Dynamic Energy Harvesting Based Semidefinite Relaxation Beamforming Optimization for Near-Field SWIPT in IoT Networks

Abstract

This paper proposes a dynamic power splitting for near-field simultaneous wireless information and power transfer (SWIPT) using semidefinite relaxation (SDR) for efficient energy harvesting (D-SR-EH) in energy-constrained internet of things (IoT) networks. The near-field spherical propagation beamforming design is formulated as a non-convex optimization problem that minimizes transmit power under simultaneous quality of service (QoS) requirements for information decoding and minimum harvested energy constraints. An efficient algorithm based on D-SR-EH is developed to solve this problem. The approach leverages spatial degrees of freedom in multi-antenna systems to balance the rate-energy tradeoff in SWIPT. Simulations show the D-SR-EH scheme improve rate-energy feasible region significantly compared to conventional schemes while maintaining communication QoS and energy harvesting performance. The solution provides a practical SWIPT framework for low-power IoT applications and is extendable to multi-user scenarios.