Publications

2026

• Determinants of radiofrequency electromagnetic fields emitted by smartphones in French cellular telephony networks
  
  • Fontaine Maëlle
  • Guida Florence
  • Moissonnier Monika
  • Beranger Remi
  • Lagroye Isabelle
  • Orlacchio Rosa
  • Laplanche Alexia
  • Dejardin Olivier
  • Conil Emmanuelle
  • Huss Anke
  • Mazloum Taghrid
  • Wiart Joe
  • Danjou Aurélie
  • Schüz Joachim
  • Bories Serge
  • Deltour Isabelle
  Environmental Research, Elsevier, 2026, 305, Part 2, pp.125023-1:125023-11. Smartphones are a major source of radiofrequency electromagnetic field exposure, yet real-life determinants of uplink emissions remain poorly characterised. We conducted the first epidemiological study of smartphone cellular network uplink emissions in a general population sample, recruiting 167 volunteers across three French cities in 2022-2023. Emissions were measured quasi-continuously for a week during everyday activities using the novel DEVIN exposimeter, while the XMobiSensePlus application simultaneously recorded sensor values and smartphone activities in the background. Determinants of UL emission occurrence and level were analysed using logistic and linear regressions. Over 8001 h analysed, voice calls were recorded in 2.4% of time and data uploads in 81.3%. Voice calls were associated to emission occurrence (OR = 7.09, 95% CI: 5.96-8.43). In fully adjusted models, emission levels were associated to calls (+9.16 dBm, CI: 7.15; 11.16), and to Wi-Fi connection with substantially lower cellular emissions outside call periods (-15.37 dBm, CI: -17.28, -13.46). Cellular upload rates differentially increased emissions depending on Wi-Fi connection, while received signal quality differentially reduced emissions depending on call status. Legacy technologies (2G, 3G) were associated with higher emissions than 4G. Results varied across centers. Smartphone brand, operator, and Android version showed no independent association with emitted power after System-on-Chip adjustment. These findings demonstrate that real-life smartphone radiofrequency electromagnetic cellular emissions are shaped by a complex interplay of usage patterns, network conditions, and device characteristics. (10.1016/j.envres.2026.125023)
  DOI : 10.1016/j.envres.2026.125023
• A posteriori closure of turbulence models: Are symmetries preserved?
  
  • Freitas André
  • Um Kiwon
  • Desbrun Mathieu
  • Buzzicotti Michele
  • Biferale Luca
  European Journal of Mechanics - B/Fluids, Elsevier, 2026, 119, pp.204496:1-204496:8. Turbulence modeling remains a longstanding challenge in fluid dynamics. Recent advances in data- driven methods have led to a surge of novel approaches aimed at addressing this problem. This work builds upon our recent work [Phys. Rev. Fluids 10, 044602 (2025)], where we introduced a new closure for a shell model of turbulence using an a posteriori (or solver-in-the-loop) approach. Unlike most deep learning-based models, our method explicitly incorporates physical equations into the neural network framework, ensuring that the closure remains constrained by the underlying physics benefiting from enhanced stability and generalizability. In this paper, we further analyze the learned closure, probing its capabilities and limitations. In particular, we look at joint probability density functions between resolved and unresolved variables, as well as the scale invariance of multipliers (ratios between adjacent shells) within the inertial range. Although our model excels in reproducing high-order statistical moments, it breaks this known symmetry near the cutoff, indicating a fundamental limitation. We discuss the implications of these findings for subgrid-scale modeling in 3D turbulence and outline directions for future research. (10.1016/j.euromechflu.2026.204496)
  DOI : 10.1016/j.euromechflu.2026.204496
• The role of Mrs. Gerber’s Lemma for evaluating the information leakage of secret sharing schemes
  
  • Rioul Olivier
  • Béguinot Julien
  , 2026. (10.1007/978-3-032-13992-4_5)
  DOI : 10.1007/978-3-032-13992-4_5
• Towards Understanding Low-Rank Learning for Classification
  
  • Wang Xiaolin
  • Rioul Olivier
  • Mokraoui Anissa
  • Duhamel Pierre
  , 2026. <div><p>Understanding the sampling complexity of a neural network-the number of training examples it needs to generalize-is a fundamentally unresolved question, especially for modern architectures that use low-rank constraints for compression and fine-tuning. Although low-rank methods such as LoRA are widely adopted in practice, their effect on sampling complexity for classification remains poorly understood. In this work, we investigate shallow ReLU networks with low-rank constrained weights in a teacher-student context. It is shown that the inherent structure of classification tasks is a prerequisite for successful low-rank learning. A distinctive three-stage sample complexity curve is observed : (i) initial phase of random guessing; (ii) abrupt transition to rapid improvement once a critical sample size is reached; and (iii) final plateau whose height depends on the network's rank. It is shown that the critical sample size is determined by the effective task structure rather than by the raw dimension of the input data, and that the rank constraint limits the maximum achievable accuracy. Simulations on synthetic data validate our predictions, revealing a rigorous trade-off between compression and accuracy. This can guide rank selection in low-rank learning and constitutes a crucial step toward understanding low-rank training in more complex architectures and real-world contexts.</p></div>
• On the privacy cost for dependent Gaussian data: spectral density estimation under local differential privacy
  
  • Issartel Yann
  • Roueff François
  , 2026. We study the fundamental problem of estimating the dependence structure of a centered stationary Gaussian process under local differential privacy (LDP). In this setting, the spectral density characterizes the dependence structure of the data and is the quantity to be estimated. Our main contribution is to close the open $\alpha^2$-versus-$\alpha^4$ gap between the previously known lower and upper bounds on the minimax rate. Specifically, we establish a minimax lower bound showing that, over Sobolev-type classes of spectral densities, the effective sample size in the high-privacy regime is $N\alpha^4$, rather than the usual $N\alpha^2$ arising for independent observations. This additional privacy cost is caused by the temporal dependence between the observations rather than by their marginal distributions. The proof relies on a contraction bound for privatized dependent Gaussian observations. Our second contribution is a matching upper bound, free of the polylogarithmic losses present in previous work. Rather than applying a generic privatization scheme to classical estimators, we construct a problem-specific procedure attaining the rate identified by our lower bound. Beyond closing the gaps in spectral density estimation, we apply the tools developed for this problem to several related questions. We (i) close the logarithmic gap for fixed-lag autocovariance estimation, (ii) show that the $\alpha^4$ cost arises locally around every spectral density bounded away from zero, and (iii) establish that classical asymptotic equivalence with an independent Gaussian experiment generally fails under LDP.
• Semi-Supervised Novel Intrusion Detection via Orthogonal Feature Extraction and Score-Space Meta-Classifier Assembly
  
  • Vu Minh-Khanh
  • Ene Cristian
  • Mounier Laurent
  • Ramparison Mathias
  , 2026. Signature-based endpoint detection fails against novel payloads whose behavioral patterns do not match any catalogued specimen. A semi-supervised host-based intrusion detection system is introduced that decomposes each system-call window into three structurally independent views, each trained exclusively on benign traces: frequency and rarity (TF-IDF with a one-class Support Vector Machine), local topology (a 1D convolutional autoencoder), and chronological grammar (a GRU next-event predictor). The three anomaly scalars are assembled into a score-space meta-matrix over which an XGBoost layer learns the final decision boundary without accessing raw syscall sequences or malware signatures directly. On the ADFA-LD 32-bit benchmark, the fusion model achieves Area Under the ROC Curve (AUC) 0.9113, Area Under the Precision-Recall Curve (AUPR) 0.8950, and F1 0.8523 at a full-pipeline latency of 0.2217 ms per window. Per-family evaluation across six attack families yields average AUC 0.9073, and cross-path Pearson correlations of 0.43 to 0.46 indicate that the three views occupy productively distinct operating regimes and increase the number of behavioural constraints an attacker must preserve to evade detection.
• Neural Multichannel Distant Speaker Diarization and Source Separation with Beta Speaker Activity Prior
  
  • Mao Sicheng
  • Fontaine Mathieu
  • Larcher Anthony
  • Badeau Roland
  , 2026. Distant speaker diarization remains challenging due to adverse acoustic conditions, varying numbers of speakers and overlapping speech. While data-driven approaches have shown strong performance, model-driven methods offer a compelling alternative by leveraging spatial information from multichannel recordings. This paper is motivated to propose a Bayesian diarization model for a model-driven method called neural FCASA to enhance its robustness. Specifically, we propose a beta prior over speaker activity and hence a variational lower bound objective that can be seen as a regularized continuous speaker activity score in place of the original cross-entropy loss to train the diarization model. Our experiments show significant improvements in terms of Diarization Error Rate by at least 3% (16% relatively) and Jaccard Error Rate by at least 4% (20% relatively) on the AMI dataset compared to the baseline.
• Superviz26-SQL: A Multi-Domain Benchmark for SQL Attack Detection
  
  • Quetel Grégor
  • Gimenez Pierre-François
  • Robert Thomas
  • Pautet Laurent
  , 2026. Machine-learning intrusion detectors that excel on a testbed often fail once the deployment environment changes. Yet comparable multi-domain datasets with controlled distribution shifts remain scarce. This holds for SQL attack detection (SQLAD), whose public clear-text data covers only single applications, leaving the cross-domain behaviour of detection methods unmeasured. We introduce Superviz26-SQL, the first multi-domain SQLAD benchmark, extending the synthetic generation methodology of Superviz25-SQL to four heterogeneous domains derived from real-world database projects. From these domains we derive ready-to-use datasets for three evaluation protocols: Superviz26-SQL-LODO for cross-domain generalisation, Superviz26-SQL-CD for samedomain concept drift, and Superviz26-SQL-FSL for few-shot adaptation. Lexical-field analysis confirms genuine inter-domain diversity, and each training set is large enough to train deep detectors. We illustrate each protocol using reference baselines spanning five feature extractors and three decision engines. Superviz26-SQL, its three derivatives, and all generation and experiment code are publicly released to support reproducible cross-domain SQLAD research.
• Learning and simulating bosonic systems via finite-energy locality
  
  • Möbus Tim
  • Bluhm Andreas
  • Caro Matthias
  • Werner Albert
  • Rouzé Cambyse
  , 2026. Bosonic devices promise applications in simulation, sensing and quantum error correction, but infinite-dimensional local Hilbert spaces obstruct the locality tools that make qubit dynamics efficiently learnable and simulable. We establish a finite-energy locality principle for geometrically local bosonic open systems satisfying photon-number moment propagation. It compares unbounded generators with Galerkin cutoffs, transferring finite-dimensional Lieb--Robinson, product-formula and circuit techniques to bosons with explicit errors. For this moment-controlled class, we obtain, to our knowledge, the first model-independent weak Lieb--Robinson bounds beyond Bose--Hubbard-type dynamics, together with quantitative Trotter and simulation guarantees for polynomial bosonic GKSL generators. As a central application, coherent-state preparation and local heterodyne detection suffice to learn coefficients of a known bounded-degree polynomial Hamiltonian ansatz local on a bounded-growth interaction graph to accuracy $\varepsilon$ and failure probability $\delta$, with sample complexity and total evolution time both $\widetilde{\mathcal{O}}(\varepsilon^{-2}\log(m/\delta))$, where $m$ counts on-site and interaction terms. This matches the best known finite-dimensional locality-assisted scaling in accuracy and system size, up to polylogarithmic factors. The assumptions hold for Bose--Hubbard and quadratic dynamics without added dissipation; for more general local polynomial Hamiltonians they can be supplied natively or engineered by known multi-photon loss in stabilized bosonic architectures. (10.48550/arXiv.2307.15026)
  DOI : 10.48550/arXiv.2307.15026
• ReLTEx: Reliable LLM-based Taxonomy Expansion
  
  • Ghamlouch Zeinab
  • Alam Mehwish
  , 2026. Recent advances in Large Language Models (LLMs) have demonstrated strong capabilities in generating semantically relevant concepts and relations, making them promising tools for taxonomy enrichment. However, directly relying on LLM-generated expansions often leads to noisy, redundant, or hierarchically inconsistent structures, limiting their reliability for automated taxonomy expansion. In this paper, we present ReLTEx, a framework for reliable LLM-based taxonomy expansion. ReLTEx combines LLM-driven candidate generation with structure-aware validation and recursive expansion control to improve the consistency and quality of generated taxonomies by reducing hallucinations. We evaluate the proposed framework using benchmark taxonomies under a masked taxonomy expansion setting and compare multiple validation strategies. Experimental results, supported by both adapted evaluation metrics and human evaluation, demonstrate that ReLTEx produces more reliable and semantically coherent taxonomy expansions.
• LELA: An End-to-end LLM-based Entity Linking Framework with Zero-shot Domain Adaptation
  
  • Haffoudhi Samy
  • Dobričić Nikola
  • Suchanek Fabian
  • Holzenberger Nils
  , 2026. Entity linking is a key component of many downstream NLP systems, yet existing approaches are often tied to the specific target knowledge bases and domains, limiting their real world application. In this paper, we extend LELA, a modular and domain-agnostic LLM-based entity disambiguation method, into a practical Python library that integrates zero-shot Named Entity Recognition (NER) -thereby providing a complete end-toend pipeline for entity-linking in real-world usage. We provide experimental results validating LELA's performance and robustness across diverse entity linking settings. In our demo, users can play with the system on their own input texts.
• Evaluation of Electromagnetic Exposure to Signal-to-Interference Ratio in Downlink Cellular Networks
  
  • Nguyen Minh-Huy
  • Wang Shanshan
  • Wiart Joe
  , 2026. (10.46620/URSIGASS26/WXQH4454)
  DOI : 10.46620/URSIGASS26/WXQH4454
• Characterizing Perceived Readability in Data Visualization: Design and Reader Factors
  
  • Cabouat Anne-Flore
  • Kurzhals Kuno
  • Matzen Laura
  • Isenberg Tobias
  • Huron Samuel
  • Isenberg Petra
  , 2026. We report results from an online study designed to characterize perceived readability across a controlled set of static visual data representations.
• Equivariant Denoisers for Plug and Play Image Restoration
  
  • Renaud Marien
  • Guez Eliot
  • Leclaire Arthur
  • Papadakis Nicolas
  Journal of Mathematical Imaging and Vision, Springer Verlag, 2026, 68 (54). One key ingredient of image restoration is to define a realistic prior on clean images to complete the missing information in the observation. State-of-the-art restoration methods rely on a neural network to encode this prior. Typical image distributions are invariant to some set of transformations, such as rotations or flips. However, most deep architectures are not designed to represent an invariant image distribution. Recent works have proposed to overcome this difficulty by including equivariance properties within a Plug-and-Play paradigm. In this work, we propose two unified frameworks named Equivariant Regularization by Denoising (ERED) and Equivariant Plug-and-Play (EPnP) based on equivariant denoisers and stochastic optimization. We analyze the convergence of the proposed algorithms and discuss their practical benefit.
• Distributed Acoustic Sensing Over Deployed Telecommunication Fiber Networks: Opportunities and Challenges
  
  • Awwad Élie
  • Freire-Hermelo Maria
  • Prato Diane
  • Pruvost Pierre
  • Kumar Choudhury Pallab
  • Jaouën Yves
  • Gabet Renaud
  • Huang Heming
  , 2026. In this paper, we highlight opportunities and challenges of deploying Rayleigh-based distributed optical fiber sensing, also known as distributed acoustic sensing (DAS), over existing optical telecommunication networks. We review some of the prominent recent advances in this area and we shed light on future directions. First, we briefly review classifications of DAS systems and we focus on the advantages of using a continuous-wave constant-power DAS probe signal for inband coexistence between DAS and data transmission over the same fiber. Second, we emphasize on the important role of the mechanical coupling between the fiber, its hosting cable and their surrounding environment in the interpretation of the extracted strain information. Third, we show results on the estimation of distributed birefringence information using a polarization-diversity DAS interrogation. Finally, we study the impact of laser phase noise on continuous-wave constant-power DAS interrogation using phase-encoded probing sequences and attempt to mitigate it.
• Theoretical Comparison of Independent-Based Samplers
  
  • Bertholom François
  • Douc Randal
  • Roueff François
  , 2026. In this work, we analyze the convergence properties of four independent-based Markov Chain Monte-Carlo samplers, the Importance Markov Chain and three algorithms based on multiple tries. We prove that the Multiple-Try Metropolis with Independent Sampling dominates Multiple-Try Metropolis with Independent Balancing and Iterated Sampling Importance Resampling in the Peskun sense, guaranteeing its theoretical superiority over these alternatives. Furthermore, we derive a general formula for obtaining exact uniform convergence rates of these algorithms, assuming that the density ratio between the target and the proposal is bounded. In the unbounded case, while none of the three algorithms based on multiple tries are geometrically ergodic, the Importance Markov Chain only requires a simple exponential moment condition to be geometrically ergodic, or a polynomial moment condition to ensure polynomial ergodicity.
• Mutating the "Immutable": A Large-Scale Study of Git Tag Alterations
  
  • Rapaport Solal
  • Pautet Laurent
  • Tardieu Samuel
  • Zacchiroli Stefano
  • Zimmermann Théo
  , 2026. Git tags are commonly viewed as immutable references in software development, marking releases and specific repository states that underpin build reproducibility and software supply-chain integrity. Despite their intended immutability, Git allows tags to be altered through deletion or modification via force-pushed updates. The prevalence of such alterations threatens reproducible builds and dependency integrity. We conduct the first large-scale empirical study of tag alterations in public code repositories, analyzing 328.4 M software repositories from Software Heritage and identifying 10.2 M tag alterations affecting 189 k unique repositories. A cross-analysis with Nixpkgs reveals that 32 packages reference tags altered in our dataset, with 7 exhibiting confirmed build errors, providing concrete evidence that tag alterations break reproducible package builds. Our findings challenge the widespread assumption that tags are immutable anchors for released software. We therefore recommend that build systems and package managers pin dependencies to cryptographic commit hashes, that development forges expose tagmutation audit logs, and that the community adopt systematic monitoring of tag alterations as a standard supply-chain security practice. (10.1145/3820002.3828585)
  DOI : 10.1145/3820002.3828585
• Understanding Build Reproducibility in the F-Droid Ecosystem
  
  • Nanni Denise
  • Malka Julien
  • Zacchiroli Stefano
  • Zimmermann Théo
  • d'Angelo Gabriele
  , 2026. The security of open source applications benefits considerably from the possibility of rebuilding their source and verifying the output. F-Droid, a prominent distribution for open source Android applications, systematically rebuilds them from source and tests their bitwise reproducibility at app publishing time. However, F-Droid offers no guarantee that app reproducibility will continue to hold in the future. As software ecosystems evolve, reproducibility may degrade, with potential negative consequences for software preservation and security. We present the first empirical study of build reproducibility in the F-Droid app ecosystem. Analyzing historical reproducibility logs, we find that the overall bitwise reproducibility rate has been steadily increasing over time (as new versions of apps are published). We then evaluate how reproducibility holds in time for fixed app versions, by attempting to rebuild 18 904 app versions that F-Droid had previously confirmed bitwise reproducible, published between September 2018 and February 2026, achieving an 83% rebuild success rate, and identify missing dependencies as the dominant cause of failure, accounting for 76% of non-rebuildable cases. Among suc- cessfully rebuilt apps, 94% are also bitwise reproducible—i.e., they still yield bitwise identical artifacts upon rebuild. Together, these results show that while bitwise reproducibility largely holds for apps that can be rebuilt, rebuildability itself is highly sensitive to temporal decay. (10.1145/3820002.3828593)
  DOI : 10.1145/3820002.3828593
• NeuralSketch2Surf: Fast Neural Surfacing of Unoriented 3D Sketches
  
  • Ye Hongsheng
  • Sureshkumar Anandhu
  • Zhonghan Wang
  • Cani Marie-Paule
  • Hahmann Stefanie
  • Bonneau Georges-Pierre
  • Parakkat Amal Dev
  , 2026. With recent advances in VR, 3D sketches have emerged as a powerful medium for 3D model creation. However, while they already provide the user with a good perception of the intended shape, they must be surfaced before any reuse in a downstream application. This remains a challenge when sketches are unoriented, i.e., when they are simply sets of unsorted 3D strokes, without any additional normal information. We introduce NeuralSketch2Surf, the first fast and robust neural surfacing solution that processes arbitrarily unoriented sketches at interactive rates. Our approach uses S2V-Net, a new transformer network designed to mesh 3D sketches. Instead of directly inferring complex functions to represent shapes, we focus on predicting an occupancy grid, then refined using a custom smoothing function to create the desired surface. Thanks to a lightweight architecture that enables fast predictions, our method produces results in less than 2 seconds, in contrast to SOTA techniques that can take minutes or even hours. Extensive evaluations demonstrate that our method is not only fast but also generates closed surfaces with high geometric, topological, and perceptual accuracy. (10.1145/3799902.3811227)
  DOI : 10.1145/3799902.3811227
• ExposNet: A Deep Learning Framework for EMF Exposure Prediction in Complex Urban Environments
  
  • Zhang Yarui
  • Wang Shanshan
  • Wiart Joe
  IEEE Transactions on Machine Learning in Communications and Networking, Institute of Electrical and Electronics Engineers, 2026, pp.1-1. The prediction of the electric field (E-field) plays a crucial role in monitoring radiofrequency electromagnetic field (RF-EMF) exposure induced by cellular networks. In this paper, a deep learning framework is proposed to predict E-field levels in complex urban environments. First, the drive test measurement in Paris and Lyon and publicly accessible databases used to construct the training dataset are introduced, with a detailed explanation provided on how these datasets are formulated and integrated to enhance their suitability for Convolutional Neural Networks (CNNs)-based models. Then, the proposed model, ExposNet, which is a lightweight CNN-based framework, is presented. Two variants of the network structure are proposed, enabling per-frequency prediction and total E-field prediction. Extensive experimental analyses are conducted, including the comparison with a standard U-Net baseline and a classical Kriging baseline. Moreover, a detailed ablation study is conducted to quantify the contribution of each input component, and additional generalization experiments are performed on different test subsets. The overall results indicate that, despite being trained and tested on real-world measurements, the model performs well and achieves better accuracy compared to previous studies. (10.1109/TMLCN.2026.3714431)
  DOI : 10.1109/TMLCN.2026.3714431
• Indirect Core Halt via Trap Handling: Halting a RISC-V Core from User Mode Through Kernel-Mediated Fault Handling
  
  • Awais Muhammad
  • Mushtaq Maria
  • Naviner Lirida
  • Yahya Jawad Haj
  • Bruguier Florent
  , 2026. We show that a RISC-V CPU core can be persistently halted from user mode by deliberately triggering illegal CSR access instructions, requiring no kernel privilege or physical access. The fault propagates through the Linux S-mode trap handler to OpenSBI's unhandled-fault path, terminating in sbi_hart_hang(), an infinite wfi() loop independent of any debugger configuration. We validate this behaviour on a SiFive HiFive Premier P550 running Linux with OpenSBI v1.6, and demonstrate that user-triggered exceptions can cause system-wide denial of service, stressing the need for careful validation of exception-handling paths in RISC-V platforms.
• TRAPHALT: Indirect Core Halt via Trap Handling to Halt a RISC-V Core from User Mode Through Kernel-Mediated Fault Handling
  
  • Awais Muhammad
  • Mushtaq Maria
  • Naviner Lirida
  • Haj Jawad
  • Bruguier Florent
  , 2026. Modern processors rely on strict privilege separation to prevent user applications from manipulating privileged architectural state directly. In RISC-V systems, user-mode programs cannot access privileged control and status registers (CSRs) or execute privileged instructions. Still, exceptions raised by user programs are handled by the operating system kernel through the trap mechanism, which runs at a higher privilege level. This interaction between user-level faults and kernel trap handling can introduce subtle system behaviours that affect processor control state. In this work, we show that a RISC-V CPU core can be indirectly halted from user mode by deliberately triggering kernel trap handling with carefully crafted illegal instruction sequences. A user-space program forces the kernel to process a fault condition that propagates through OpenSBI's unhandled-fault path to sbi hart hang(), suspending the hart in an infinite wfi() loop. We demonstrate this behaviour on a SiFive HiFive Premier P550 platform running Linux with OpenSBI v1.6. Our results highlight how usertriggered exceptions can cause unexpected system-level effects and stress the need for careful validation of exception-handling paths in RISC-V platforms.
• System-level performance in massive JCAS networks
  
  • Mengoli Emanuele
  • Soprano-Loto Nahuel
  , 2026. <div><p>We propose a dynamic stochastic geometry framework to model and evaluate large-scale joint communication and sensing (JCAS) networks. In this model, base stations are distributed according to a homogeneous Poisson point process, while user equipments and sensing objects undergo localised random motion around their serving base station. Full spectrum reuse with a common waveform generates mutual interference that fundamentally couples the communication and sensing functionalities. Communication performance is characterised through downlink queues with service rates governed by the instantaneous signal-to-interference-plus-noise ratio (SINR) through the Shannon capacity. Sensing performance is captured by a Kalman filter (KF) whose measurement noise covariance depends on the sensing SINR, yielding a non-classical state-dependent filtering problem. We define system-level performance metrics under the Palm distribution of the base station process, distinguishing between primary metrics -namely, time-averaged communication and sensing SINRs -and derived metrics that include queue lengths and KF error covariance. Ergodic theorems permit the consistent estimation of these metrics through empirical spatial and temporal averages. Within this framework, we establish that, for the typical base station, communication and sensing metrics are associated: operating regimes favourable to one functionality tend to benefit the other. Simulation results validate the analytical findings and confirm the tracking accuracy of the proposed filter, further demonstrating that the association measure decreases as the network density increases. This work provides a theoretical foundation for cross-functional resource allocation in JCAS-enabled cellular networks.</p></div>
• Green Energy Driven Integrated Smart Grid and Wireless Networks
  
  • Wang Li-Chun
  • De Swades
  • Balakrishnan Ashutosh
  , 2026, pp.XXIII, 140. Green energy and next-generation wireless systems are no longer independent domains, rather they are rapidly converging to redefine the future of sustainable connectivity. This book presents a forward looking study outlining the design of green energy driven, integrated smart grid and wireless networks. It reimagines the integration of renewable energy with the traditional power grid, by enabling each grid user to not only consume grid energy but to also be a potential energy source, thereby revising the conventional idea of power grids. A grid networked system of such distributed ambient powered nodes is hence envisioned to potentially act as a carbon-free energy producer system to the power grid, in addition to an energy prosumer system. Through its chapters, the book outlines and highlights the importance of sustainability in 6G networks. The authors present insights on the design constraints and challenges in system analysis. The study broadly pertains to analytical modeling of spatio-temporal randomness in cellular networks, presenting novel strategies to mitigate the effects of the dual randomness on network performance. The key ideas presented include joint load and energy balancing, optimum resource provisioning, and aerial offloading. The authors also demonstrate a wider perspective by extending the concept of energy balancing to energy aware residential networks. The book concludes by discussing the scope of integration of AI in wireless networks and motivate the need for green-AI aided future networks. Written for graduate level students in computer science and electrical engineering, as well as industry professionals deploying large-scale green service solutions, this book serves as both a foundational reference and a roadmap toward sustainable, scalable, and intelligent future communication networks. (10.1007/978-3-032-20509-4)
  DOI : 10.1007/978-3-032-20509-4
• Trust-Based Admission and Departure Control for Cooperative Platooning
  
  • Braiteh Emma
  • Bassi Francesca
  • Khatoun Rida
  , 2026. <div><p>Reliable data exchange is essential for safety and coordination in Cooperative Intelligent Transport Systems (C-ITS), especially within vehicular platoons. However, this requires interaction with previously unknown vehicles, introducing security risks and requiring trust evaluation prior to sharing sensitive information. This paper presents a cooperative platooning protocol with a trust management framework based on Subjective Logic to assess the trustworthiness of vehicles based on the information they share. The framework processes heterogeneous evidence types to estimate individual trust levels at different stages. A fuzzification module classifies these trust values, enabling adaptive and interpretable trust-based decisionmaking. The resulting trust assessments are used to determine whether to accept vehicle requests to join or leave a platoon and to detect misbehavior among existing platoon members. The complete model was thoroughly evaluated using PLEXE and SUMO simulators in dynamic and realistic platooning scenarios. Empirical results demonstrate its effectiveness in identifying faulty or malicious vehicles, thereby significantly enhancing the reliability of cooperative driving decisions.</p></div>