System energy efficiency maximization-oriented dual-stage collaborative beamforming design for hybrid intelligent reflecting surface-aided EHCRSNs

Abstract

To tackle the problem of low energy efficiency (EE) caused by the energy harvesting (EH) and data transmission between cognitive radio sensor networks (CRSNs) nodes and the energy source sink via direct links, hybrid intelligent reflecting surface (H-IRS) is incorporated into CRSNs for the first time. H-IRS assists both downlink EH and uplink data communication, and a non-convex optimization problem subject to constraints is formulated to maximize the system EE. To solve this, a dual-stage collaborative beamforming mechanism is proposed, which jointly optimizes the beamforming of both the sink and H-IRS. A grouped alternating optimization strategy is employed to handle the coupling of multiple optimization variables, combined with a low-complexity algorithm that incorporates fractional programming and successive convex approximation. This mechanism progressively transforms the fractional non-convex optimization problem into a convex problem, addressing the challenges of multi-dimensional coupled variable optimization. Simulation results show that with an appropriate number of active reflecting elements and sufficient maximum amplification power budget of the active IRS sub-surface, the proposed mechanism achieves a minimum 10 % improvement ratio in system EE over the baseline mechanisms.