Energy‐Efficient Resource Management in CIoT for B5G Networks

The rapid growth of the internet of things (IoT) in urban settings necessitates innovative approaches to tackle spectrum and energy efficiency issues. This study introduces an innovative resource management approach for Cognitive IoT (CIoT) networks in Beyond 5G (B5G) environments, using hybrid orthogonal frequency multiple access, time division multiple access, and non‐orthogonal multiple access (OFDMA‐TDMA/NOMA) methodologies with radio frequency energy harvesting (RFEH). The proposed approach enhances resource distribution to promote energy efficiency (EE) while adhering to quality‐of‐service (QoS) requirements. Fractional programming and the Lagrangian multiplier approach, in conjunction with the subgradient method, are used to address the optimization problem. Simulation results indicate that the proposed system attains enhanced EE, scalability, and convergence speed relative to traditional approaches, including genetic algorithm (GA), particle swarm optimization (PSO), and rate‐splitting multiple access (RSMA). The results underscore the efficiency of the hybrid technique in accommodating an increasing number of users without compromising performance, offering a resilient option for energy‐efficient B5G CIoT networks.