arXiv subjects

Wali Ullah Khan

Publications and source records attributed to Wali Ullah Khan.

At least 55 records · Page 3Linked to original sources

arXiv2025

Advancements in UAV-based Integrated Sensing and Communication: A Comprehensive Survey

Unmanned aerial vehicle (UAV)-based integrated sensing and communication (ISAC) systems are poised to revolutionize next-generation wireless networks by enabling simultaneous sensing and communication (S\&C). This survey comprehensively reviews UAV-ISAC systems, highlighting foundational concepts, key advancements, and future research directions. We explore recent advancements in UAV-based ISAC systems from various perspectives and objectives, including advanced channel estimation (CE), beam tracking, and system throughput optimization under joint sensing and communication S\&C constraints. Additionally, we examine weighted sum rate (WSR) and sensing trade-offs, delay and age of information (AoI) minimization, energy efficiency (EE), and security enhancement. These applications highlight the potential of UAV-based ISAC systems to improve spectrum utilization, enhance communication reliability, reduce latency, and optimize energy consumption across diverse domains, including smart cities, disaster relief, and defense operations. The survey also features summary tables for comparative analysis of existing methodologies, emphasizing performance, limitations, and effectiveness in addressing various challenges. By synthesizing recent advancements and identifying open research challenges, this survey aims to be a valuable resource for developing efficient, adaptive, and secure UAV-based ISAC systems.

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arXiv2025

RIS-Driven Resource Allocation Strategies for Diverse Network Environments: A Comprehensive Review

This comprehensive survey examines how Reconfigurable Intelligent Surfaces (RIS) revolutionize resource allocation in various network frameworks. It begins by establishing a theoretical foundation with an overview of RIS technologies, including passive RIS, active RIS, and Simultaneously Transmitting and Reflecting RIS (STAR-RIS). The core of the survey focuses on RIS's role in optimizing resource allocation within Single-Input Multiple-Output (SIMO), Multiple-Input Single-Output (MISO), and Multiple-Input Multiple-Output (MIMO) systems. It further explores RIS integration in complex network environments, such as Heterogeneous Wireless Networks (HetNets) and Non-Orthogonal Multiple Access (NOMA) frameworks. Additionally, the survey investigates RIS applications in advanced communication domains like Terahertz (THz) networks, Vehicular Communication (VC), and Unmanned Aerial Vehicle (UAV) communications, highlighting the synergy between RIS and Artificial Intelligence (AI) for enhanced network efficiency. Summary tables provide comparative insights into various schemes. The survey concludes with lessons learned, future research directions, and challenges, emphasizing critical open issues.

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arXiv2024

Holographic MIMO for Next Generation Non-Terrestrial Networks: Motivation, Opportunities, and Challenges

In this article, we propose the integration of the Holographic Multiple Input Multiple Output (HMIMO) as a transformative solution for next generation Non-Terrestrial Networks (NTNs), addressing key challenges, such as high hardware costs, launch expenses, and energy inefficiency. Traditional NTNs are constrained by the financial and operational limitations posed by bulky, costly antenna systems, alongside the complexities of maintaining effective communications in space. HMIMO offers a novel approach utilizing compact and lightweight arrays of densely packed radiating elements with real-time reconfiguration capabilities, thus, capable of optimizing system performance under dynamic conditions such as varying orbital dynamics and Doppler shifts. By replacing conventional antenna systems with HMIMO, the complexity and cost of satellite manufacturing and launch can be substantially reduced, enabling more streamlined and cost-effective satellite designs. This advancement holds significant potential to democratize space communications, making them accessible to a broader range of stakeholders, including smaller nations and commercial enterprises. Moreover, the inherent capabilities of HMIMO in enhancing energy efficiency, scalability, and adaptability position this technology as a key enabler of new use cases and sustainable satellite operations.

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arXiv2024

Beyond Diagonal RIS for 6G Non-Terrestrial Networks: Potentials and Challenges

Reconfigurable intelligent surface (RIS) has emerged as a promising technology in both terrestrial and non-terrestrial networks (NTNs) due to its ability to manipulate wireless environments for better connectivity. Significant studies have been focused on conventional RIS with diagonal phase response matrices. This simple RIS architecture, though less expensive, has limited flexibility in engineering the wireless channels. As the latest member of RIS technology, beyond diagonal RIS (BD-RIS) has recently been proposed in terrestrial setups. Due to the interconnected phase response elements (PREs), BD-RIS significantly enhances the control over the wireless environment. This work proposes the potential and challenges of BD-RIS in NTNs. We begin with the motivation and recent advances in BD-RIS. Subsequently, we discuss the fundamentals of BD-RIS and NTNs. We then outline the application of BD-RIS in NTNs, followed by a case study on BD-RIS enabled non-orthogonal multiple access low earth orbit satellite communication. Finally, we highlight challenges and research directions with concluding remarks.

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arXiv2024

Beyond Diagonal IRS Assisted Ultra Massive THz Systems: A Low Resolution Approach

The terahertz communications have the potential to revolutionize data transfer with unmatched speed and facilitate the development of new high-bandwidth applications. This paper studies the performance of downlink terahertz system assisted by beyond diagonal intelligent reconfigurable surface (BD-IRS). For enhanced energy efficiency and low cost, a joint precoding and BD-IRS phase shift design satisfying the $1$-bit resolution constraints to maximize the spectral efficiency is presented. The original problem is non-linear, NP-hard, and intricately coupled, and obtaining an optimal solution is challenging. To reduce the complexity, we first transform the optimization problem into two problems and then iteratively solve them to achieve an efficient solution. Numerical results demonstrate that the proposed approach for the BD-IRS assisted terahertz system significantly enhances the spectral efficiency compared to the conventional diagonal IRS assisted system.

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arXiv2024

CR-Enabled NOMA Integrated Non-Terrestrial IoT Networks with Transmissive RIS

This work proposes a T-RIS-equipped LEO satellite communication in cognitive radio-enabled integrated NTNs. In the proposed system, a GEO satellite operates as a primary network, and a T-RIS-equipped LEO satellite operates as a secondary IoT network. The objective is to maximize the sum rate of T-RIS-equipped LEO satellite communication using downlink NOMA while ensuring the service quality of GEO cellular users. Our framework simultaneously optimizes the total transmit power of LEO, NOMA power allocation for LEO IoT (LIoT) and T-RIS phase shift design subject to the service quality of LIoT and interference temperature to the primary GEO network. To solve the non-convex sum rate maximization problem, we first adopt successive convex approximations to reduce the complexity of the formulated optimization. Then, we divide the problem into two parts, i.e., power allocation of LEO and phase shift design of T-RIS. The power allocation problem is solved using KKT conditions, while the phase shift problem is handled by Taylor approximation and semidefinite programming. Numerical results are provided to validate the proposed optimization framework.

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arXiv2024

Joint Computation and Communication Resource Optimization for Beyond Diagonal UAV-IRS Empowered MEC Networks

Recent advancements in 6G systems signal a leap towards universal connectivity and ultra-reliable, low-latency communications for real-time data devices. Yet, these advancements encounter obstacles such as limited device battery life and computational power, along with urban signal blockages. To counter these, Intelligent Reconfigurable Surfaces (IRS) within Mobile Edge Cloud (MEC) infrastructures offer enhanced computing to overcome device limitations and create alternative communication paths. Despite these improvements, connectivity issues remain for remote areas. Our paper presents the Beyond Diagonal IRS (BD-IRS or IRS 2.0), integrated with UAVs in MEC networks (BD-IRS-UAV), providing on-demand links for remote users to offload tasks, tackling resource and battery limitations. We propose a joint optimization strategy to reduce system's worst-case latency and UAV hovering time by optimizing BD-IRS-UAV deployment and resource allocation. This challenge is approached by dividing it into two sub-problems: BD-IRS-UAV Placement and Computational Resource Optimization, and Communication Resource Optimization, each solved iteratively. This design significantly enhances system performance, showing a $17.75\%$ increase over traditional diagonal IRS and a $25.43\%$ improvement over IRS on buildings, with a $13.44\%$ enhancement in worst-case latency compared to binary offloading schemes.

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arXiv2024

Efficient Resource Allocation and User Association in NOMA-Enabled Vehicular-Aided HetNets with High Altitude Platforms

The increasing demand for massive connectivity and high data rates has made the efficient use of existing spectrum resources an increasingly challenging problem. Non-orthogonal multiple access (NOMA) is a potential solution for future heterogeneous networks (HetNets) due to its high capacity and spectrum efficiency. In this study, we analyze an uplink NOMA-enabled vehicular-aided HetNet, where multiple vehicular user equipment (VUEs) share the access link spectrum, and a high-altitude platform (HAP) communicates with roadside units (RSUs) through a backhaul communication link. We propose an improved algorithm for user association that selects VUEs for HAPs based on channel coefficient ratios and terrestrial VUEs based on a caching-state backhaul communication link. The joint optimization problems aim to maximize a utility function that considers VUE transmission rates and cross-tier interference while meeting the constraints of backhaul transmission rates and QoS requirements of each VUE. The joint resource allocation optimization problem consists of three sub-problems: bandwidth allocation, user association, and transmission power allocation. We derive a closed-form solution for bandwidth allocation and solve the transmission power allocation sub-problem iteratively using Taylor expansion to transform a non-convex term into a convex one. Our proposed three-stage iterative algorithm for resource allocation integrates all three sub-problems and is shown to be effective through simulation results. Specifically, the results demonstrate that our solution achieves performance improvements over existing approaches.

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arXiv2023

Reconfigurable Intelligent Surfaces for 6G Non-Terrestrial Networks: Assisting Connectivity from the Sky

This paper studies the potential of RIS-integrated NTNs to revolutionize the next-generation connectivity. First, it discusses the fundamentals of RIS technology. Secondly, it delves into reporting the recent advances in RIS-enabled NTNs. Subsequently, it presents a novel framework based on the current state-of-the-art for low earth orbit satellites (LEO) communications, wherein the signal received at the user terminal traverses both a direct link and an RIS link, and the RIS is mounted on a high-altitude platform (HAP) situated within the stratosphere. Finally, the paper concludes by highlighting open challenges and future research directions to revolutionize the realm of RIS-integrated NTNs.

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arXiv2023

Dynamic Resource Management in Integrated NOMA Terrestrial-Satellite Networks using Multi-Agent Reinforcement Learning

This study introduces a resource allocation framework for integrated satellite-terrestrial networks to address these challenges. The framework leverages local cache pool deployments and non-orthogonal multiple access (NOMA) to reduce time delays and improve energy efficiency. Our proposed approach utilizes a multi-agent enabled deep deterministic policy gradient algorithm (MADDPG) to optimize user association, cache design, and transmission power control, resulting in enhanced energy efficiency. The approach comprises two phases: User Association and Power Control, where users are treated as agents, and Cache Optimization, where the satellite (Bs) is considered the agent. Through extensive simulations, we demonstrate that our approach surpasses conventional single-agent deep reinforcement learning algorithms in addressing cache design and resource allocation challenges in integrated terrestrial-satellite networks. Specifically, our proposed approach achieves significantly higher energy efficiency and reduced time delays compared to existing methods.

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arXiv2023

Deep Reinforcement Learning for Backscatter Communications: Augmenting Intelligence in Future Internet of Things

Backscatter communication (BC) technology offers sustainable solutions for next-generation Internet-of-Things (IoT) networks, where devices can transmit data by reflecting and adjusting incident radio frequency signals. In parallel to BC, deep reinforcement learning (DRL) has recently emerged as a promising tool to augment intelligence and optimize low-powered IoT devices. This article commences by elucidating the foundational principles underpinning BC systems, subsequently delving into the diverse array of DRL techniques and their respective practical implementations. Subsequently, it investigates potential domains and presents recent advancements in the realm of DRL-BC systems. A use case of RIS-aided non-orthogonal multiple access BC systems leveraging DRL is meticulously examined to highlight its potential. Lastly, this study identifies and investigates salient challenges and proffers prospective avenues for future research endeavors.

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arXiv2023

Enhancing Congestion Control to Improve User Experience in IoT Using LSTM Network

This study suggests a new strategy for improving congestion control by deploying Long Short-Term Memory (LSTM) networks. LSTMs are recurrent neural networks (RNN), that excel at capturing temporal relationships and patterns in data. IoT-specific data such as network traffic patterns, device interactions, and congestion occurrences are gathered and analyzed. The gathered data is used to create and train an LSTM network architecture specific to the IoT environment. Then, the LSTM model's predictive skills are incorporated into the congestion control methods. This work intends to optimize congestion management methods using LSTM networks, which results in increased user satisfaction and dependable IoT connectivity. Utilizing metrics like throughput, latency, packet loss, and user satisfaction, the success of the suggested strategy is evaluated. Evaluation of performance includes rigorous testing and comparison to conventional congestion control methods.

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arXiv2023

Reconfigurable Intelligent Surfaces Enhanced NOMA D2D Communications Underlaying UAV Networks

Device-to-device (D2D) communications offers high spectral efficiency, low energy consumption and transmission latency. However, one of the main limitations of D2D communications is co-channel interference from underlaying wireless system. Reconfigurable intelligent surfaces (RIS) is a promising technology because it can manipulate the electromagnetic waves in their environment to overcome interference and enhance wireless communications. This paper considers RIS enhanced D2D communications underlaying unmanned aerial vehicle (UAV) networks with non-orthogonal multiple access (NOMA). The objective is to maximize the sum rate of NOMA D2D communications by simultaneously optimizing the power budget of D2D transmitter, NOMA power allocation coefficients of D2D receivers and passive beamforming of RIS while guaranteeing the quality of services of UAV user. Due to non-convexity, the optimization problem is intractable and challenging to handle. Therefore, it is solved in two parts using alternating optimization. Simulation results unviel the performance of the proposed RIS enhanced D2D communications scheme. Results demonstrate that the proposed scheme achieves 15\% and 27\% higher sum rates compared to the fixed power D2D and orthogonal D2D schemes.

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arXiv2023

RIS-Assisted Energy-Efficient LEO Satellite Communications with NOMA

This paper proposes an energy-efficient RIS-assisted downlink NOMA communication for LEO satellite networks. The proposed framework simultaneously optimizes the transmit power of ground terminals of the LEO satellite and the passive beamforming of RIS while ensuring the quality of services. Due to the nature of the considered system and optimization variables, the energy efficiency maximization problem is non-convex. In practice, obtaining the optimal solution for such problems is very challenging. Therefore, we adopt alternating optimization methods to handle the joint optimization in two steps. In step 1, for any given phase shift vector, we calculate satellite transmit power towards each ground terminal using the Lagrangian dual method. Then, in step 2, given the transmit power, we design passive beamforming for RIS by solving the semi-definite programming. We also compare our solution with a benchmark framework having a fixed phase shift design and a conventional NOMA framework without involving RIS. Numerical results show that the proposed optimization framework achieves 21.47\% and 54.9\% higher energy efficiency compared to the benchmark and conventional frameworks.

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arXiv2023

Integration of NOMA with Reflecting Intelligent Surfaces: A Multi-cell Optimization with SIC Decoding Errors

Reflecting intelligent surfaces (RIS) has gained significant attention due to its high energy and spectral efficiency in next-generation wireless networks. By using low-cost passive reflecting elements, RIS can smartly reconfigure the signal propagation to extend the wireless communication coverage. This letter proposes a new optimization scheme in a multi-cell RIS-NOMA network to enhance the spectral efficiency under signal decoding errors. In particular, the power budget of the base station and the transmit power of NOMA users while the reflection matrix of RIS is simultaneously optimized in each cell. Due to objective function and quality of service constraints, the joint problem is formulated as non-convex, which is very complex and challenging to obtain the optimal global solution. To make the problem tractable, we first decouple it into two sub-problems and transform it by inner approximation and successive convex approximation techniques. Then we adopt a standard optimization method for power allocation and DC programming for the reflection matrix. Numerical results demonstrate the benefits of the proposed optimization scheme in the multi-cell RIS-NOMA network.

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arXiv2023

Energy-Efficient RIS-Enabled NOMA Communication for 6G LEO Satellite Networks

This paper proposes an energy-efficient RIS-enabled NOMA communication for LEO satellite networks. The proposed framework simultaneously optimizes the transmit power of ground terminals at LEO satellite and passive beamforming at RIS while ensuring the quality of services. Due to the nature of the considered system and optimization variables, the problem of energy efficiency maximization is formulated as non-convex. In practice, it is very challenging to obtain the optimal solution for such problems. Therefore, we adopt alternating optimization methods to handle the joint optimization in two steps. In step 1, for any given phase shift vector, we calculate efficient power for ground terminals at satellite using Lagrangian dual method. Then, in step 2, given the transmit power, we design passive beamforming for RIS by solving the semi-definite programming. To validate the proposed solution, numerical results are also provided to demonstrate the benefits of the proposed optimization framework.

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arXiv2022

Swarm of UAVs for Network Management in 6G: A Technical Review

Fifth-generation (5G) cellular networks have led to the implementation of beyond 5G (B5G) networks, which are capable of incorporating autonomous services to swarm of unmanned aerial vehicles (UAVs). They provide capacity expansion strategies to address massive connectivity issues and guarantee ultra-high throughput and low latency, especially in extreme or emergency situations where network density, bandwidth, and traffic patterns fluctuate. On the one hand, 6G technology integrates AI/ML, IoT, and blockchain to establish ultra-reliable, intelligent, secure, and ubiquitous UAV networks. 6G networks, on the other hand, rely on new enabling technologies such as air interface and transmission technologies, as well as a unique network design, posing new challenges for the swarm of UAVs. Keeping these challenges in mind, this article focuses on the security and privacy, intelligence, and energy-efficiency issues faced by swarms of UAVs operating in 6G mobile networks. In this state-of-the-art review, we integrated blockchain and AI/ML with UAV networks utilizing the 6G ecosystem. The key findings are then presented, and potential research challenges are identified. We conclude the review by shedding light on future research in this emerging field of research.

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arXiv2022

Intelligent and Secure Radio Environments for 6G Vehicular Aided HetNets: Key Opportunities and Challenges

Reconfigurable meta-surfaces are emerging as a novel and revolutionizing technology to enable intelligent wireless environments. Due to the low cost, improved efficiency, and passive nature of reflecting elements, it is becoming possible to program and control the wireless environment. Since wireless physical layer technologies can generally adapt to the wireless environment, their combination with reconfigurable surfaces and deep learning approaches can open new avenues for achieving secure 6G vehicular aided heterogeneous networks (HetNets). Motivated by these appealing advantages, this work provides an intelligent and secure radio environment (ISRE) paradigm for 6G vehicular aided HetNets. We present an overview of enabling technologies for ISRE-based 6G vehicular aided HetNets. We discuss features, design goals, and applications of such networks. Next, we outline new opportunities provided by ISRE-based 6G vehicular HetNets and we present a case study using the contextual bandit approach in terms of best IRS for secure communications. Finally, we discuss some future research opportunities.

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