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

• Published in: International journal of communication systems Vol. 37; no. 16
• Main Authors: Ampoma Affum, Emmanuel, Tweneboah‐Koduah, Samuel, Kubi Appiah, Michael, Gyamfi, Eric, Adeola Ajagbe, Sunday, Agyeman Antwi, Owusu, Adigun, Matthew
• Format: Journal Article
• Language: English
• Published: Chichester Wiley Subscription Services, Inc 10.11.2024
• Subjects: Beamforming; Communication networks; Complexity; Constraints; convex optimization solver (CVX); Convexity; Errors; Functions (mathematics); intelligent reflecting surface (IRS); Internet of Things; minimum of constrained nonlinear multivariable function (Fmincon); Nonorthogonal multiple access; nonorthogonal multiple access (NOMA); Optimization; Optimization techniques; Performance evaluation; Power management; Reconfigurable intelligent surfaces; successive interference cancellation (SIC); Wireless communications; wireless energy transfer (WET); wireless information transfer (WIT); Wireless networks; wireless powered communication networks (WPCN); WPCN
• DOI: 10.1002/dac.5911
• ISSN: 1074-5351

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 ( PT) and transmit time ( Tx). 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. Wireless powered communication networks (WPCNs) satisfy the energy requirements of mobile and Internet of Things (IoT) devices. Combining Nonorthogonal Multiple Access (NOMA) with Intelligent Reflecting Surface (IRS) boosts performance and coverage, but NOMA's Successive Interference Cancellation (SIC) Signal‐to‐Interference‐plus‐Noise Ratio (SINR) presents a nonconvex optimization issue for sum‐rate enhancement. Traditional convex optimization solvers fail with nonconvex problems. The work proposes using Fmincon to address such challenges. Our method directly addresses nonconvex issues by focusing on two key aspects: joint optimization of transmit power and time, leading to a significant increase in the sum rate compared with previous works.