be successfully decoded, and where transmissions should occur within some strict time constraints. Its main contribution consists in quantifying the worst-case transmissions decoding delays in the uplink of cellular and cell-free networks using successive interference cancellation. We show how such decoding delay guarantees can be obtained using spatial network calculus, a new tool introduced recently, and in particular spatial regulation. The results rely on the assumption of absence of fading. We nevertheless outline what this approach will lead to in the fading case for cellular networks.

cs.NI↗

arXiv2026

Optimization of Sparse VLSF Codes for Short-Packet Transmission via Saddlepoint Methods

In this work, we present an optimization framework for sparse variable-length stop-feedback (VLSF) codes based on a saddlepoint approximation, which jointly optimizes the decoding configuration parameters. Thanks to the analytical tractability of the saddlepoint approximation, the framework enables efficient gradient-based optimization of such parameters for common memoryless channels, including the additive white Gaussian noise, binary symmetric, and binary erasure channels. We further propose a refined decoding rule that extends the conventional fixed-threshold rule and leads to a tighter achievability bound. Numerical results demonstrate that our framework provides near-optimal decoding configurations at low computational cost. Moreover, the results from our refined rule demonstrate that the fixed-threshold decoding rule is restrictive and that achievability bounds can be further tightened.

cs.IT↗

arXiv2026

VLSF Decoding with Reliability Guarantees over Correlated Noncoherent Fading Channels

This paper studies reliability-guaranteed decoding for variable-length stop-feedback (VLSF) codes over correlated noncoherent fading channels. The decoding rule is based on the evolution of the information density associated with a given channel input-output realization. Due to channel memory, exact evaluation of this information density is intractable. To enable constructive decoding, computable finite-blocklength lower and upper bounds on the information density that hold uniformly over time along each input-output sequence are derived. The lower bound enables a stopping-time analysis for VLSF decoding and has an operational meaning, while the upper bound provides a reference for the relaxation gap, which is explicitly characterized. As a concrete application, the Gauss-Markov fading channel with Gaussian signaling is considered to numerically investigate the stopping-time distribution and the impact of fading correlation on decoding performance.

cs.IT↗

arXiv2026

Achievability Bounds of Coding with Finite Blocklength for Gaussian Broadcast Channels

In this paper, we study the achievable performance of dirty paper coding for the Gaussian broadcast channel (BC) with finite blocklength and we propose two different achievability bounds for this problem. We present the broadcast adaptation of dependence testing bound of Polyanskiy et al. 2010, which is an upper bound on the average error probability that depends on the channel dispersion terms of each error event for fixed input. Additionally, we introduce the $κβ$ lower bounds on the maximal code sizes of each user using dirty paper coding.

cs.IT↗

arXiv2025

Streaming Federated Learning with Markovian Data

Federated learning (FL) is now recognized as a key framework for communication-efficient collaborative learning. Most theoretical and empirical studies, however, rely on the assumption that clients have access to pre-collected data sets, with limited investigation into scenarios where clients continuously collect data. In many real-world applications, particularly when data is generated by physical or biological processes, client data streams are often modeled by non-stationary Markov processes. Unlike standard i.i.d. sampling, the performance of FL with Markovian data streams remains poorly understood due to the statistical dependencies between client samples over time. In this paper, we investigate whether FL can still support collaborative learning with Markovian data streams. Specifically, we analyze the performance of Minibatch SGD, Local SGD, and a variant of Local SGD with momentum. We answer affirmatively under standard assumptions and smooth non-convex client objectives: the sample complexity is proportional to the inverse of the number of clients with a communication complexity comparable to the i.i.d. scenario. However, the sample complexity for Markovian data streams remains higher than for i.i.d. sampling.

cs.LG↗

arXiv2025

Neyman Pearson Detector for Multiple Ambient Backscatter Zero-Energy-Devices Beacons using Near-Perfect Code

Recently, a novel ultra-low-power indoor localization system based on Zero-Energy Devices (ZEDs) has shown promising results in ambient backscatter communication. In this paper, we study detection of multiple coexisting ZEDs in ambient backscatter systems under interference and synchronization uncertainty. Building on a Neyman-Pearson (NP) formulation previously applied to single-tag detection, we introduce a detector tailored to multi-tag scenarios. The core idea is to use a Near-Perfect Code (NPC) as the synchronization sequence, which substantially improves the peak-to-sidelobe (PSL) ratio and thus separability among concurrent tags. The proposed scheme replaces dual band-pass filtering with dual correlators, enabling an explicit Bayesian detector and tight control of the false-alarm rate; we further incorporate a contrast metric and multi-frequency combining to reveal secondary tags. Experiments on the CorteXlab testbed (part of the SLICES-EU infrastructure) confirm robustness at low SNR, with observed PSL improvements from about 11 dB to about 22 dB. These results advance scalable, reliable ambient backscatter localization in practical multi-tag environments.

eess.SP↗

arXiv2025

Neyman-Pearson Detector for Ambient Backscatter Zero-Energy-Devices Beacons

Recently, a novel ultra-low power indoor wireless positioning system has been proposed. In this system, Zero-Energy-Devices (ZED) beacons are deployed in Indoor environments, and located on a map with unique broadcast identifiers. They harvest ambient energy to power themselves and backscatter ambient waves from cellular networks to send their identifiers. This paper presents a novel detection method for ZEDs in ambient backscatter systems, with an emphasis on performance evaluation through experimental setups and simulations. We introduce a Neyman-Pearson detection framework, which leverages a predefined false alarm probability to determine the optimal detection threshold. This method, applied to the analysis of backscatter signals in a controlled testbed environment, incorporates the use of BC sequences to enhance signal detection accuracy. The experimental setup, conducted on the FIT/CorteXlab testbed, employs a two-node configuration for signal transmission and reception. Key performance metrics, which is the peak-to-lobe ratio, is evaluated, confirming the effectiveness of the proposed detection model. The results demonstrate a detection system that effectively handles varying noise levels and identifies ZEDs with high reliability. The simulation results show the robustness of the model, highlighting its capacity to achieve desired detection performance even with stringent false alarm thresholds. This work paves the way for robust ZED detection in real-world scenarios, contributing to the advancement of wireless communication technologies.

eess.SP↗

arXiv2025

Broadcast Channels with Heterogeneous Arrival and Decoding Deadlines: Second-Order Achievability

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 arrive rapidly and reliability constraints require that the number of channel uses exceed the time between arrivals. In this paper, 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 is consistent with the normal approximation. 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.

cs.IT↗

arXiv2024

Joint Constellation Shaping Using Gradient Descent Approach for MU-MIMO Broadcast Channel

We introduce a learning-based approach to optimize a joint constellation for a multi-user MIMO broadcast channel ($T$ Tx antennas, $K$ users, each with $R$ Rx antennas), with perfect channel knowledge. The aim of the optimizer (MAX-MIN) is to maximize the minimum mutual information between the transmitter and each receiver, under a sum-power constraint. The proposed optimization method do neither impose the transmitter to use superposition coding (SC) or any other linear precoding, nor to use successive interference cancellation (SIC) at the receiver. Instead, the approach designs a joint constellation, optimized such that its projection into the subspace of each receiver $k$, maximizes the minimum mutual information $I(W_k;Y_k)$ between each transmitted binary input $W_k$ and the output signal at the intended receiver $Y_k$. The rates obtained by our method are compared to those achieved with linear precoders.

cs.IT↗

arXiv2024

Joint Slot and Power Optimization for Grant Free Random Access with Unknown and Heterogeneous Device Activity

Grant Free Random Access (GFRA) is a popular protocol in the Internet of Things (IoT) to reduce the control signaling. GFRA is a framed protocol where each frame is split into two parts: device identification; and data transmission part which can be viewed as a form of Frame Slotted ALOHA (FSA). A common assumption in FSA is device homogeneity; that is the probability that a device seeks to transmit data in a particular frame is common for all devices and independent of the other devices. Recent work has investigated the possibility of tuning the FSA protocol to the statistics of the network by changing the probability for a particular device to access a particular slot. However, power control with a successive interference cancellation (SIC) receiver has not yet been considered to further increase the performance of the tuned FSA protocols. In this paper, we propose algorithms to jointly optimize both the slot selection and the transmit power of the devices to minimize the outage of the devices in the network. We show via a simulation study that our algorithms can outperform baselines (including slotted ALOHA) in terms of expected number of devices transmitting without outage and in term of transmit power.

cs.IT↗

arXiv2024

Exploiting Device Heterogeneity in Grant-Free Random Access: A Data-Driven Approach

Grant-free random access (GFRA) is now a popular protocol for large-scale wireless multiple access systems in order to reduce control signaling. Resource allocation in GFRA can be viewed as a form of frame slotted ALOHA, where a ubiquitous design assumption is device homogeneity. In particular, the probability that a device seeks to transmit data is common to all devices. Recently, there has been an interest in designing frame slotted ALOHA algorithms for networks with heterogeneous activity probabilities. These works have established that the throughput can be significantly improved over the standard uniform allocation. However, the algorithms for optimizing the probability a device accesses each slot require perfect knowledge of the active devices within each frame. In practice, this assumption is limiting as device identification algorithms in GFRA rarely provide activity estimates with zero errors. In this paper, we develop a new algorithm based on stochastic gradient descent for optimizing slot allocation probabilities in the presence of activity estimation errors. Our algorithm exploits importance weighted bias mitigation for stochastic gradient estimates, which is shown to provably converge to a stationary point of the throughput optimization problem. In moderate size systems, our simulations show that the performance of our algorithm depends on the type of error distribution. We study symmetric bit flipping, asymmetric bit flipping and errors resulting from a generalized approximate message passing (GAMP) algorithm. In these scenarios, we observe gains up to 40\%, 66\%, and 19\%, respectively.

cs.IT↗

arXiv2024

Indoor Localization of Smartphones Thanks to Zero-Energy-Devices Beacons

In this paper, we present a new ultra-low power method of indoor localization of smartphones (SM) based on zero-energy-devices (ZEDs) beacons instead of active wireless beacons. Each ZED is equipped with a unique identification number coded into a bit-sequence, and its precise position on the map is recorded. An SM inside the building is assumed to have access to the map of ZEDs. The ZED backscatters ambient waves from base stations (BSs) of the cellular network. The SM detects the ZED message in the variations of the received ambient signal from the BS. We accurately simulate the ambient waves from a BS of Orange 4G commercial network, inside an existing large building covered with ZED beacons, thanks to a ray-tracing-based propagation simulation tool. Our first performance evaluation study shows that the proposed localization system enables us to determine in which room a SM is located, in a realistic and challenging propagation scenario.

eess.SP↗

arXiv2023

Interference Networks with Random User Activity and Heterogeneous Delay Constraints

To answer the call for a new theoretical framework to simultaneously accommodate random user activity and heterogeneous delay traffic in Internet of Things (IoT) systems, in this paper we propose coding schemes and information-theoretic converse results for the transmission of heterogeneous delay traffic over interference networks with random user activity and random data arrivals. 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 $ρ\in [0,1]$. We consider two different models for the arrival of the mixed-delay traffic: in Model~$1$, each active transmitter sends a delay-tolerant message, and with probability $ρ_f \in [0,1]$ also transmits an additional delay-sensitive message; in Model~$2$, each active transmitter sends either a delay-sensitive message with probability $ρ_f$ or a delay-tolerant message with probability $1-ρ_f$. 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. They also show that when both transmitters and receivers can cooperate, then under Model~$1$, transmitting delay-sensitive messages hardly causes any penalty on the sum per-user MG, and under Model~$2$, operating at large delay-sensitive per-user MGs incurs no penalty on the delay-tolerant per-user MG and thus increases the sum per-user MG.

cs.IT↗

arXiv2022

The Impact of Side Information on Physical Layer Security under Correlated Fading Channels

In this paper, we investigate the impact of side information (SI) on the performance of physical layer security (PLS) under correlated fading channels. By considering non-causally known SI at the transmitter and exploiting the copula technique to describe the fading correlation, we derive closed-from expressions for the average secrecy capacity (ASC) and secrecy outage probability (SOP) under positive/negative dependence conditions. We indicate that considering such knowledge at the transmitter is beneficial for system performance and ensures reliable communication with higher rates, as it improves the SOP and brings higher values of the ASC.

cs.IT↗

arXiv2022

Joint Coding of URLLC and eMBB in Wyner's Soft-Handoff Network in the Finite Blocklength Regime

Wyner's soft-handoff network is considered where transmitters simultaneously send messages of enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) services. Due to the low-latency requirements, the URLLC messages are transmitted over fewer channel uses compared to the eMBB messages. To improve the reliability of the URLLC transmissions, we propose a coding scheme with finite blocklength codewords that exploits dirty-paper coding (DPC) to precancel the interference from eMBB transmissions. Rigorous bounds are derived for the error probabilities of eMBB and URLLC transmissions achieved by our scheme. Numerical results illustrate that they are lower than for standard time-sharing.

cs.IT↗

arXiv2022

Scalable Joint Learning of Wireless Multiple-Access Policies and their Signaling

In this paper, we apply an multi-agent reinforcement learning (MARL) framework allowing the base station (BS) and the user equipments (UEs) to jointly learn a channel access policy and its signaling in a wireless multiple access scenario. In this framework, the BS and UEs are reinforcement learning (RL) agents that need to cooperate in order to deliver data. The comparison with a contention-free and a contention-based baselines shows that our framework achieves a superior performance in terms of goodput even in high traffic situations while maintaining a low collision rate. The scalability of the proposed method is studied, since it is a major problem in MARL and this paper provides the first results in order to address it.

cs.IT↗

arXiv2022

An Information-Theoretic View of Mixed-Delay Traffic in 5G and 6G

Fifth generation mobile communication systems (5G) have to accommodate both Ultra-Reliable Low-Latency Communication (URLLC) and enhanced Mobile Broadband (eMBB) services. While, eMBB applications support high data rates, URLLC services aim at guaranteeing low-latencies and high-reliabilities. eMBB and URLLC services are scheduled on the same frequency band, where the different latency requirements of the communications render the coexistence challenging. In this survey, we review, from an information theoretic perspective, coding schemes that simultaneously accommodate URLLC and eMBB transmissions and show that they outperform traditional scheduling approaches. Various communication scenarios are considered, including point-to-point channels, broadcast channels, interference networks, cellular models, and cloud radio access networks (C-RANs). The main focus is on the set of rate pairs that can simultaneously be achieved for URLLC and eMBB messages, which well captures the tension between the two types of communications. We also discuss finite-blocklength results where the measure of interest is the set of error probability pairs that can simultaneously be achieved on the two communication regimes.

cs.IT↗

arXiv2021

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.

cs.IT↗