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Ekram Hossain

Publications and source records attributed to Ekram Hossain.

At least 19 recordsLinked to original sources
arXiv2026

A Survey of Decentralized Physical Infrastructure Network,Research Directions, and Open Challenges

The Decentralized Physical Infrastructure Network (DePIN) represents a transformative paradigm that redefines the construction, operation, and governance of Information and Communication Technology (ICT) infrastructure in the Web 3.0 era. DePIN integrates physical resources, such as networking equipment, storage, and computing power, with decentralized digital governance, forming a self-incentivized ecosystem that is collaboratively built, shared, and governed by the community. It provides a foundational framework for future communication networks, facilitating decentralized edge intelligence, efficient resource sharing, and trustworthy coordination among heterogeneous devices. Focusing on the feasibility of this emerging paradigm, this paper examines the technology landscape in the pre-DePIN era and gaps between existing methodologies and the forthcoming decentralized infrastructure for Web 3.0. It provides a systematic and comprehensive survey of the background, core characteristics, technical architecture, and applications of DePIN across various vertical domains. The paper analyzes the DePIN technology stack from six layers: physical infrastructure, blockchain, interaction, trust, incentive, and application, with special attention to their cross-layer feedback loops, implementation readiness, and deployment limitations. To further bridge conceptual analysis and practical deployment, we propose a DePIN feasibility assessment framework covering technical, governance, and economic dimensions. Moreover, we highlight promising research directions, providing insights and guidance for further exploration and deployment of DePIN.

eess.SP↗
arXiv2026

A-MADiff: Attention-Guided Multi-Agent DRL with Diffusion Policies for Memory-Aware Task Orchestration in Mobile AIGC Networks

Artificial Intelligence-Generated Content (AIGC) services employ Generative AI (GenAI) models to automatically generate diverse content. Mobile AIGC networks host GenAI models on edge-located AIGC Service Providers (ASPs) to deliver low-latency and personalized AIGC services for mobile users. However, AIGC inference tasks typically occupy GPU memory until task completion, causing GPU memory exhaustion at serving ASPs and triggering out-of-memory failures rather than merely increasing service latency. Existing studies on AIGC task orchestration have largely overlooked GPU memory feasibility constraints. To address this issue, we develop a cooperative multi-agent orchestration framework, in which each edge node is equipped with a scheduling agent to route tasks to local ASPs or neighboring edge nodes. Since scheduling agents make decisions based only on local observations, while peer offloading couples their resource states and long-term utilities, we formulate the orchestration process as a cooperative Decentralized Partially Observable Markov Decision Process (Dec-POMDP). To solve the Dec-POMDP, we propose an \underline{A}ttention-guided \underline{M}ulti-\underline{A}gent deep reinforcement learning algorithm with \underline{Diff}usion policies (A-MADiff) under the centralized training with a decentralized execution paradigm. A-MADiff employs diffusion-based decentralized actors to generate multi-modal preferences over feasible orchestration actions, and an attention-guided centralized critic to estimate per-agent values from cross-agent states under GPU memory heterogeneity. Numerical results demonstrate that A-MADiff significantly improves the cumulative reward over the state-of-the-art baseline.

cs.NI↗
arXiv2026

Cross-reality location privacy protection in 6G-enabled vehicular metaverses: an LLM-enhanced hybrid generative diffusion model-based approach

The emergence of 6G-enabled vehicular metaverses enables Autonomous Vehicles (AVs) to operate across physical and virtual spaces through space-air-ground-sea integrated networks. The AVs can deploy AI agents powered by large AI models as personalized assistants, on edge servers to support intelligent driving decision making and enhanced on-board experiences. However, such cross-reality interactions may cause serious location privacy risks, as adversaries can infer AV trajectories by correlating the location reported when AVs request LBS in reality with the location of the edge servers on which their corresponding AI agents are deployed in virtuality. To address this challenge, we design a cross-reality location privacy protection framework based on hybrid actions, including continuous location perturbation in reality and discrete privacy-aware AI agent migration in virtuality. In this framework, a new privacy metric, termed cross-reality location entropy, is proposed to effectively quantify the privacy levels of AVs. Based on this metric, we formulate an optimization problem to optimize the hybrid action, focusing on achieving a balance between location protection, service latency reduction, and quality of service maintenance. To solve the complex mixed-integer problem, we develop a novel LLM-enhanced Hybrid Diffusion Proximal Policy Optimization (LHDPPO) algorithm, which integrates LLM-driven informative reward design to enhance environment understanding with double Generative Diffusion Models-based policy exploration to handle high-dimensional action spaces, thereby enabling reliable determination of optimal hybrid actions. Extensive experiments on real-world datasets demonstrate that the proposed framework effectively mitigates cross-reality location privacy leakage for AVs while maintaining strong user immersion within 6G-enabled vehicular metaverse scenarios.

cs.NI↗
arXiv2026

Through-the-Earth Magnetic Induction Communication and Networking: A Comprehensive Survey

Magnetic induction (MI) communication (MIC) has emerged as a promising candidate for underground communication networks due to its excellent penetration capabilities. Integration with Space-Air-Ground-Underground (SAGUI) networks in next-generation mobile communication systems requires a well-defined network architecture. A recent discovery in MIC research, MI fast fading, remains in its early stages and presents unique challenges. This paper provides a comprehensive survey on through-the-earth (TTE) MIC, covering MI applications, channel modeling, point-to-point MIC design, relay techniques, network frameworks, and emerging technologies. We compare various MIC applications to highlight TTE-specific challenges and review the principles of channel modeling, addressing both MI slow fading and MI fast fading, along with its potential impact on existing MIC theories. We conduct a fine-grained decomposition of MI channel power gain into four distinct physical parameters, and propose a novel geometric model to analyze MI fast fading. We also summarize MI relay techniques, examine crosstalk effects in relay and high-density networks, and explore key research tasks within the OSI framework for a holistic MI network protocol in SAGUI. To bridge the gaps identified, we propose a MIC framework that supports TCP/IP and Linux, enabling full implementation of existing and emerging MIC solutions. This framework empowers researchers to leverage Linux resources and deep learning platforms for accelerated development of MIC in SAGUI networks. Remaining research challenges, open issues, and promising novel techniques are further identified to advance MIC research.

eess.SY↗
arXiv2026

Explainable AI for Next-Generation Wireless Physical Layer: Basics, State-of-the-Art, and Open Challenges

Next-generation wireless systems are expected to be ``AI-native," with neural networks (NNs) embedded throughout the physical (PHY) layer protocol stack to improve spectral efficiency, latency, and network autonomy. However, the opacity of deep learning (DL) models raises increasing concerns about system reliability, safety, and privacy, especially under complex and time-varying network environments. This survey studies explainable AI (XAI) in wireless PHY layers from the explainability perspective. We first formalize a series of responsibility-oriented goals for wireless XAI. Then, we develop a systematic taxonomy of explainability approaches and distill practical criteria for deploying explanations in communication scenarios. We provide a comprehensive review of where and how XAI can be applied throughout the PHY layer, connecting representative learning paradigms to appropriate explanation techniques, evaluation metrics, and deployment considerations. Open challenges and future directions are discussed, including explainability-performance tradeoffs, explainability-aware data processing, customized XAI for communication-specific structures, cross-layer explanation consistency, and emerging needs for explaining LLM- and Agentic-AI-driven PHY layers.

eess.SP↗
arXiv2026

Deep Unfolding for SIM-Assisted Multiband MU-MISO Downlink Systems

To improve the efficiency of scarce radio-frequency (RF) resources in next-generation wireless systems, an intelligent transceiver architecture based on stacked intelligent metasurfaces (SIM) has recently emerged, where multiple programmable metasurface layers are cascaded and each layer comprises passive meta-atoms that perform beamforming directly in the wave domain. In parallel, inter-band carrier aggregation enables multi-band transmission with high spectral efficiency. Their integration in multi-band multiuser downlink transmission is challenging because a single SIM phase configuration must remain effective across all subcarriers, while user scheduling and power allocation vary across scheduling intervals. To address these challenges, we propose an alternating-optimization framework that decomposes the joint design into a power-constrained precoder update and a SIM phase update. For the SIM phase subproblem, we develop a physically consistent multi-band deep-unfolding network (MBDU-Net) that unrolls projected-gradient phase updates into a compact trainable architecture. Each stage computes an analytic gradient from the cascaded SIM channel model and learns lightweight parameters, including per-stage step sizes and band-aware scaling, enabling fast convergence. Numerical results for multi-band multiuser downlink scenarios demonstrate reliable convergence and consistent sum-rate gains on unseen channel realizations.

cs.IT↗
arXiv2026

End-to-End Performance of Video Streaming With MPEG-DASH Over Satellite 5G IAB Networks

We present an end-to-end performance evaluation of MPEG-DASH video streaming over a Low-Earth Orbit (LEO) satellite-based 5G Integrated Access and Backhaul (IAB) network. Our objective is to investigate how modern transport protocols and congestion control algorithms affect adaptive video delivery in an integrated satellite-terrestrial network (ISTN), where latency, throughput variation, and playback continuity jointly shape the user Quality-of-Experience (QoE). We implement a simulation framework in ns-3 by adapting open-source modules for the 5G radio access network, LEOS backhaul, transport layer protocols, and MPEG-DASH application behavior. Within this framework, TCP and QUIC are evaluated with multiple congestion control algorithms, including CUBIC, NewReno, and BBR. Performance is assessed using application-level and transport-level metrics, including playback duration, interruption duration, stall count, playback bitrate, throughput, latency, and fairness. The results show that no single configuration is uniformly optimal across all metrics. However, clear tradeoffs are observed among throughput, latency, playback continuity, and fairness. In particular, QUIC-BBR provides the most balanced overall behavior from a streaming QoE perspective, combining adequate playback duration with fewer interruptions and substantially lower latency than other alternatives. These findings highlight the importance of jointly considering transport design and congestion control when evaluating adaptive video streaming over ISTNs.

cs.NI↗
arXiv2026

Entanglement Rate Maximization for Dual-Connectivity Wireless Quantum Networks

The development of quantum networks (QNs) relies on efficient mechanisms for distributing entanglement among multiple quantum users (QUs) under practical system constraints. This paper investigates the problem of entanglement rate maximization in a dual-connectivity (DC) wireless quantum network comprising multiple quantum base stations (QBSs). Under the DC architecture, each QU can associate with up to two QBSs, thereby enhancing resource utilization compared to conventional single-connectivity (SC) schemes. The joint QBS-QU association and entanglement generation rate allocation problem is formulated as a mixed-integer nonlinear programming problem that incorporates practical constraints, including limited QBS entanglement generation capacity as well as heterogeneous minimum entanglement rate demands and fidelity requirements for QUs. To efficiently solve this challenging problem, an alternating optimization (AO) algorithm is developed, which decomposes the original formulation into entanglement rate allocation and association subproblems. Simulation results demonstrate that the proposed DC architecture significantly outperforms SC schemes, while the AO algorithm achieves near-optimal performance with substantially reduced computational complexity.

quant-ph↗
arXiv2026

Advanced Holographic Multi-Antenna Solutions for Global Non-Terrestrial Network Integration in IMT-2030 Systems

Sixth-generation (6G) networks are expected to provide ubiquitous connectivity across terrestrial and non-terrestrial domains. This will be possible by integrating non-terrestrial networks (NTNs) to extend coverage to underserved areas. Antennas are central to this vision, with multiple-input multiple-output (MIMO) technologies receiving the most attention due to their ability to exploit spatial multiplexing to improve link capacity and reliability. However, conventional MIMO can consume significant energy, as each antenna element typically requires an independent RF chain. This limitation is particularly critical in non-terrestrial systems, where onboard energy resources are limited. Holographic MIMO (HMIMO) has emerged as a promising alternative in this context. These systems are based on theoretically continuous apertures, where radiation is generated through controlled modulation of surface impedance. This enables beamforming mechanisms with significantly fewer RF chains, reducing power consumption. In this work, we make the case for HMIMO as a suitable candidate for NTN integration within IMT-2030 systems. We discuss its advantages over conventional MIMO and present a case study of HMIMO integration in LEO-based multi-user communication.

cs.NI↗
arXiv2026

Aerial Agentic AI: Synergizing LLM and SLM for Low-Altitude Wireless Networks

Low-Altitude Wireless Networks (LAWNs), composed of Unmanned Aerial Vehicles (UAVs) and mobile terminals, are emerging as a critical extension of 6G. However, applying Large Language Models in LAWNs faces three major challenges: 1) Computational and energy constraints; 2) Communication and bandwidth limitations; 3) Real-time and reliability conflicts. To address these challenges, we propose Aerial Agentic AI, a hierarchical framework integrating UAV-side fast-thinking Small Language Model (SLMs) with BS-side slow-thinking Large Language Model (LLMs). First, we design SLM-based Agents capable of on-board perception, short-term memory enhancement, and real-time decision-making on the UAVs. Second, we implement a LLM-based Agent system that leverages long-term memory, global knowledge, and tool orchestration at the Base Station (BS) to perform deep reasoning, knowledge updates, and strategy optimization. Third, we establish an efficient hierarchical coordination mechanism, enabling UAVs to execute high-frequency tasks locally while synchronizing with the BS only when necessary. Experimental results validate the effectiveness of the proposed Aerial Agentic AI.

cs.IT↗
arXiv2026

Benchmarking Video Foundation Models for Remote Parkinson's Disease Screening

Video-based assessments offer a scalable pathway for remote Parkinson's disease (PD) screening. While traditional approaches rely on handcrafted features mimicking clinical scales, recent advances in video foundation models (VFMs) enable representation learning without task-specific customization. However, the comparative effectiveness of different VFM architectures across diverse clinical tasks remains poorly understood. We present a large-scale systematic study using a novel video dataset from 1,888 participants (727 with PD), comprising 32,847 videos across 16 standardized clinical tasks. We evaluate seven state-of-the-art VFMs -- including VideoPrism, V-JEPA, ViViT, and VideoMAE -- to determine their robustness in clinical screening. By evaluating frozen embeddings with a linear classification head, we demonstrate that task saliency is highly model-dependent: VideoPrism excels in capturing visual speech kinematics (no audio) and facial expressivity, while V-JEPA proves superior for upper-limb motor tasks. Notably, TimeSformer remains highly competitive for rhythmic tasks like finger tapping. Our experiments yield AUCs of 76.4 - 85.3% and accuracies of 71.5 - 80.6%. While high specificity (up to 90.3%) suggests strong potential for ruling out healthy individuals, the lower sensitivity (43.2 - 57.3%) highlights the need for task-aware calibration and integration of multiple tasks and modalities. Overall, this work establishes a rigorous baseline for VFM-based PD screening and provides a roadmap for selecting suitable tasks and architectures in remote neurological monitoring. Code and anonymized structured data are publicly available: https://anonymous.4open.science/r/parkinson\_video\_benchmarking-A2C5

cs.CV↗
arXiv2026

Are Stacked Intelligent Metasurfaces (SIMs) Better than Single-layer Reconfigurable Intelligent Surfaces (RISs) for Wideband Multi-user MIMO Communication Systems?

Cascaded or stacked intelligent metasurfaces (SIMs) have emerged as a promising technology to overcome the physical limitations of single-layer reconfigurable intelligent surfaces (RISs) in wideband wireless communication. By intelligently manipulating electromagnetic waves, SIMs enhance signal propagation in complex environments and offer additional degrees of freedom for beamforming. This paper proposes a coupling-aware, wideband, circuit-based framework that captures frequency-dependent mutual coupling and wideband channel responses over multiple subbands. Based on this model, we formulate a joint active and passive beamforming design that optimizes the base-station precoder to enable carrier aggregation across frequency-selective subbands, together with metasurface phase shifts, to maximize spectral efficiency. Simulation results reveal the importance of accounting for coupling and wideband effects, and show that performance depends strongly on operating conditions. Single-layer RIS configurations can be favorable in narrowband and/or low-SNR regimes, whereas SIMs can significantly outperform under wideband multi-user conditions by mitigating coupling-induced distortion and maintaining a more consistent phase response across frequencies. The results provide physical insights into design trade-offs between structural simplicity and wideband adaptability, highlighting SIMs as a scalable solution for future-generation wideband multi-user MIMO systems. We further show that partially reconfigurable SIM architectures achieve near-optimal performance with reduced complexity.

cs.IT↗
arXiv2026

Science-Informed Design of Deep Learning With Applications to Wireless Systems: A Tutorial

Recent advances in computational infrastructure and large-scale data processing have accelerated the adoption of data-driven inference methods, particularly deep learning (DL), to solve problems in many scientific and engineering domains. In wireless systems, DL has been applied to problems where analytical modeling or optimization is difficult to formulate, relies on oversimplified assumptions, or becomes computationally intractable. However, conventional DL models are often regarded as non-transparent, as their internal reasoning mechanisms are difficult to interpret even when model parameters are fully accessible. This lack of transparency undermines trust and leads to three interrelated challenges: limited interpretability, weak generalization, and the absence of a principled framework for parameter tuning. Science-informed deep learning (ScIDL) has emerged as a promising paradigm to address these limitations by integrating scientific knowledge into deep learning pipelines. This integration enables more precise characterization of model behavior and provides clearer explanations of how and why DL models succeed or fail. Despite growing interest, the existing literature remains fragmented and lacks a unifying taxonomy. This tutorial presents a structured overview of ScIDL methods and their applications in wireless systems. We introduce a structured taxonomy that organizes the ScIDL landscape, present two representative case studies illustrating its use in challenging wireless problems, and discuss key challenges and open research directions. The pedagogical structure guides readers from foundational concepts to advanced applications, making the tutorial accessible to researchers in wireless communications without requiring prior expertise in AI.

cs.IT↗
arXiv2026

Generalization Analysis and Method for Domain Generalization for a Family of Recurrent Neural Networks

Deep learning (DL) has driven broad advances across scientific and engineering domains. Despite its success, DL models often exhibit limited interpretability and generalization, which can undermine trust, especially in safety-critical deployments. As a result, there is growing interest in (i) analyzing interpretability and generalization and (ii) developing models that perform robustly under data distributions different from those seen during training (i.e. domain generalization). However, the theoretical analysis of DL remains incomplete. For example, many generalization analyses assume independent samples, which is violated in sequential data with temporal correlations. Motivated by these limitations, this paper proposes a method to analyze interpretability and out-of-domain (OOD) generalization for a family of recurrent neural networks (RNNs). Specifically, the evolution of a trained RNN's states is modeled as an unknown, discrete-time, nonlinear closed-loop feedback system. Using Koopman operator theory, these nonlinear dynamics are approximated with a linear operator, enabling interpretability. Spectral analysis is then used to quantify the worst-case impact of domain shifts on the generalization error. Building on this analysis, a domain generalization method is proposed that reduces the OOD generalization error and improves the robustness to distribution shifts. Finally, the proposed analysis and domain generalization approach are validated on practical temporal pattern-learning tasks.

cs.LG↗
arXiv2026

Joint Communication and Sensing in RIS-Assisted MIMO System Under Mutual Coupling

This paper considers a downlink Reconfigurable Intelligent Surface (RIS)-assisted Joint Communication and Sensing (JCAS) system within a physically-consistent setting, accounting for the effect of mutual coupling between RIS elements arising due to sub-element spacing. The system features a multiple-input multiple-output (MIMO) terrestrial base station (BS) and explores both monostatic and bistatic radar configurations to enable joint communication and sensing. In the monostatic configuration, both the transmitter and receiver are at the same location, while the bistatic configuration separates the transmitter and receiver spatially. System performance is evaluated using Fisher Information (FI) to quantify sensing accuracy and Mutual Information (MI) to measure communication efficiency. To achieve an optimal balance between communication and sensing, the RIS reflective coefficients and BS transmit beamforming are jointly optimized by maximizing a weighted sum of FI and MI. A novel solution approach is proposed for a single-user, single-object scenario, leveraging the mutual coupling model to enhance system realism. The impact of self-interference on sensing performance is also investigated through signal quantization. Numerical results reveal a fundamental trade-off between FI and MI and demonstrate that incorporating mutual coupling within a physically-consistent framework significantly improves both communication and sensing performance compared to conventional RIS-assisted JCAS models. Additionally, the analysis highlights how the choice of monostatic versus bistatic radar configuration affects system performance, offering valuable insights for the design of RIS-assisted JCAS systems.

cs.NI↗
arXiv2026

Multi-Objective Optimization for Joint Communication and Sensing in Multi-user MIMO Systems: Characterizing the Pareto Boundary

This paper investigates the Pareto boundary performance of a joint communication and sensing (JCAS) system that addresses both sensing and communication functions at the same time. In this scenario, a multiple-antenna base station (BS) transmits information to multiple single-antenna communication users while concurrently estimating the parameters of a single sensing object using the echo signal. We present an integrated beamforming approach for JCAS in a multi-user multiple-input and multiple-output (MIMO) system. The performance measures for communication and sensing are Fisher information (FI) and mutual information (MI). Our research considers two scenarios: multiple communication users with a single sensing object and a single communication user with a single sensing object. We formulate a multi-objective optimization problem to maximize the weighted sum of MI and FI, subject to a total transmit power budget for both cases. As a particular case, we address the equivalent isotropic radiated power (EIRP) for the single communication user scenario. We use the uplink-downlink duality for the multi-user case to simplify the problem and apply Lagrangian optimization and line search methods with a block-coordinate ascending technique. We use projected gradient descent (PGD) to solve the optimization problem in the single-user case. Our numerical results demonstrate that joint beamforming is optimal for the multi-user JCAS system, as opposed to independent beamforming for each user and the sensing object. Furthermore, we reveal the Pareto boundary for the multi-user case, with variations in the number of communication users and the number of transmitting and receiving antennas. We provide the Pareto boundary depending on EIRP limitations for the single-user case.

cs.NI↗
arXiv2025

Synchronization, Identification, and Signal Detection for Underwater Photon-Counting Communications With Input-Dependent Shot Noise

Photon counting (PhC) is an effective detection technology for underwater optical wireless communication (OWC) systems. The presence of signal-dependent Poisson shot noise and asynchronous multi-user interference (MUI) complicates the processing of received data signals, hindering the effective signal detection of PhC OWC systems. This paper proposes a novel iterative signal detection method in grant-free, multi-user, underwater PhC OWC systems with signal-dependent Poisson shot noise. We first introduce a new synchronization algorithm with a unique frame structure design.The algorithm performs active user identification and transmission delay estimation. Specifically, the estimation is performed first on a user group basis and then at the individual user level with reduced complexity and latency.We also develop a nonlinear iterative multi-user detection (MUD) algorithm that utilizes a detection window for each user to identify interfering symbols and estimate MUI on a slot-by-slot basis, followed by maximum \textit{a-posteriori} probability detection of user signals.Simulations demonstrate that our scheme achieves bit error rates comparable to scenarios with transmission delays known and signal detection perfectly synchronized.

eess.SY↗
arXiv2025

Non-Orthogonal Multiple-Access for Coherent-State Optical Quantum Communications Under Lossy Photon Channels

Coherent states have been increasingly considered in optical quantum communications (OQCs). With the inherent non-orthogonality of coherent states, non-orthogonal multiple-access (NOMA) naturally lends itself to the implementation of multi-user OQC. However, this remains unexplored in the literature. This paper proposes a novel successive interference cancellation (SIC)-based Kennedy receiver for uplink NOMA-OQC systems, along with a new approach for power allocation of the coherent states emitted by users. The key idea is to rigorously derive the asymptotic sum-rate of the considered systems, taking into account the impact of atmospheric turbulence, background noise, and lossy photon channel. With the asymptotic sum-rate, we optimize the average number of photons (or powers) of the coherent states emitted by the users. Variable substitution and successive convex approximation (SCA) are employed to convexify and maximize the asymptotic sum-rate iteratively. A new coherent-state power allocation algorithm is developed for a small-to-medium number of users. We further develop its low-complexity variant using adaptive importance sampling, which is suitable for scenarios with a medium-to-large number of users. Simulations demonstrate that our algorithms significantly enhance the sum-rate of uplink NOMA-OQC systems using coherent states by over 20\%, compared to their alternatives.

quant-ph↗