Energy-Efficient Joint Collaborative and Passive Beamforming for Intelligent-Reflecting-Surface-Assisted Wireless Sensor Networks

Intelligent reflecting surface (IRS) provides a promising technology that can improve the energy efficiency of wireless communications, by building the controllable wireless propagation environment with the utilization of massive passive reflecting elements. Motivated by this, we apply the IRS to assist the data collection based on collaborative beamforming in wireless sensor networks (WSNs). With the objective to maximize the network lifetime, we present an energy-efficient joint collaborative and passive beamforming design for an IRS-assisted WSN, which jointly optimizes the beamforming vector for all nodes and phase shifts for IRS's reflecting elements. To efficiently resolve the joint optimization problem, we develop a penalty dual-decomposition (PDD)-based algorithm combined with the augmented Lagrangian method, penalty-based method and block coordinate descent algorithm, which decomposes the joint optimization problem into four simplified subproblems. Especially, in solving IRS's phase-shift optimization subproblem, we formulate this subproblem as a quadratically constrained quadratic programming (QCQP) problem and further propose a low computational complexity approximate iteration algorithm to resolve this QCQP problem, which iteratively linearizes this QCQP problem to a sequence of approximate programming with linear equality constraints, whose closed-form solution is presented. Theoretical analysis indicates the proposed algorithm can converge to a KKT solution. Simulation results indicate that the IRS-assisted design and proposed algorithm improve the energy efficiency and prolong the network lifetime as compared to alternative algorithms.

Automated summary

This study proposes an energy-efficient joint collaborative and passive beamforming design for an Intelligent Reflecting Surface (IRS)-assisted Wireless Sensor Network (WSN), aiming to maximize the network lifetime. Through the development of a penalty dual-decomposition (PDD)-based algorithm, the joint optimization problem is efficiently solved, and a low computational complexity approximate iteration algorithm is proposed for the IRS's phase-shift optimization subproblem.