arXiv subjects

H. Vincent Poor

Publications and source records attributed to H. Vincent Poor.

At least 73 records · Page 4Linked to original sources

arXiv2025

Differentially Private Online Community Detection for Censored Block Models: Algorithms and Fundamental Limits

We study the private online change detection problem for dynamic communities, using a censored block model (CBM). We consider edge differential privacy (DP) in both local and central settings, and propose joint change detection and community estimation procedures for both scenarios. We seek to understand the fundamental tradeoffs between the privacy budget, detection delay, and exact community recovery of community labels. Further, we provide theoretical guarantees for the effectiveness of our proposed method by showing necessary and sufficient conditions for change detection and exact recovery under edge DP. Simulation and real data examples are provided to validate the proposed methods.

cs.SI↗

arXiv2025

Towards Communication-efficient Federated Learning via Sparse and Aligned Adaptive Optimization

Adaptive moment estimation (Adam), as a Stochastic Gradient Descent (SGD) variant, has gained widespread popularity in federated learning (FL) due to its fast convergence. However, federated Adam (FedAdam) algorithms suffer from a threefold increase in uplink communication overhead compared to federated SGD (FedSGD) algorithms, which arises from the necessity to transmit both local model updates and first and second moment estimates from distributed devices to the centralized server for aggregation. Driven by this issue, we propose a novel sparse FedAdam algorithm called FedAdam-SSM, wherein distributed devices sparsify the updates of local model parameters and moment estimates and subsequently upload the sparse representations to the centralized server. To further reduce the communication overhead, the updates of local model parameters and moment estimates incorporate a shared sparse mask (SSM) into the sparsification process, eliminating the need for three separate sparse masks. Theoretically, we develop an upper bound on the divergence between the local model trained by FedAdam-SSM and the desired model trained by centralized Adam, which is related to sparsification error and imbalanced data distribution. By minimizing the divergence bound between the model trained by FedAdam-SSM and centralized Adam, we optimize the SSM to mitigate the learning performance degradation caused by sparsification error. Additionally, we provide convergence bounds for FedAdam-SSM in both convex and non-convex objective function settings, and investigate the impact of local epoch, learning rate and sparsification ratio on the convergence rate of FedAdam-SSM. Experimental results show that FedAdam-SSM outperforms baselines in terms of convergence rate (over 1.1$\times$ faster than the sparse FedAdam baselines) and test accuracy (over 14.5\% ahead of the quantized FedAdam baselines).

cs.LG↗

arXiv2025

Introducing Meta-Fiber into Stacked Intelligent Metasurfaces for MIMO Communications: A Low-Complexity Design with only Two Layers

Stacked intelligent metasurfaces (SIMs), which integrate multiple programmable metasurface layers, have recently emerged as a promising technology for advanced wave-domain signal processing. SIMs benefit from flexible spatial degree-of-freedom (DoF) while reducing the requirement for costly radio-frequency (RF) chains. However, current state-of-the-art SIM designs face challenges such as complex phase shift optimization and energy attenuation from multiple layers. To address these aspects, we propose incorporating meta-fibers into SIMs, with the aim of reducing the number of layers and enhancing the energy efficiency. First, we introduce a meta-fiber-connected 2-layer SIM that exhibits the same flexible signal processing capabilities as conventional multi-layer structures, and explains the operating principle. Subsequently, we formulate and solve the optimization problem of minimizing the mean square error (MSE) between the SIM channel and the desired channel matrices. Specifically, by designing the phase shifts of the meta-atoms associated with the transmitting-SIM and receiving-SIM, a non-interference system with parallel subchannels is established. In order to reduce the computational complexity, a closed-form expression for each phase shift at each iteration of an alternating optimization (AO) algorithm is proposed. We show that the proposed algorithm is applicable to conventional multi-layer SIMs. The channel capacity bound and computational complexity are analyzed to provide design insights. Finally, numerical results are illustrated, demonstrating that the proposed two-layer SIM with meta-fiber achieves over a 25% improvement in channel capacity while reducing the total number of meta-atoms by 59% as compared with a conventional seven-layer SIM.

cs.IT↗

arXiv2025

A Broadcast Channel Framework for MIMO-OFDM Integrated Sensing and Communication

Integrated sensing and communication (ISAC) is expected to be one of the major features of 6G wireless networks. In an ISAC system, communications and sensing functionalities are jointly performed using the same waveform, frequency band and hardware, thereby enabling various use cases such as in cyber physical systems, digital twin and smart cities. A major challenge to the design and analysis of ISAC is a unified framework that incorporates the two distinct functions. By viewing ISAC as a type of broadcast channel, in this paper, we propose a unified ISAC framework in which communication and sensing signals are broadcast to the actual communication users and virtual sensing users. This framework allows the application of existing multiplexing schemes, such as dirty paper coding (DPC) and frequency division multiplexing (FDM) that have been intensively studied in data communications and information theory. Within this framework, we propose different superposition coding schemes, for cases when the sensing waveform is known or unknown to the communication receiver. We propose the waveform optimization algorithms in a multiple-input multiple-output (MIMO) setting accounting for the effects of clutter and Doppler shift. The proposed framework is numerically evaluated for different schemes under various sensing and communications performance metrics.

cs.IT↗

arXiv2025

Holographic Beamforming for Integrated Sensing and Communication with Mutual Coupling Effects

Integrated sensing and communication (ISAC) is envisioned as a key technology in 6G networks, owing to its potential for high spectral and cost efficiency. As a promising solution for extremely large-scale arrays, reconfigurable holographic surfaces (RHS) can be integrated with ISAC to form the holographic ISAC paradigm, where enlarged radiation apertures of RHS can achieve significant beamforming gains, thereby improving both communication and sensing performance. In this paper, we investigate holographic beamforming designs for ISAC systems, which, unlike existing holographic beamforming schemes developed for RHS-aided communications, requires explicit consideration of mutual coupling effects within RHS. This is because, different from prior works only considering communication performance, ISAC systems incorporate sensing functionality, which is sensitive to sidelobe levels. Ignoring mutual coupling in holographic beamforming can lead to notable undesired sidelobes, thus degrading sensing performance. The consideration of mutual coupling introduces new challenges, i.e., it induces non-linearity in beamforming problems, rendering them inherently non-convex. To address this issue, we propose a tractable electromagnetic-compliant holographic ISAC model that characterizes mutual coupling in a closed form using coupled dipole approximations. We then develop an efficient mutual coupling aware holographic beamforming algorithm to suppress sidelobes and enhance ISAC performance. Numerical results validate effectiveness of the proposed algorithm.

cs.IT↗

arXiv2025

Bayesian-Driven Graph Reasoning for Active Radio Map Construction

With the emergence of the low-altitude economy, radio maps have become essential for ensuring reliable wireless connectivity to aerial platforms. Autonomous aerial agents are commonly deployed for data collection using waypoint-based navigation; however, their limited battery capacity significantly constrains coverage and efficiency. To address this, we propose an uncertainty-aware radio map (URAM) reconstruction framework that explicitly leverages graph-based reasoning tailored for waypoint navigation. Our approach integrates two key deep learning components: (1) a Bayesian neural network that estimates spatial uncertainty in real time, and (2) an attention-based reinforcement learning policy that performs global reasoning over a probabilistic roadmap, using uncertainty estimates to plan informative and energy-efficient trajectories. This graph-based reasoning enables intelligent, non-myopic trajectory planning, guiding agents toward the most informative regions while satisfying safety constraints. Experimental results show that URAM improves reconstruction accuracy by up to 34% over existing baselines.

eess.SP↗

arXiv2025

Joint Communication and Indoor Positioning Based on Visible Light in the Presence of Dimming

This paper proposes a joint communication and indoor positioning (JCP) system based on visible light communication (VLC) designed for high-precision indoor environments. The framework supports 2D and 3D positioning using received signal strength (RSS) from pilot transmissions, enhanced by the radical axis theorem to improve accuracy under measurement uncertainties. Communication is achieved using spatial modulation (SM) with M-ary pulse amplitude modulation (PAM), where data is conveyed through the modulation symbol and the active light-emitting diode (LED) index, improving spectral efficiency while maintaining low complexity. A pilot-aided least squares (LS) estimator is employed for joint channel and dimming coefficient estimation, enabling robust symbol detection in multipath environments characterized by both line-of-sight (LOS) and diffuse non-line-of-sight (NLOS) components, modeled using Rician fading. The proposed system incorporates a dimming control mechanism to meet lighting requirements while maintaining reliable communication and positioning performance. Simulation results demonstrate sub-centimeter localization accuracy at high signal-to-noise ratios (SNRs) and bit error rates (BERs) below 10^{-6} for low-order PAM schemes. Additionally, comparative analysis across user locations reveals that positioning and communication performance improve significantly near the geometric center of the LED layout. These findings validate the effectiveness of the proposed system for future 6G indoor networks requiring integrated localization and communication under practical channel conditions.

eess.SP↗

arXiv2025

Analytical Optimization for Antenna Placement in Pinching-Antenna Systems

As the main issue in pinching-antenna system design, antenna location optimization is key to realizing channel reconfigurability and system flexibility. Most existing works in this area adopt sophisticated optimization and learning tools to identify the optimal antenna locations in a numerical manner, where insightful understandings of the pinching antenna placement are still missing. Motivated by this research gap, this paper aims to carry out analytical optimization for pinching antenna placement, where closed-form solutions for the optimal antenna locations are obtained to reveal the impact of antenna placement on the system performance. In particular, for the user-fairness-oriented orthogonal multiple access (OMA) based transmission, analytical results are obtained to reveal that the pinching antenna needs to be activated at the place that would be beneficial to all served users; however, the users' distances to the waveguide have no impact on the location selection. For the greedy-allocation-based OMA transmission, an asymptotic study based on a high signal-to-noise ratio approximation is carried out to show that the optimal antenna location is in close proximity to the user who is nearest to the waveguide. For non-orthogonal multiple access (NOMA) based transmission, even with a user-fairness-oriented objective, the obtained analytical results show that the optimal antenna location is not the position that can benefit all users, but rather is near the user positioned closest to the waveguide.

cs.IT↗

arXiv2025

Robust Resource Allocation for Pinching-Antenna Systems under Imperfect CSI

Pinching-antenna technology has lately showcased its promising capability for reconfiguring wireless propagation environments, especially in high-frequency communication systems like millimeter-wave and terahertz bands. By dynamically placing the antenna over a dielectric waveguide, line-of-sight (LoS) connections can be made to significantly improve system performance. Although recent research have illustrated the advantages of pinching-antenna-assisted designs, they mainly presuppose complete knowledge of user locations -- an impractical assumption in real-world systems. To address this issue, the robust resource allocation in a multi-user pinching antenna downlink system with uncertain user positions is investigated, aiming to minimize total transmit power while satisfying individual outage probability constraints. First, we address the single-user case, deriving the optimal pinching antenna position and obtaining the corresponding power allocation using a bisection method combined with geometric analysis. We then extend this solution to the multi-user case. In this case, we optimize the pinching antenna position using a particle swarm optimization (PSO) algorithm to handle the resulting non-convex and non-differentiable optimization problem. Simulation results demonstrate that the proposed scheme outperforms conventional fixed-antenna systems and validate the effectiveness of the PSO-based antenna placement strategy under location uncertainty.

cs.IT↗

arXiv2025

Optimizing Model Splitting and Device Task Assignment for Deceptive Signal Assisted Private Multi-hop Split Learning

In this paper, deceptive signal-assisted private split learning is investigated. In our model, several edge devices jointly perform collaborative training, and some eavesdroppers aim to collect the model and data information from devices. To prevent the eavesdroppers from collecting model and data information, a subset of devices can transmit deceptive signals. Therefore, it is necessary to determine the subset of devices used for deceptive signal transmission, the subset of model training devices, and the models assigned to each model training device. This problem is formulated as an optimization problem whose goal is to minimize the information leaked to eavesdroppers while meeting the model training energy consumption and delay constraints. To solve this problem, we propose a soft actor-critic deep reinforcement learning framework with intrinsic curiosity module and cross-attention (ICM-CA) that enables a centralized agent to determine the model training devices, the deceptive signal transmission devices, the transmit power, and sub-models assigned to each model training device without knowing the position and monitoring probability of eavesdroppers. The proposed method uses an ICM module to encourage the server to explore novel actions and states and a CA module to determine the importance of each historical state-action pair thus improving training efficiency. Simulation results demonstrate that the proposed method improves the convergence rate by up to 3x and reduces the information leaked to eavesdroppers by up to 13% compared to the traditional SAC algorithm.

cs.LG↗

arXiv2025

Optimal Sizing and Control of a Grid-Connected Battery in a Stacked Revenue Model Including an Energy Community

Recent years have seen rapid increases in intermittent renewable generation, requiring novel battery energy storage systems (BESS) solutions. One recent trend is the emergence of large grid-connected batteries, that can be controlled to provide multiple storage and flexibility services, using a stacked revenue model. Another emerging development is renewable energy communities (REC), in which prosumers invest in their own renewable generation capacity, but also requiring battery storage for flexibility. In this paper, we study settings in which energy communities rent battery capacity from a battery operator through a battery-as-a-service (BaaS) model. We present a methodology for determining the sizing and pricing of battery capacity that can be rented, such that it provides economic benefits to both the community and the battery operator that participates in the energy market. We examine how sizes and prices vary across a number of different scenarios for different types of tariffs (flat, dynamic) and competing energy market uses. Second, we conduct a systematic study of linear optimization models for battery control when deployed to provide flexibility to energy communities. We show that existing approaches for battery control with daily time windows have a number of important limitations in practical deployments, and we propose a number of regularization functions in the optimization to address them. Finally, we investigate the proposed method using real generation, demand, tariffs, and battery data, based on a practical case study from a large battery operator in the Netherlands. For the settings in our case study, we find that a community of 200 houses with a 330 kW wind turbine can save up to 12,874 euros per year by renting just 280 kWh of battery capacity (after subtracting battery rental costs), with the methodology applicable to a wide variety of settings and tariff types.

eess.SY↗

arXiv2025

The Rate-Distortion Function for Sampled Cyclostationary Gaussian Processes with Memory and with Bounded Processing Delay: Extended Version with Proofs

We study the rate-distortion function (RDF) for the lossy compression of discrete-time (DT) wide-sense almost cyclostationary (WSACS) Gaussian processes with memory, arising from sampling continuous-time (CT) wide-sense cyclostationary (WSCS) Gaussian source processes. The importance of this problem arises as such CT processes represent communications signals, and sampling must be applied to facilitate the DT processing associated with their compression. Moreover, the physical characteristics of oscillators imply that the sampling interval is incommensurate with the period of the autocorrelation function (AF) of the physical process, giving rise to the DT WSACS model considered. In addition, to reduce the loss, the sampling interval is generally shorter than the correlation length, and thus, the DT process is correlated as well. The difficulty in the RDF characterization follows from the information-instability of WSACS processes, which renders the traditional information-theoretic tools inapplicable. In this work we utilize the information-spectrum framework to characterize the RDF when a finite and bounded delay is allowed between processing of subsequent source sequences. This scenario extends our previous works which studied settings without processing delays or without memory. Numerical evaluations reveal the impact of scenario parameters on the RDF with asynchronous sampling.

cs.IT↗

arXiv2025

Federated Learning-Enabled Hybrid Language Models for Communication-Efficient Token Transmission

Hybrid Language Models (HLMs) combine the low-latency efficiency of Small Language Models (SLMs) on edge devices with the high accuracy of Large Language Models (LLMs) on centralized servers. Unlike traditional end-to-end LLM inference, HLMs reduce latency and communication by invoking LLMs only when local SLM predictions are uncertain, i.e., when token-level confidence is low or entropy is high. However, ambiguous or low-confidence predictions still require frequent offloading to the LLM, leading to significant communication overhead in bandwidth-constrained settings. To address this, we propose FedHLM, a communication-efficient HLM framework that integrates uncertainty-aware inference with Federated Learning (FL). FedHLM's key innovation lies in collaboratively learning token-level uncertainty thresholds that govern when LLM assistance is needed. Rather than using static or manually tuned thresholds, FedHLM employs FL to optimize these thresholds in a privacy-preserving, distributed manner. Additionally, it leverages embedding-based token representations for Peer-to-Peer (P2P) resolution, enabling clients to reuse tokens inferred by semantically similar peers without engaging the LLM. We further introduce hierarchical model aggregation: edge servers refine local routing policies through client updates, while cross-cluster coordination aligns global decision boundaries. This layered design captures recurring uncertainty patterns, reducing redundant LLM queries. Experiments on large-scale news classification tasks show that FedHLM reduces LLM transmissions by over 95 percent with negligible accuracy loss, making it well-suited for scalable and efficient edge-AI applications.

cs.LG↗

arXiv2025

A New Pathway to Integrated Learning and Communication (ILAC): Large AI Model and Hyperdimensional Computing for Communication

The rapid evolution of forthcoming sixth-generation (6G) wireless networks necessitates the seamless integration of artificial intelligence (AI) with wireless communications to support emerging intelligent applications that demand both efficient communication and robust learning performance. This dual requirement calls for a unified framework of integrated learning and communication (ILAC), where AI enhances communication through intelligent signal processing and adaptive resource management, while wireless networks support AI model deployment by enabling efficient and reliable data exchanges. However, achieving this integration presents significant challenges in practice. Communication constraints, such as limited bandwidth and fluctuating channels, hinder learning accuracy and convergence. Simultaneously, AI-driven learning dynamics, including model updates and task-driven inference, introduce excessive burdens on communication systems, necessitating flexible, context-aware transmission strategies. Finally, we present a case study on a cost-to-performance optimization problem, where task assignments, model size selection, bandwidth allocation, and transmission power control are jointly optimized, considering computational cost, communication efficiency, and inference accuracy. Leveraging the Dinkelbach and alternating optimization algorithms, we offer a practical and effective solution to achieve an optimal balance between learning performance and communication constraints.

eess.SP↗

arXiv2025

LLM-Integrated Digital Twins for Hierarchical Resource Allocation in 6G Networks

Next-generation (NextG) wireless networks are expected to require intelligent, scalable, and context-aware radio resource management (RRM) to support ultra-dense deployments, diverse service requirements, and dynamic network conditions. Digital twins (DTs) offer a powerful tool for network management by creating high-fidelity virtual replicas that model real-time network behavior, while large language models (LLMs) enhance decision-making through their advanced generalization and contextual reasoning capabilities. This article proposes LLM-driven DTs for network optimization (LLM-DTNet), a hierarchical framework that integrates multi-layer DT architectures with LLM-based orchestration to enable adaptive, real-time RRM in heterogeneous NextG networks. We present the fundamentals and design considerations of LLM-DTNet while discussing its effectiveness in proactive and situation-aware network management across terrestrial and non-terrestrial applications. Furthermore, we highlight key challenges, including scalable DT modeling, secure LLM-DT integration, energy-efficient implementations, and multimodal data processing, shaping future advancements in NextG intelligent wireless networks.

eess.SP↗

arXiv2025

Robust Group Anomaly Detection for Quasi-Periodic Network Time Series

Many real-world multivariate time series are collected from a network of physical objects embedded with software, electronics, and sensors. The quasi-periodic signals generated by these objects often follow a similar repetitive and periodic pattern, but have variations in the period, and come in different lengths caused by timing (synchronization) errors. Given a multitude of such quasi-periodic time series, can we build machine learning models to identify those time series that behave differently from the majority of the observations? In addition, can the models help human experts to understand how the decision was made? We propose a sequence to Gaussian Mixture Model (seq2GMM) framework. The overarching goal of this framework is to identify unusual and interesting time series within a network time series database. We further develop a surrogate-based optimization algorithm that can efficiently train the seq2GMM model. Seq2GMM exhibits strong empirical performance on a plurality of public benchmark datasets, outperforming state-of-the-art anomaly detection techniques by a significant margin. We also theoretically analyze the convergence property of the proposed training algorithm and provide numerical results to substantiate our theoretical claims.

cs.LG↗

arXiv2025

Towards AI-Driven RANs for 6G and Beyond: Architectural Advancements and Future Horizons

It is envisioned that 6G networks will be supported by key architectural principles, including intelligence, decentralization, interoperability, and digitalization. With the advances in artificial intelligence (AI) and machine learning (ML), embedding intelligence into the foundation of wireless communication systems is recognized as essential for 6G and beyond. Existing radio access network (RAN) architectures struggle to meet the ever growing demands for flexibility, automation, and adaptability required to build self-evolving and autonomous wireless networks. In this context, this paper explores the transition towards AI-driven RAN (AI-RAN) by developing a novel AI-RAN framework whose performance is evaluated through a practical scenario focused on intelligent orchestration and resource optimization. Besides, the paper reviews the evolution of RAN architectures and sheds light on key enablers of AI-RAN including digital twins (DTs), intelligent reflecting surfaces (IRSs), large generative AI (GenAI) models, and blockchain (BC). Furthermore, it discusses the deployment challenges of AI-RAN, including technical and regulatory perspectives, and outlines future research directions incorporating technologies such as integrated sensing and communication (ISAC) and agentic AI.

eess.SP↗

arXiv2025

An Integrated Sensing and Communication System for Time-Sensitive Targets with Random Arrivals

In 6G networks, integrated sensing and communication (ISAC) is envisioned as a key technology that enables wireless systems to perform joint sensing and communication using shared hardware, antennas and spectrum. ISAC designs facilitate emerging applications such as smart cities and autonomous driving. Such applications also demand ultra-reliable and low-latency communication (URLLC). Thus, an ISAC-enabled URLLC system can prioritize time-sensitive targets and ensure information delivery under strict latency and reliability constraints. We propose a bi-static MIMO ISAC system to detect the arrival of URLLC messages and prioritize their delivery. In this system, a base station (BS) communicates with a user equipment (UE) and a sensing receiver (SR) is deployed to collect echo signals reflected from a target of interest. The BS regularly transmits messages of enhanced mobile broadband (eMBB) services to the UE. During each eMBB transmission, if the SR senses the presence of a target of interest, it immediately triggers the transmission of an additional URLLC message. To reinforce URLLC transmissions, we propose a dirty-paper coding (DPC)-based technique that mitigates the interference of both eMBB and sensing signals. To decode the eMBB message, we consider two approaches for handling the URLLC interference: treating interference as noise and successive interference cancellation. For this system, we formulate the rate-reliability-detection trade-off in the finite blocklength (FBL) regime by evaluating the communication rate of the eMBB transmissions, the reliability of the URLLC transmissions and the probability of the target detection. Our numerical analysis show that our proposed DPC-based ISAC scheme significantly outperforms power-sharing and traditional time-sharing schemes. In particular, it achieves higher eMBB transmission rate while satisfying both URLLC and sensing constraints.

cs.IT↗