Owais Muhammad

Fifth-generation (5G) and sixth-generation (6G) wireless communications aim to achieve significantly higher data speeds, remarkably low latency, and substantial improvements in the efficiency of base stations. With the rapid increase in the utilization of broadband data driven by Internet of Things (IoT) gadgets, smart home systems, autonomous vehicles, and virtual reality devices, 5G and 6G networks are set to overcome the limitations of earlier telecommunication technologies and serve as key enablers for future IoT applications. Anticipated as the primary infrastructure for delivering emerging services, 5G cellular networks introduce new requirements and challenges that complicate the achievement of desired objectives. This paper provides a comprehensive overview of machine learning (ML) methods and their application in 5G and 6G wireless networks, covering supervised, unsupervised, and reinforcement learning (RL) approaches. ML is set to play a central and important role in 6G systems for these wireless networks. Subsequently, this paper thoroughly explores a series of challenges within the domain of 5G and 6G networks and examines research opportunities for applying ML techniques to address these challenges.

Unmanned Aerial Vehicle (UAV) path planning in unknown environments continues to pose a significant challenge, as Deep Reinforcement Learning (DRL) solutions are often severely hampered by slow convergence rates as well as unstable training dynamics. To address this gap, we introduce a Genetic Programming–seeded Soft Actor–Critic (GP+SAC) approach in which Genetic Programming produces high-quality trajectories that are introduced into the replay buffer of SAC as a “warm-start” policy to prevent wasteful early exploration. Through experiments in three benchmark grid environments, we demonstrate that GP+SAC converges significantly more rapidly than the FA-DQN baseline, achieving superior returns in fewer episodes while capitalizing on the same reward design. We show that in large environments, GP+SAC achieved a mean path length of 30.55 units as compared to FA-DQN’s 28.38, thus validating that rapid convergence has no tradeoff in path efficiency. Observably, results also show that as much as GP+SAC obtains superior cumulative rewards, there is a visible fluctuation in the level of training that is indicative of instabilities under very constrained environments. Numerical evaluations show that the proposed GP+SAC agent converges significantly faster than the FA-DQN baseline, achieving higher episodic returns within only a few episodes. In terms of path efficiency, GP+SAC yields an average path length of 30.55 units, which is comparable to the FA-DQN’s 28.38 units, demonstrating that accelerated convergence is achieved without sacrificing path optimality.

Energy harvesting wireless sensor networks (EH-WSNs) appear as the fundamental backbone of research that attempts to expand the lifespan and efficiency of sensor networks positioned in resource-constrained environments. This review paper provides an in-depth examination of latest developments in this area, highlighting the important components comprising routing protocols, energy management plans, cognitive radio applications, physical layer security (PLS), and EH approaches. Across a well-ordered investigation of these features, this article clarifies the notable developments in technology, highlights recent barriers, and inquires avenues for future revolution. This article starts by furnishing a detailed analysis of different energy harvesting methodologies, incorporating solar, thermal, kinetic, and radio frequency (RF) energy, and their respective efficacy in non-identical operational circumstances. It also inspects state-of-the-art energy management techniques aimed at optimizing energy consumption and storage to guarantee network operability. Moreover, the integration of cognitive radio into EH-WSNs is acutely assessed, highlighting its capacity to improve spectrum efficiency and tackle associated technological problems. The present work investigates ground-breaking methodologies in PLS that uses energy-harvesting measures to improve the data security. In this review article, these techniques are explored with respect to classical encryption and discussed from network security points of view as well.The assessment furthers criticizes traditional routing protocols and their significance in EH-WSNs as well as the balance that has long been sought between energy efficiency and security in this space. This paper closes with the importance of continuous research to tackle existing challenges and to leverage newly available means as highlighted in this document. In order to adequately serve the increasingly changing requirements of EH-WSNs, future research will and should be geared towards incorporating AI techniques with some advanced energy storage solutions. This paper discusses the integration of novel methodologies and interdisciplinary advancements for better performance, security, and sustainability for WSNs.

The rapid evolution of wireless technologies within the 5G network brings significant challenges in managing the increased connectivity and traffic of mobile devices. This enhanced connectivity brings challenges for base stations, which must handle increased traffic and efficiently serve a growing number of mobile devices. One of the key solutions to address these challenges is integrating device-to-device (D2D) communication with ultra-reliable and low-latency communication (URLLC). This study examines the impact of the Rician fading channel on the performance of D2D communication under URLLC. It addresses the critical problem of optimizing power allocation to maximize the minimum data rate in D2D communication. A significant challenge arises due to interference issues, as the problem of maximizing the minimum data rate is non-convex, which leads to high computational complexity. This complexity makes it difficult to derive optimal solutions efficiently. To address this challenge, we introduce an algorithm that is based on derivatives to find the optimal power allocation. Comparisons are made with the branch and bound (B&B) algorithm, heuristic algorithm, and particle swarm optimization (PSO) algorithm. Our proposed algorithm improves power allocation performance and also achieves faster execution with lower computational complexity compared to the B&B, PSO, and heuristic algorithms.

Energy harvesting wireless sensor networks (EH-WSNs) appear as the fundamental backbone of research that attempts to expand the lifespan and efficiency of sensor networks positioned in resource-constrained environments. This review paper provides a in-depth examination of latest developments in this area, highlighting the important components comprising routing protocols, energy management plans, cognitive radio applications, physical layer security (PLS), and EH approaches. Across a well-ordered investigation of these features, the article clarifies the notable developments in technology, highlights recent barriers, and inquires avenues for future revolution. The article starts by furnishing a detailed analysis of different energy harvesting methodologies, incorporating solar, thermal, kinetic, and radio frequency (RF) energy, and their respective efficacy in non-identical operational circumstances. It also inspects state-of-the-art energy management techniques aimed at optimizing energy consumption and storage to guarantee network operability. Moreover, the integration of cognitive radio into EH-WSNs is acutely assessed, highlighting its capacity to improve spectrum efficiency and tackle associated technological problems. The present work investigates ground-breaking methodologies in Physical Layer Security (PLS) that uses energy harvesting measures to improve the data security. In this review article, these techniques are explored with respect to classical encryption and discussed its as well the network security points of view. The assessment furthers criticizes traditional routing protocols and their significance in Energy Harvesting Wireless Sensor Networks (EH-WSNs) as well as the balance that has long been sought between energy efficiency and security in this space. The paper closes with the importance of continuous research to tackle existing challenges and to leverage newly available means as highlighted in this document. In order to adequately serve the increasingly changing requirements of EH-WSNs, future research will and should, be geared towards incorporating AI techniques with some advanced energy storage solutions. This paper discusses the integration of novel methodologies and interdisciplinary advancements for better performance, security and sustainability for WSNs.

Over the past two decades, numerous research projects have concentrated on cognitive radio wireless sensor networks (CR-WSNs) and their benefits. To tackle the problem of energy and spectrum shortfall in CR-WSNs, this research proposes an underpinning decode-&-forward (DF) relaying technique. Using the suggested time-slot architecture (TSA), this technique harvests energy from a multi-antenna power beam (PB) and delivers source information to the target utilizing energy-constrained secondary source and relay nodes. The study considers three proposed relay selection schemes: enhanced hybrid partial relay selection (E-HPRS), conventional opportunistic relay selection (C-ORS), and leading opportunistic relay selection (L-ORS). We present evidence for the sustainability of the suggested methods by examining the outage probability (OP) and throughput (TPT) under multiple primary users (PUs). These systems leverage time switching (TS) receiver design to increase end-to-end performance while taking into account the maximum interference constraint and transceiver hardware inadequacies. In order to assess the efficacy of the proposed methods, we derive the exact and asymptotic closed-form equations for OP and TPT & develop an understanding to learn how they affect the overall performance all across the Rayleigh fading channel. The results show that OP of the L-ORS protocol is 16% better than C-ORS and 75% better than E-HPRS in terms of transmitting SNR. The OP of L-ORS is 30% better than C-ORS and 55% better than E-HPRS in terms of hardware inadequacies at the destination. The L-ORS technique outperforms C-ORS and E-HPRS in terms of TPT by 4% and 11%, respectively.