arXiv subjects

Ekram Hossain

Publications and source records attributed to Ekram Hossain.

At least 37 records · Page 2Linked to original sources

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↗

arXiv2025

Non-Orthogonal Multiple Access-Based Continuous-Variable Quantum Key Distribution: Secret Key Rate Analysis and Power Allocation

We address the multi-user quantum key distribution (QKD) problem under malicious quantum attacks, which is critical for realizing a large-scale quantum Internet. This paper maximizes the sum secret key rate (SKR) of a novel uplink non-orthogonal multiple access based continuous-variable QKD (NOMA-CVQKD) system under collective attacks. The proposed system uses Gaussian-modulated coherent states and a quantum successive interference cancellation based heterodyne receiver. We derive closed-form asymptotic bounds for the legitimate users' achievable key rates via the entropy power inequality and maximum entropy principle, as well as for the eavesdropper's intercepted information based on Holevo information. A successive convex approximation based power allocation algorithm is developed to maximize the asymptotic sum SKR of the NOMA-CVQKD system under collective attacks, with guaranteed convergence to a locally optimal Karush-Kuhn-Tucker solution. Simulation results show that the proposed NOMA-CVQKD system with the power allocation algorithm achieves up to 23% higher sum SKR than quantum-orthogonal multiple access, supports 16 users at excess noise variance 0.1, and remains robust under varying turbulence intensities and transmission distances.

quant-ph↗

arXiv2025

Tool-Aided Evolutionary LLM for Generative Policy Toward Efficient Resource Management in Wireless Federated Learning

Federated Learning (FL) enables distributed model training across edge devices in a privacy-friendly manner. However, its efficiency heavily depends on effective device selection and high-dimensional resource allocation in dynamic and heterogeneous wireless environments. Conventional methods demand a confluence of domain-specific expertise, extensive hyperparameter tuning, and/or heavy interaction cost. This paper proposes a Tool-aided Evolutionary Large Language Model (T-ELLM) framework to generate a qualified policy for device selection in a wireless FL environment. Unlike conventional optimization methods, T-ELLM leverages natural language-based scenario prompts to enhance generalization across varying network conditions. The framework decouples the joint optimization problem mathematically, enabling tractable learning of device selection policies while delegating resource allocation to convex optimization tools. To improve adaptability, T-ELLM integrates a sample-efficient, model-based virtual learning environment that captures the relationship between device selection and learning performance, facilitating subsequent group relative policy optimization. This concerted approach reduces reliance on real-world interactions, minimizing communication overhead while maintaining high-fidelity decision-making. Theoretical analysis proves that the discrepancy between virtual and real environments is bounded, ensuring the advantage function learned in the virtual environment maintains a provably small deviation from real-world conditions. Experimental results demonstrate that T-ELLM outperforms benchmark methods in energy efficiency and exhibits robust adaptability to environmental changes.

cs.LG↗

arXiv2025

Integrated Sensing and Communication with UAV Swarms via Decentralized Consensus ADMM

UAV swarms can form virtual antenna arrays to exploit additional spatial degrees of freedom and enhance integrated sensing and communication (ISAC). The optimization of UAV positions is challenging due to the distributed nature of swarms and the lack of a global view at individual UAVs. This paper presents a new decentralized optimization framework that allows UAVs to decide their locations in parallel and reach consensus on a globally optimal swarm geometry for ISAC. Specifically, we derive the achievable uplink rate and Cramér-Rao Bound (CRB) as tractable metrics for communication and sensing, respectively. The UAV positions are optimized to balance maximizing the communication rate and minimizing the CRB. To solve this non-convex problem with coupled variables, we develop a decentralized consensus alternating direction method of multipliers (ADMM) algorithm, which enables the UAVs to iteratively align their local updates and reach consensus. The algorithm decomposes the global objective into local projection updates, proxy-assisted consensus coordination, and lightweight dual updates, ensuring scalability and consistency throughout the swarm. Simulations demonstrate that the proposed consensus ADMM algorithm converges rapidly with strong scalability, and that the UAV swarm significantly outperforms fixed-array baselines in both communication and sensing performance.

eess.SP↗

arXiv2025

Toward Hyper-Dimensional Connectivity in Beyond 6G: A Conceptual Framework

Cellular wireless networks enable mobile broadband connectivity for Internet-based applications through their radio access and core network infrastructure. While Fifth-Generation (5G) cellular systems are currently being deployed, ongoing research on cellular technologies primarily focuses on Sixth-Generation (6G) networks to set the stage for developing standards for these systems. Therefore, the time has come to articulate the visions for beyond 6G (B6G) systems. In this article, we present a visionary framework toward hyper-dimensional connectivity in B6G that enables wireless access to hyper-immersive Internet technologies. Our contributions include a conceptual framework for B6G cellular systems with jointly integrated communication, cognition, computing, and cyber-physical capabilities as core connectivity dimensions, a set of technical definitions outlining potential use cases and system-level requirements, a mapping of prospective technology enablers, and a forward-looking research agenda for B6G systems. The conceptual discussions in this article would be helpful for identifying innovation drivers, shaping long-term technical goals, and defining research agendas for the future of mobile broadband technologies.

cs.NI↗

arXiv2025

Dual-Function Beam Pattern Design for Multi-Target ISAC Systems: A Decoupled Approach

We investigate the beampattern design problem for mono-static multi-user (MU) multi-point-target integrated sensing and communication (ISAC) systems, where a dual-function multiple-input multiple-output (DF-MIMO) base station (BS) performs downlink communication and radar sensing simultaneously. In ISAC systems, sensing and communication inherently compete for resources. As communication demand increases, the beam pattern is reshaped, which might degrade the direction of arrival (DoA) sensing accuracy, measured in terms of mean-squared error (MSE) and lower-bounded by the Cramer-Rao lower bound (CRLB). Since conventional joint formulations of the sensing-based problem often overlook this trade-off, our work addresses it by decomposing the sensing-based problem into two subproblems (SPs). This decomposition enables a more effective exploitation of the beam pattern's physical properties, which we refer to as the Sensing-Guided Communication Dual-Function (SGCDF) beam pattern design. We further develop a low-complexity extension using the Riemannian Manifold Optimization (RMO) and convex closed-set projection. Simulation results confirm that the proposed method improves multi-target estimation accuracy, compared to traditional joint optimization strategies, by preserving the beam pattern, while the low-complexity version offers an excellent performance-complexity tradeoff, maintaining high accuracy with significantly reduced computational cost.

eess.SP↗

arXiv2025

Beamforming Control in RIS-Aided Wireless Communications: A Predictive Physics-Based Approach

Integrating reconfigurable intelligent surfaces (RIS) into wireless communication systems is a promising approach for enhancing coverage and data rates by intelligently redirecting signals, through a process known as beamforming. However, the process of RIS beamforming (or passive beamforming) control is associated with multiple latency-inducing factors. As a result, by the time the beamforming is effectively updated, the channel conditions may have already changed. For example, the low update rate of localization systems becomes a critical limitation, as a mobile UE's position may change significantly between two consecutive measurements. To address this issue, this work proposes a practical and scalable physics-based solution that is effective across a wide range of UE movement models. Specifically, we propose a kinematic observer and predictor to enable proactive RIS control. From low-rate position estimates provided by a localizer, the kinematic observer infers the UE's speed and acceleration. These motion parameters are then used by a predictor to estimate the UE's future positions at a higher rate, allowing the RIS to adjust promptly and compensate for inherent delays in both the RIS control and localization systems. Numerical results validate the effectiveness of the proposed approach, demonstrating real-time RIS adjustments with low computational complexity, even in scenarios involving rapid UE movement.

eess.SY↗

arXiv2025

A Minimalistic Approach to Predict and Understand the Relation of App Usage with Students' Academic Performances

Due to usage of self-reported data which may contain biasness, the existing studies may not unveil the exact relation between academic grades and app categories such as Video. Additionally, the existing systems' requirement for data of prolonged period to predict grades may not facilitate early intervention to improve it. Thus, we presented an app that retrieves past 7 days' actual app usage data within a second (Mean=0.31s, SD=1.1s). Our analysis on 124 Bangladeshi students' real-time data demonstrates app usage sessions have a significant (p<0.05) negative association with CGPA. However, the Productivity and Books categories have a significant positive association whereas Video has a significant negative association. Moreover, the high and low CGPA holders have significantly different app usage behavior. Leveraging only the instantly accessed data, our machine learning model predicts CGPA within 0.36 of the actual CGPA. We discuss the design implications that can be potential for students to improve grades.

cs.HC↗

arXiv2025

6G Cellular Networks: Mapping the Landscape for the IMT-2030 Framework

The IMT-2030 framework provides the vision and conceptual foundation for the next-generation of mobile broadband systems, colloquially known as Sixth-Generation (6G) cellular networks. Academic circles, industry players, and Standard Developing Organizations (SDOs) are already engaged in early standardization discussions for the system, providing key insights for future technical specifications. In this context, a structured thematic synthesis aligned with IMT-2030 is essential to inform the discussions and assist collaboration among 6G stakeholders -- including scholars, professionals, regulators, and SDO officials. This review intends to offer a concise yet informative synthesis of well-established 6G literature, viewed through the IMT-2030 lens, for both specialists and generalists engaged in shaping future standards and advancing 6G research.

cs.NI↗

arXiv2025

Koopman-Based Generalization of Deep Reinforcement Learning With Application to Wireless Communications

Deep Reinforcement Learning (DRL) is a key machine learning technology driving progress across various scientific and engineering fields, including wireless communication. However, its limited interpretability and generalizability remain major challenges. In supervised learning, generalizability is commonly evaluated through the generalization error using information-theoretic methods. In DRL, the training data is sequential and not independent and identically distributed (i.i.d.), rendering traditional information-theoretic methods unsuitable for generalizability analysis. To address this challenge, this paper proposes a novel analytical method for evaluating the generalizability of DRL. Specifically, we first model the evolution of states and actions in trained DRL algorithms as unknown discrete, stochastic, and nonlinear dynamical functions. Then, we employ a data-driven identification method, the Koopman operator, to approximate these functions, and propose two interpretable representations. Based on these interpretable representations, we develop a rigorous mathematical approach to evaluate the generalizability of DRL algorithms. This approach is formulated using the spectral feature analysis of the Koopman operator, leveraging the H_\infty norm. Finally, we apply this generalization analysis to compare the soft actor-critic method, widely recognized as a robust DRL approach, against the proximal policy optimization algorithm for an unmanned aerial vehicle-assisted mmWave wireless communication scenario.

cs.LG↗

arXiv2025

Convergence-Privacy-Fairness Trade-Off in Personalized Federated Learning

Personalized federated learning (PFL), e.g., the renowned Ditto, strikes a balance between personalization and generalization by conducting federated learning (FL) to guide personalized learning (PL). While FL is unaffected by personalized model training, in Ditto, PL depends on the outcome of the FL. However, the clients' concern about their privacy and consequent perturbation of their local models can affect the convergence and (performance) fairness of PL. This paper presents PFL, called DP-Ditto, which is a non-trivial extension of Ditto under the protection of differential privacy (DP), and analyzes the trade-off among its privacy guarantee, model convergence, and performance distribution fairness. We also analyze the convergence upper bound of the personalized models under DP-Ditto and derive the optimal number of global aggregations given a privacy budget. Further, we analyze the performance fairness of the personalized models, and reveal the feasibility of optimizing DP-Ditto jointly for convergence and fairness. Experiments validate our analysis and demonstrate that DP-Ditto can surpass the DP-perturbed versions of the state-of-the-art PFL models, such as FedAMP, pFedMe, APPLE, and FedALA, by over 32.71% in fairness and 9.66% in accuracy.

cs.LG↗

arXiv2025

Towards Quantum-Native Communication Systems: State-of-the-Art, Trends, and Challenges

The potential synergy between quantum communications and future wireless communication systems is explored. By proposing a quantum-native or quantum-by-design philosophy, the survey examines technologies such as quantumdomain (QD) multi-input multi-output, QD non-orthogonal multiple access, quantum secure direct communication, QD resource allocation, QD routing, and QD artificial intelligence. The recent research advances in these areas are summarized. Given the behavior of photonic and particle-like Terahertz (THz) systems, a comprehensive system-oriented perspective is adopted to assess the feasibility of using quantum communications in future systems. This survey also reviews quantum optimization algorithms and quantum neural networks to explore the potential integration of quantum communication and quantum computing in future systems. Additionally, the current status of quantum sensing, quantum radar, and quantum timing is briefly reviewed in support of future applications. The associated research gaps and future directions are identified, including extending the entanglement coherence time, developing THz quantum communications devices, addressing challenges in channel estimation and tracking, and establishing the theoretical bounds and performance trade-offs of quantum communication, computing, and sensing. This survey offers a unique perspective on the potential for quantum communications to revolutionize future systems and pave the way for even more advanced technologies.

quant-ph↗

arXiv2025

Route-and-Aggregate Decentralized Federated Learning Under Communication Errors

Decentralized federated learning (D-FL) allows clients to aggregate learning models locally, offering flexibility and scalability. Existing D-FL methods use gossip protocols, which are inefficient when not all nodes in the network are D-FL clients. This paper puts forth a new D-FL strategy, termed Route-and-Aggregate (R&A) D-FL, where participating clients exchange models with their peers through established routes (as opposed to flooding) and adaptively normalize their aggregation coefficients to compensate for communication errors. The impact of routing and imperfect links on the convergence of R&A D-FL is analyzed, revealing that convergence is minimized when routes with the minimum end-to-end packet error rates are employed to deliver models. Our analysis is experimentally validated through three image classification tasks and two next-word prediction tasks, utilizing widely recognized datasets and models. R&A D-FL outperforms the flooding-based D-FL method in terms of training accuracy by 35% in our tested 10-client network, and shows strong synergy between D-FL and networking. In another test with 10 D-FL clients, the training accuracy of R&A D-FL with communication errors approaches that of the ideal C-FL without communication errors, as the number of routing nodes (i.e., nodes that do not participate in the training of D-FL) rises to 28.

cs.DC↗

arXiv2025

Advancing Ubiquitous Wireless Connectivity through Channel Twinning

As an emerging trend in channel acquisition (CA), the concept of channel twinning (CT) has been proposed as a powerful enabler of ubiquitous connectivity in next-generation (xG) wireless systems. By fusing multimodal sensor data, CT advocates a high-fidelity and low-overhead CA paradigm, which is promising to provide accurate channel prediction in cross-domain and high-mobility scenarios of ubiquitous xG networks. However, existing literature lacks a universal CT architecture to address the challenges of heterogeneous scenarios, data, and resources in xG networks, which hinders the widespread deployment and applications of CT. This article discusses a new modularized CT architecture to bridge scene recognition, cooperative sensing, and decentralized training, comprising versatile model configuration, multimodal cooperative sensing, and lightweight twin modeling modules. Additionally, this article presents a detailed concept, technical features, and case studies of CT, outlines mainstream trends of realization methods, followed by potential applications of CT-empowered ubiquitous connectivity, and issues requiring future investigations.

eess.SP↗

arXiv2025

Decentralizing Trust: Consortium Blockchains and Hyperledger Fabric Explained

Trust models are essential components of networks of any nature, as they refer to confidence frameworks to evaluate and verify if their participants act reliably and fairly. They are necessary to any social, organizational, or computer network model to ensure truthful interactions, data integrity, and overall system resilience. Trust models can be centralized or distributed, each providing a good fair of benefits and challenges. Blockchain is a special case of distributed trust models that utilize advanced cryptographic techniques and decentralized consensus mechanisms to enforce confidence among participants within a network. In this piece, we provide an overview of blockchain networks from the trust model perspective, with a special focus on the Hyperledger Fabric framework, a widespread blockchain implementation with a consortium architecture. We explore Fabric in detail, including its trust model, components, overall architecture, and a general implementation blueprint for the platform. We intend to offer readers with technical backgrounds but not necessarily experts in the blockchain field a friendly review of these topics to spark their curiosity to continue expanding their knowledge on these increasingly popular technologies.

cs.DC↗

arXiv2025

Multi-Task Semantic Communication With Graph Attention-Based Feature Correlation Extraction

Multi-task semantic communication can serve multiple learning tasks using a shared encoder model. Existing models have overlooked the intricate relationships between features extracted during an encoding process of tasks. This paper presents a new graph attention inter-block (GAI) module to the encoder/transmitter of a multi-task semantic communication system, which enriches the features for multiple tasks by embedding the intermediate outputs of encoding in the features, compared to the existing techniques. The key idea is that we interpret the outputs of the intermediate feature extraction blocks of the encoder as the nodes of a graph to capture the correlations of the intermediate features. Another important aspect is that we refine the node representation using a graph attention mechanism to extract the correlations and a multi-layer perceptron network to associate the node representations with different tasks. Consequently, the intermediate features are weighted and embedded into the features transmitted for executing multiple tasks at the receiver. Experiments demonstrate that the proposed model surpasses the most competitive and publicly available models by 11.4% on the CityScapes 2Task dataset and outperforms the established state-of-the-art by 3.97% on the NYU V2 3Task dataset, respectively, when the bandwidth ratio of the communication channel (i.e., compression level for transmission over the channel) is as constrained as 1 12 .

cs.LG↗

arXiv2024

Distributed Traffic Control in Complex Dynamic Roadblocks: A Multi-Agent Deep RL Approach

Autonomous Vehicles (AVs) represent a transformative advancement in the transportation industry. These vehicles have sophisticated sensors, advanced algorithms, and powerful computing systems that allow them to navigate and operate without direct human intervention. However, AVs' systems still get overwhelmed when they encounter a complex dynamic change in the environment resulting from an accident or a roadblock for maintenance. The advanced features of Sixth Generation (6G) technology are set to offer strong support to AVs, enabling real-time data exchange and management of complex driving maneuvers. This paper proposes a Multi-Agent Reinforcement Learning (MARL) framework to improve AVs' decision-making in dynamic and complex Intelligent Transportation Systems (ITS) utilizing 6G-V2X communication. The primary objective is to enable AVs to avoid roadblocks efficiently by changing lanes while maintaining optimal traffic flow and maximizing the mean harmonic speed. To ensure realistic operations, key constraints such as minimum vehicle speed, roadblock count, and lane change frequency are integrated. We train and test the proposed MARL model with two traffic simulation scenarios using the SUMO and TraCI interface. Through extensive simulations, we demonstrate that the proposed model adapts to various traffic conditions and achieves efficient and robust traffic flow management. The trained model effectively navigates dynamic roadblocks, promoting improved traffic efficiency in AV operations with more than 70% efficiency over other benchmark solutions.

eess.SP↗

arXiv2024

A Novel Fusion Architecture for PD Detection Using Semi-Supervised Speech Embeddings

We present a framework to recognize Parkinson's disease (PD) through an English pangram utterance speech collected using a web application from diverse recording settings and environments, including participants' homes. Our dataset includes a global cohort of 1306 participants, including 392 diagnosed with PD. Leveraging the diversity of the dataset, spanning various demographic properties (such as age, sex, and ethnicity), we used deep learning embeddings derived from semi-supervised models such as Wav2Vec 2.0, WavLM, and ImageBind representing the speech dynamics associated with PD. Our novel fusion model for PD classification, which aligns different speech embeddings into a cohesive feature space, demonstrated superior performance over standard concatenation-based fusion models and other baselines (including models built on traditional acoustic features). In a randomized data split configuration, the model achieved an Area Under the Receiver Operating Characteristic Curve (AUROC) of 88.94% and an accuracy of 85.65%. Rigorous statistical analysis confirmed that our model performs equitably across various demographic subgroups in terms of sex, ethnicity, and age, and remains robust regardless of disease duration. Furthermore, our model, when tested on two entirely unseen test datasets collected from clinical settings and from a PD care center, maintained AUROC scores of 82.12% and 78.44%, respectively. This affirms the model's robustness and it's potential to enhance accessibility and health equity in real-world applications.

cs.SD↗