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

Abstract

Abstract 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.