arXiv subjects

Bassant Selim

Publications and source records attributed to Bassant Selim.

14 recordsLinked to original sources

arXiv2026

Efficient Approximation of the Wigner Kernel in Phase-Space Quantum Mechanics

The Signed Particle Formulation provides a particle-based interpretation of quantum mechanics in phase space, where quantum dynamics are represented through the creation and evolution of signed particles. A central computational challenge in this framework is the evaluation of the Wigner kernel, which generally involves highly oscillatory integrals and can become computationally demanding in time-dependent simulations. This paper proposes an analytical approximation of the Wigner kernel for one-dimensional single body quantum systems by exploiting a series-based representation of the potential function. The resulting expression provides an efficient way to approximate the Wigner kernel and the associated Gamma function, which governs the particle-generation process in the Signed Particle Formulation framework. The proposed approximation is evaluated for several Gaussian-based potential profiles, including single, double, triple, and quadruple Gaussian potentials. Numerical comparisons between the approximated and directly computed Wigner kernels and Gamma functions show that the proposed method captures the main behavior of the exact quantities while significantly reducing the computational cost. These results indicate that the proposed approximation can serve as an efficient computational component for scalable Signed Particle Formulation based quantum simulations.

quant-ph↗

arXiv2026

Sequential Automorphism Ensemble Decoding with Early Stopping

In this paper, a low-complexity approach for the automorphism ensemble decoder (AED) using successive cancellation (SC) as constituent decoders is proposed. The approach sequentially activates sub-decoders and terminates the decoding process based on pre-optimized parameters, derived from the strong correlation observed between the decoding outcome and the SC path metric. An algorithm is proposed to find a list of early termination thresholds that minimize average decoding complexity subject to a block-error rate (BLER) constraint. For various code parameters and a BLER below $10^{-3}$, simulation results show that average decoding complexity is reduced by a factor of at least $6 \times$, and up to $22 \times$, compared to the original AED complexity, with a negligible degradation in BLER.

cs.IT↗

arXiv2025

Distributed Beamforming in Massive MIMO Communication for a Constellation of Airborne Platform Stations

Non-terrestrial base stations (NTBSs), including high-altitude platform stations (HAPSs) and hot-air balloons (HABs), are integral to next-generation wireless networks, offering coverage in remote areas and enhancing capacity in dense regions. In this paper, we propose a distributed beamforming framework for a massive MIMO network with a constellation of aerial platform stations (APSs). Our approach leverages an entropy-based multi-agent deep reinforcement learning (DRL) model, where each APS operates as an independent agent using imperfect channel state information (CSI) in both training and testing phases. Unlike conventional methods, our model does not require CSI sharing among APSs, significantly reducing overhead. Simulations results demonstrate that our method outperforms zero forcing (ZF) and maximum ratio transmission (MRT) techniques, particularly in high-interference scenarios, while remaining robust to CSI imperfections. Additionally, our framework exhibits scalability, maintaining stable performance over an increasing number of users and various cluster configurations. Therefore, the proposed method holds promise for dynamic and interference-rich NTBS networks, advancing scalable and robust wireless solutions.

eess.SY↗

arXiv2025

Beamforming for Massive MIMO Aerial Communications: A Robust and Scalable DRL Approach

This paper presents a distributed beamforming framework for a constellation of airborne platform stations (APSs) in a massive Multiple-Input and Multiple-Output (MIMO) non-terrestrial network (NTN) that targets the downlink sum-rate maximization under imperfect local channel state information (CSI). We propose a novel entropy-based multi-agent deep reinforcement learning (DRL) approach where each non-terrestrial base station (NTBS) independently computes its beamforming vector using a Fourier Neural Operator (FNO) to capture long-range dependencies in the frequency domain. To ensure scalability and robustness, the proposed framework integrates transfer learning based on a conjugate prior mechanism and a low-rank decomposition (LRD) technique, thus enabling efficient support for large-scale user deployments and aerial layers. Our simulation results demonstrate the superiority of the proposed method over baseline schemes including WMMSE, ZF, MRT, CNN-based DRL, and the deep deterministic policy gradient (DDPG) method in terms of average sum rate, robustness to CSI imperfection, user mobility, and scalability across varying network sizes and user densities. Furthermore, we show that the proposed method achieves significant computational efficiency compared to CNN-based and WMMSE methods, while reducing communication overhead in comparison with shared-critic DRL approaches.

eess.SP↗

arXiv2025

A Deep Multi-Task Learning Approach to Impulsive Noise Parameter Estimation

Impulsive noise poses a significant challenge to the reliability of wireless communication systems, necessitating accurate estimation of its statistical parameters for effective mitigation. This paper introduces a multitask learning (MTL) framework based on a CNN-LSTM architecture enhanced with an attention mechanism for the joint estimation of impulsive noise parameters. The proposed model leverages a unified weighted-loss function to enable simultaneous learning of multiple parameters within a shared representation space, improving learning efficiency and generalization across related tasks. Experimental results show that the proposed MTL framework achieves stable convergence, faster training, and enhanced scalability with modest computational overhead. Benchmarking against conventional single-task learning (STL) models confirms its favorable complexity-performance trade-off and significant memory savings, indicating the effectiveness of the MTL approach for real-time impulsive noise parameter estimation in wireless systems.

eess.SP↗

arXiv2025

Evaluation of Missing Data Imputation for Time Series Without Ground Truth

The challenge of handling missing data in time series is critical for maintaining the accuracy and reliability of machine learning (ML) models in applications like fifth generation mobile communication (5G) network management. Traditional methods for validating imputation rely on ground truth data, which is inherently unavailable. This paper addresses this limitation by introducing two statistical metrics, the wasserstein distance (WD) and jensen-shannon divergence (JSD), to evaluate imputation quality without requiring ground truth. These metrics assess the alignment between the distributions of imputed and original data, providing a robust method for evaluating imputation performance based on internal structure and data consistency. We apply and test these metrics across several imputation techniques. Results demonstrate that WD and JSD are effective metrics for assessing the quality of missing data imputation, particularly in scenarios where ground truth data is unavailable.

cs.LG↗

arXiv2025

Beyond Diagonal RIS for ISAC Network: Statistical Analysis and Network Parameter Estimation

This paper investigates the use of beyond diagonal reconfigurable intelligent surface (BD-RIS) with $N$ elements to advance integrated sensing and communication (ISAC). We address a key gap in the statistical characterizations of the radar signal-to-noise ratio (SNR) and the communication signal-to-interference-plus-noise ratio (SINR) by deriving tractable closed-form cumulative distribution functions (CDFs) for these metrics. Our approach maximizes the radar SNR by jointly configuring radar beamforming and BD-RIS phase shifts. Subsequently, zero-forcing is adopted to mitigate user interference, enhancing the communication SINR. To meet ISAC outage requirements, we propose an analytically-driven successive non-inversion sampling (SNIS) algorithm for estimating network parameters satisfying network outage constraints. Numerical results illustrate the accuracy of the derived CDFs and demonstrate the effectiveness of the proposed SNIS algorithm.

eess.SP↗

arXiv2024

Multi-UAV Speed Control with Collision Avoidance and Handover-aware Cell Association: DRL with Action Branching

This paper presents a deep reinforcement learning solution for optimizing multi-UAV cell-association decisions and their moving velocity on a 3D aerial highway. The objective is to enhance transportation and communication performance, including collision avoidance, connectivity, and handovers. The problem is formulated as a Markov decision process (MDP) with UAVs' states defined by velocities and communication data rates. We propose a neural architecture with a shared decision module and multiple network branches, each dedicated to a specific action dimension in a 2D transportation-communication space. This design efficiently handles the multi-dimensional action space, allowing independence for individual action dimensions. We introduce two models, Branching Dueling Q-Network (BDQ) and Branching Dueling Double Deep Q-Network (Dueling DDQN), to demonstrate the approach. Simulation results show a significant improvement of 18.32% compared to existing benchmarks.

cs.LG↗

arXiv2023

The Journey Towards 6G: A Digital and Societal Revolution in the Making

While the fifth generation (5G) is bringing an innovative fabric of breakthrough technologies, enabling smart factories, cities, and Internet-of-Things (IoT), the unprecedented strain on communication networks put by these applications, in terms of highly cognitive, agile architectures and the support of massive connectivity, energy efficiency, and extreme ultralow latency, is pushing 5G to their limits. As such, the focus of academic and industrial efforts has shifted toward beyond 5G (B5G) and the conceptualization of sixth generation (6G) systems. This article discusses four main digital and societal use cases (UCs) that will drive the need to reconcile a new breed of network requirements. Based on this, we provide our vision of the fundamental architectural ingredients that will enable the promise of 6G networks of bringing the unification of experiences across the digital, physical, and human worlds. We outline key disruptive technological paradigms that will support 6G materialize a bouquet of unique expectations and redefine how we live and protect our planet. Finally, we adopt the recently envisaged ecosystem of the Internet-of-Musical Things (IoMusT) to depict how the discussed UCs and technological paradigms may be exploited to realize this ecosystem.

cs.NI↗

arXiv2021

Intelligent Reflecting Surfaces for Enhanced NOMA-based Visible Light Communications

The emerging intelligent reflecting surface (IRS) technology introduces the potential of controlled light propagation in visible light communication (VLC) systems. This concept opens the door for new applications in which the channel itself can be altered to achieve specific key performance indicators. In this paper, for the first time in the open literature, we investigate the role that IRSs can play in enhancing the link reliability in VLC systems employing non-orthogonal multiple access (NOMA). We propose a framework for the joint optimisation of the NOMA and IRS parameters and show that it provides significant enhancements in link reliability. The enhancement is even more pronounced when the VLC channel is subject to blockage and random device orientation.

cs.IT↗

arXiv2021

Intelligent Link Adaptation for Grant-Free Access Cellular Networks: A Distributed Deep Reinforcement Learning Approach

With the continuous growth of machine-type devices (MTDs), it is expected that massive machine-type communication (mMTC) will be the dominant form of traffic in future wireless networks. Applications based on this technology, have fundamentally different traffic characteristics from human-to-human (H2H) communication, which involves a relatively small number of devices transmitting large packets consistently. Conversely, in mMTC applications, a very large number of MTDs transmit small packets sporadically. Therefore, conventional grant-based access schemes commonly adopted for H2H service, are not suitable for mMTC, as they incur in a large overhead associated with the channel request procedure. We propose three grant-free distributed optimization architectures that are able to significantly minimize the average power consumption of the network. The problem of physical layer (PHY) and medium access control (MAC) optimization in grant-free random access transmission is is modeled as a partially observable stochastic game (POSG) aimed at minimizing the average transmit power under a per-device delay constraint. The results show that the proposed architectures are able to achieve significantly less average latency than a baseline, while spending less power. Moreover, the proposed architectures are more robust than the baseline, as they present less variance in the performance for different system realizations.

cs.MA↗

arXiv2020

Effect of Impulsive Noise on Uplink NOMA Systems

Non-orthogonal multiple access (NOMA) was recently proposed as a viable technology that can potentially provide the spectral efficiency, low latency, and massive connectivity requirements of future radio networks. In this context, numerous ultra-high reliability technologies such as the industrial Internet of things, smart grids, and smart homes present environments which are characterized by the presence of impulsive electromagnetic interference, known as impulsive noise. Under such conditions, the power domain multiplexing in NOMA is expected to render the system particularly sensitive to this additional impulsive noise. Therefore, in this article, we quantify the effects of impulsive noise on the outage performance of uplink NOMA systems. Extensive Monte-Carlo simulations as well as offered analytical results demonstrate the vulnerability of the involved NOMA users to this type of noise. This highlights the need for effective modeling of the impulsive noise as well as the design of mitigation techniques that are suitable for the particular demands and challenges of NOMA.

eess.SP↗

arXiv2017

Performance Analysis of Coherent and Noncoherent Modulation under I/Q Imbalance

In-phase/quadrature-phase Imbalance (IQI) is considered a major performance-limiting impairment in direct-conversion transceivers. Its effects become even more pronounced at higher carrier frequencies such as the millimeter-wave frequency bands being considered for 5G systems. In this paper, we quantify the effects of IQI on the performance of different modulation schemes under multipath fading channels. This is realized by developing a general framework for the symbol error rate (SER) analysis of coherent phase shift keying, noncoherent differential phase shift keying and noncoherent frequency shift keying under IQI effects. In this context, the moment generating function of the signal-to-interference-plus-noise-ratio is first derived for both single-carrier and multi-carrier systems suffering from transmitter (TX) IQI only, receiver (RX) IQI only and joint TX/RX IQI. Capitalizing on this, we derive analytic expressions for the SER of the different modulation schemes. These expressions are corroborated by comparisons with corresponding results from computer simulations and they provide insights into the dependence of IQI on the system parameters. We demonstrate that the effects of IQI differ considerably depending on the considered system as some cases of single-carrier transmission appear robust to IQI, whereas multi-carrier systems experiencing IQI at the RX require compensation in order to achieve a reliable communication link.

eess.SP↗

arXiv2015

Modeling and Analysis of Wireless Channels via the Mixture of Gaussian Distribution

Considerable efforts have been devoted to statistical modeling and the characterization of channels in a range of statistical models for fading channels. In this paper, we consider a unified approach to model wireless channels by the mixture of Gaussian (MoG) distribution. Simulations provided have shown the new probability density function to accurately characterize multipath fading as well as composite fading channels. We utilize the well known expectation-maximization algorithm to estimate the parameters of the MoG model and further utilize the Kullback-Leibler divergence and the mean square error criteria to demonstrate that our model provides both high accuracy and low computational complexity, in comparison with existing results. Additionally, we provide closed form expressions for several performance metrics used in wireless communication systems, including the moment generating function, the raw moments, the amount of fading, the outage probability, the average channel capacity, and the probability of energy detection for cognitive radio. Numerical Analysis and Monte-Carlo simulations are presented to corroborate the analytical results and to provide detailed performance comparisons with the other models in the literature.

cs.IT↗