An Efficient Allocation of Resources in wireless Communications IoT Network for Numerous Users

Wireless-powered communication (WPC) emerges as a highly promising technological avenue for a diverse range of intelligent network applications spanning the industrial, commercial, and security domains. The proliferation of Internet of Things (IoT) devices on a global scale is experiencing an unprecedented surge. In the landscape of next-generation networks, the strategic deployment of distributed fog computing at the network periphery offers a pivotal means of provisioning computational resources, with a specific focus on applications demanding minimal latency. Pertaining to the matter of link spectral efficiency within wireless networks, the conventional assumption that higher values inevitably translate to heightened efficiency across the broader radio access network is challenged. Within this context, we introduce a low-complexity Robust Channel Allocation Algorithm (RCAA) designed for efficient user grouping. Furthermore, we utilize Reinforcement Learning (RL) to model the challenges posed by unpredictable energy arrival and fluctuating channel conditions for each user. Within this framework, we introduce an innovative power allocation algorithm designed to enhance the overall network throughput over a limited time span comprising multiple time slots. To verify the effectiveness and resilience of our proposal, we employ Logarithmic-based utility functions that cater to the unique requirements of both devices tolerant to delays and real-time Internet of Things (IoT) applications. Through thorough simulations, we establish the effectiveness of our approach, providing conclusive evidence of its ability to efficiently manage resource allocation and achieve optimal results.