Joint Sensor Clustering and Power Allocation in Wireless Body Area Networks Based on NOMA

With the transformation of modern medical care towards a prevention-centred model and the growing demand for high-density sensor deployments in wireless body area networks (WBANs), traditional orthogonal multiple access (OMA) schemes face critical limitations in spectral efficiency and energy sustainability. Non-orthogonal multiple access (NOMA) combined with radio frequency energy harvesting (RFEH) emerges as a promising solution, yet existing research often overlooks the strict energy causality constraints of sensors. To address this issue, this paper proposes a novel joint optimization framework for NOMA-assisted WBANs, which strategically pairs predefined sensor groups (classified by body positions) into NOMA clusters and optimizes power allocation while accounting for harvested RF energy. The proposed sorted-greedy joint algorithm enhances successive interference cancellation (SIC) efficiency by pairing sensor groups with the largest channel gain differences, followed by a greedy power allocation strategy that maximizes throughput under energy and power constraints. Simulation results demonstrate that the proposed algorithm outperforms conventional greedy and random clustering approaches in total system throughput across varying numbers of sensor groups, sensors per group, and transmission power levels.