IRS-Assisted Downlink and Uplink NOMA in Wireless Powered Communication Networks

Cantos

et al. 2022

Sensors

Recently, intelligent reflecting surfaces (IRSs) have drawn huge attention as a promising solution for 6G networks to enhance diverse performance metrics in a cost-effective way. For massive connectivity toward a higher spectral efficiency, we address an intelligent reflecting surface (IRS) to an uplink nonorthogonal multiple access (NOMA) network supported by a multiantenna receiver. We maximize the sum rate of the IRS-aided NOMA network by optimizing the IRS reflection pattern under unit modulus and practical reflection. For a moderate-sized IRS, we obtain an upper bound on the optimal sum rate by solving a determinant maximization (max-det) problem after rank relaxation, which also leads to a feasible solution through Gaussian randomization. For a large number of IRS elements, we apply the iterative algorithms relying on the gradient, such as Broyden–Fletcher–Goldfarb–Shanno (BFGS) and limited-memory BFGS algorithms for which the gradient of the sum rate is derived in a computationally efficient form. The results show that the max-det approach provides a near-optimal performance under unit modulus reflection, while the gradient-based iterative algorithms exhibit merits in performance and complexity for a large-sized IRS with practical reflection.

Section: Gradient-based Iterative Algorithms For a Large-sized Irsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

Sum Rate Optimization of IRS-Aided Uplink Muliantenna NOMA with Practical Reflection

Cantos

et al. 2022

Sensors

Unified performance analysis with exact solutions for uplink non‐orthogonal multiple access system‐based cellular networks

Serhat Bulut,

Ilhan,

Gatera

2023

Int J Communication

SummaryThis research presents a unified performance analysis methodology for the power domains of uplink non‐orthogonal multiple access (NOMA) system consisting of a base station and an arbitrary number of users over Rayleigh fading channels. The study derives exact closed‐form expressions for key performance metrics, such as outage probability (OP), symbol error rate (SER), outage capacity (OC), average channel capacity (AVC), and amount of fading, using binary phase shift keying (BPSK) and quadrature phase shift keying (QPSK) modulations. The analysis encompasses all necessary channel parameters for evaluating the performance of an uplink NOMA system. The theoretical and simulation results completely supported one another. Furthermore, the results were compared with experiments in the literature using similar parameters. The proposed system was determined to increase performance by 40% for OP, 62% for SER, and 4.74 times for AVC at SNR = 20 dB. Finally, this study introduced exact solutions that can significantly accelerate NOMA systems analysis by exploiting the information in the existing database for analytical design processes in communications theory.

Maximization of sum rate for Wireless Powered Communication Network with Intelligent Reflecting Surface and NOMA in the nonappearance of uplink and downlink beamforming matrix, subject to transmit power and time

Ampoma Affum,

Tweneboah‐Koduah,

Kubi Appiah

et al. 2024

Int J Communication

Wireless Powered Communication Networks (WPCNs) represent a transformative approach to address the energy demands of mobile and Internet of Things (IoT) devices. By integrating Nonorthogonal Multiple Access (NOMA) and Intelligent Reflecting Surfaces (IRS), we can significantly enhance system performance, extend coverage, and elevate the sum rate. NOMA efficiently utilizes the entire bandwidth by employing a power allocation strategy, whereas IRS, serving as an alternative to traditional relay amplification, further bolsters the sum rate. Despite these advancements, optimizing the sum rate introduces a nonconvex optimization challenge, primarily owing to the signal‐to‐interference‐plus‐noise ratio (SINR) complexities introduced by NOMA's Successive Interference Cancellation (SIC). Traditional convex optimization solvers, such as the CVX, struggle to address nonconvexity directly. Consequently, they were unable to produce the desired outcome. Moreover, the combination of multiple technologies to improve the sum rate complicates the optimization framework, necessitating a multitude of constraints that not only heightens the mathematical complexity but also induces errors through the requisite approximations for convexity conversion. To circumvent these hurdles, we advocate the application of a minimum constrained nonlinear multivariable function (Fmincon). This approach enables us to tackle the nonconvex problem head‐on, maintaining consistent simulation parameters while limiting constraints to two pivotal factors: joint optimization of the transmit power ( ) and transmit time ( ). This strategic simplification mitigates complexity and minimizes errors. Our numerical analyses confirmed the efficacy of the proposed model and optimization technique. By co‐optimizing the transmission power and time, we achieved a notable sum rate. Comparative evaluations with extant models underscored the superior performance of our proposed framework, marking a significant stride in WPCN advancement.