Reconfigurable Intelligent Surfaces Enhanced NOMA D2D Communications Underlaying UAV Networks

E-print/Working paper (2023)

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 ... [more ▼]

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 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 ach [less ▲]

NOMA-Based Backscatter Communications: Fundamentals, Applications, and Advancements

E-print/Working paper (2023)

Developing wireless communication technologies is an ongoing process to satisfy the requirements of new applications and the increasing proliferation of interconnected devices. Using non-orthogonal ... [more ▼]

Developing wireless communication technologies is an ongoing process to satisfy the requirements of new applications and the increasing proliferation of interconnected devices. Using non-orthogonal multiple access (NOMA) and backscatter communication (BC) has surfaced as an advantageous approach for enhancing energy efficiency (EE), maximizing sum rates, ensuring security, and optimizing resource allocation. NOMA permits multiple users to share time and frequency resources even without the requirement of antenna arrays, whereas BC employs ambient RF signals for low-power communication. By integrating the advantages of NOMA and BC, NOMA-based BC provides a solution for future energy-efficient and low-power networks. Despite its potential, there is a lack of a comprehensive overview of NOMA-BC, necessitating a systematic survey that covers its principles, applications, challenges, and future directions. This survey aims to bridge the gap by exploring NOMA-BC within B5G and 6G networks. We delve into its technical aspects, performance optimization techniques, and real-world applications to enhance understanding and knowledge. First, we cover topics such as enhancing EE, maximizing the sum rates, ensuring security, and optimizing resource allocation. Our primary goal is to provide researchers and practitioners with valuable insights that enable them to grasp the capabilities and benefits of NOMA-BC. To achieve this, we comprehensively analyze various schemes' performance by presenting detailed summary tables. These analyses cover a range of scenarios, methods, and objectives, focusing on emerging B5G technologies such as reconfigurable intelligent surfaces (RIS), visible light communication (VLC), and unmanned aerial vehicle (UAV) communication. By examining NOMA-BC's effectiveness within these contexts, we aim to provide a holistic view of its potential and applicability in diverse technological domains. Moreover, our survey identifies and discusses open research challenges and proposes future directions to guide researchers toward unexplored areas and facilitate advancements in NOMA-BC. [less ▲]

Joint optimization of UAV-IRS placement and resource allocation for wireless powered mobile edge computing networks

in Journal of King Saud University - Computer and Information Sciences (2023), 35(8), 101646

The rapid evolution of communication systems towards the next generation has led to an increased deployment of Internet of Things (IoT) devices for various real-time applications. However, these devices ... [more ▼]

The rapid evolution of communication systems towards the next generation has led to an increased deployment of Internet of Things (IoT) devices for various real-time applications. However, these devices often face limitations in terms of processing power and battery life, which can hinder overall system performance. Additionally, applications such as augmented reality and surveillance require intensive computations within tight timeframes. This research focuses on investigating a mobile edge computing (MEC) network empowered by unmanned aerial vehicle intelligent reflecting surfaces (UAV-IRS) to enhance the computational energy efficiency of the system through optimized resource allocation. The MEC infrastructure incorporates the energy transfer circuit (ETC) and edge server (ES), co-located with the intelligent access point (AP). To eliminate interference between energy transfer and data transmission, a time-division multiple access method is utilized. In the first phase, the ETC wirelessly transfers power to low-power IoT devices, which efficiently harvest and store the received energy in their batteries. In the second phase, IoT devices employ the stored energy for local computing or offloading tasks. Furthermore, the presence of tall buildings may obstruct communication routes, impacting system functionality. To address these challenges, we propose an optimization framework that simultaneously considers time, power, phase shift design, and local computational resources. This joint optimization problem is non-convex and non-linear, making it NP-hard. To tackle this complexity, we decompose the problem into subproblems and solve them iteratively using a convex optimization toolbox like CVX. Through simulations, we demonstrate that our proposed optimization framework significantly improves 40.7% system performance compared to alternative approaches. [less ▲]

Energy-Efficient Beamforming and Resource Optimization for STAR-IRS Enabled Hybrid-NOMA 6G Communications

in IEEE Transactions on Green Communications and Networking (2023)

In this manuscript, we propose an energy-efficient optimization framework for a multi-cluster simultaneous transmitting and reflecting intelligent reflecting surfaces (STAR-IRS) enabled time-division ... [more ▼]

In this manuscript, we propose an energy-efficient optimization framework for a multi-cluster simultaneous transmitting and reflecting intelligent reflecting surfaces (STAR-IRS) enabled time-division multiple-access (TDMA) based hybrid-NOMA system to realize the future sixth-generation (6G) wireless communication systems. Specifically, the energy-efficiency maximization is achieved by optimizing the successive-interference cancellation (SIC) decoding order, time-allocation, and active-beamforming vectors at the transmitter, as well as transmission and reflection coefficients at the STAR-IRS under quality-of-service (QoS), conservation of energy, time-allocation, phase-shifts, and SIC-decoding constraints. Moreover, the proposed alternating optimization algorithm tackles the considered highly non-convex optimization problem in four steps. In first step, for computing the SIC-decoding order of NOMA users, an efficient optimization technique is proposed which maximizes the sum of combined channel gains by optimizing the transmission and reflection beamforming vectors of the considered STAR-IRS assisted hybrid-NOMA system. Further, in second step, an optimal time-allocation for each cluster in transmission and reflection region is computed for given SIC-decoding order. With decoding order and time-allocation in hand, active-beamforming vectors are computed by exploiting the sequential-convex approximation (SCA) and second-order-conic programming (SOCP) in third step. Finally, in the fourth step, the transmission and reflection coefficients of STAR-IRS are computed by transforming the non-convex optimization problem into a semi-definite programming (SDP) problem. Th numerical simulation results demonstrate that the proposed optimization framework exhibits an efficient energy efficiency performance and converges within a few iterations. [less ▲]

Vehicular Communication Network Enabled CAVData Offloading: A Review

in IEEE Transactions on Intelligent Transportation Systems (2023)

The connected and autonomous vehicles (CAV) applications and services-based traffic make an extra burden on the already congested cellular networks. Offloading is envisioned as a promising solution to ... [more ▼]

The connected and autonomous vehicles (CAV) applications and services-based traffic make an extra burden on the already congested cellular networks. Offloading is envisioned as a promising solution to tackle cellular networks' traffic explosion problem. Notably, vehicular traffic offloading leveraging different vehicular communication network (VCN) modes is one of the potential techniques to address the data traffic problem in cellular networks. This paper surveys the state-of-the-art literature for vehicular data offloading under a communication perspective, i.e., vehicle to vehicle (V2V), vehicle to roadside infrastructure (V2I), and vehicle to everything (V2X). First, we pinpoint the significant classification of vehicular data/traffic offloading techniques, considering whether data is to download or upload. Next, for better intuition of each data offloading's category, we sub-classify the existing schemes based on their objectives. Then, the existing literature on vehicular data/traffic is elaborated, compared, and analyzed based on approaches, objectives, merits, demerits, etc. Finally, we highlight the open research challenges in this field and predict future research trends. [less ▲]

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

in IEEE Transactions on Green Communications and Networking (2023)

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 ... [more ▼]

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. On the other hand, non-orthogonal multiple access (NOMA) has been proven as a key air interface technique for supporting massive connections over limited resources. Utilizing the superposition coding and successive interference cancellation (SIC) techniques, NOMA can multiplex multiple users over the same spectrum and time resources by allocating different power levels. This paper proposes a new optimization scheme in a multi-cell RIS-NOMA network to enhance the spectral efficiency under SIC decoding errors. In particular, the power budget of the base station and the transmit power of NOMA users while the passive beamforming 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 reduce the complexity and make the problem tractable, we first decouple the original problem into two sub-problems for power allocation and passive beamforming. Then, the efficient solution of each sub-problem is obtained in two-steps. In the first-step of For power allocation sub-problem, we transform it to a convex problem by inner approximation method and then solve it through a standard convex optimization solver in the second-step. Accordingly, in the first-step of passive beamforming, it is transformed to a standard semidefinite programming problem by successive convex approximation and different of convex programming methods. Then, penalty based method is used to achieve a Rank-1 solution for passive beamforming in second-step. Numerical results demonstrate the benefits of the proposed optimization scheme in the multi-cell RIS-NOMA network. [less ▲]

DRL-Assisted Delay Optimized Task Offloading in Automotive-Industry 5.0 based VECNs

in Journal of King Saud University - Computer and Information Sciences (2023)

The rapid growth of Automotive-Industry 5.0 and its emergence with beyond fifth-generation (B5G) communications, is making vehicular edge computing networks (VECNs) increasingly complex. The latency ... [more ▼]

The rapid growth of Automotive-Industry 5.0 and its emergence with beyond fifth-generation (B5G) communications, is making vehicular edge computing networks (VECNs) increasingly complex. The latency constraints of modern automotive applications make it difficult to run complex applications on vehicle on-board units (OBUs). While multi-access edge computing (MEC) can facilitate task offloading to execute these applications, it is still a challenge to access them promptly and optimally. Traditional algorithms struggle to guarantee accuracy in such dynamic environment, but deep reinforcement learning (DRL) methods offer improved accuracy, robustness, and real-time decision-making capabilities. In this paper, we propose a DRL-based mobility, contact, and load aware cooperative task offloading (DCTO) scheme. DCTO is designed for both cellular and mmWave radio access technologies (RATs), and both binary and partial offloading mechanisms. DCTO targets delay minimization by opportunistically switching RATs and offloading mechanisms. We consider relative efficacy and neutrality factors as key performance indicators and use them to derive the DRL agent’s reward function. Extensive evaluations demonstrate that the DCTO scheme exhibits a substantial enhancement in task success rate, with an increase from 2.61% to 21.34%. It also improves the efficacy factor from 1.38 to 3.52 and reduces the neutrality factor from 4.99 to 0.76. Furthermore, the average task processing time is reduced by a range of 3.77% to 24.15%. Additionally, the DCTO scheme outperforms the other evaluated schemes in terms of reward and TFPS ratio. [less ▲]

Emission-aware Resource Optimization Framework for Backscatter-enabled Uplink NOMA Networks

in Low Electromagnetic Field Exposure Wireless Devices: Fundamentals and Recent Advances (2023)

In the last decade, a sharp surge in the number of user proximity wireless devices (UPWDs) has been observed. This has increased the level of electromagnetic field (EMF) exposure of the users ... [more ▼]

In the last decade, a sharp surge in the number of user proximity wireless devices (UPWDs) has been observed. This has increased the level of electromagnetic field (EMF) exposure of the users substantially and hence, the possible physiological effects. Ambient backscatter communications (ABC) has appeared to be a promising solution to reduce the power consumption of UPWDs by converting ambient radio frequency (RF) signals into useful signals while non‐orthogonal multiple access (NOMA) is a compelling multiplexing scheme for enhanced spectral efficiency. This chapter utilizes a novel combination of ABC and NOMA to reduce the EMF in the uplink of wireless communication systems. This contemporary approach of EMF‐aware resource optimization is based on k‐medoids and Silhouette analysis. To curtail the uplink EMF, a power allocation strategy is also derived by converting a non‐convex problem to a convex one and solving accordingly. The numerical results exhibit that the proposed ABC, NOMA, and unsupervised learning based scheme achieves a reduction in the EMF by at least 75% in comparison with the existing solutions. [less ▲]

Ergodic Performance Analysis of Double Intelligent Reflecting Surfaces-Aided NOMA–UAV Systems with Hardware Impairment

in Drones (2022)

In this work, we design an intelligent reflecting surface (IRS)-assisted Internet of Things (IoT) by enabling non-orthogonal multiple access (NOMA) and unmanned aerial vehicles (UAV) approaches. We pay ... [more ▼]

In this work, we design an intelligent reflecting surface (IRS)-assisted Internet of Things (IoT) by enabling non-orthogonal multiple access (NOMA) and unmanned aerial vehicles (UAV) approaches. We pay attention to studying the achievable rates for the ground users. A practical system model takes into account the presence of hardware impairment when Rayleigh and Rician channels are adopted for the IRS–NOMA–UAV system. Our main findings are presented to showcase the exact expressions for achievable rates, and then we derive their simple approximations for a more insightful performance evaluation. The validity of these approximations is demonstrated using extensive Monte Carlo simulations. We confirm the achievable rate improvement decided by main parameters such as the average signal to noise ratio at source, the position of IRS with respect to the source and destination and the number of IRS elements. As a suggestion for the deployment of a low-cost IoT system, the double-IRS model is a reliable approach to realizing the system as long as the hardware impairment level is controlled. The results show that the proposed scheme can greatly improve achievable rates, obtain optimal performance at one of two devices and exhibit a small performance gap compared with the other benchmark scheme. [less ▲]

Optimizing Computational and Communication Resources for MEC Network Empowered UAV-RIS Communication

Scientific Conference (2022, December)

With the technological evolution and new applications, user equipment (UEs) has become a vital part of our lives. However, limited computational capabilities and finite battery life bottleneck the ... [more ▼]

With the technological evolution and new applications, user equipment (UEs) has become a vital part of our lives. However, limited computational capabilities and finite battery life bottleneck the performance of computationally demanding applications. A practical solution to enhance the quality of experience (QoE) is to offload the extensive computation to the mobile edge cloud (MEC). Moreover, the network’s performance can be further improved by deploying an unmanned aerial vehicle (UAV) integrated with intelligent reflective surfaces (IRS): an effective alternative to massive antenna systems to enhance the signal quality and suppress interference. In this work, the MEC network architecture is assisted by UAV-IRS to provide computational services to the UEs. To do so, a cost minimization problem in terms of computing time and hovering energy consumption is formulated. Furthermore, to achieve an efficient solution to a formulated challenging problem, the original optimization problem is decoupled into sub-problems using the block-coordinate decent method. Moreover, numerical results are compared to baseline schemes to determine the effectiveness of the proposed scheme. Simulation results demonstrate that the optimal allocation of local computational resources results in minimizing tasks’ computational time and hovering energy consumption. [less ▲]