JSSA: Joint Sidelobe Suppression Approach for Collaborative Beamforming in Wireless Sensor Networks

Conventional collaborative beamforming (CB) with virtual node antenna array often results in high maximum sidelobe level (SLL) due to the unexpected positions of the nodes. In this paper, a sidelobe suppression approach (JSSA) to suppress the maximum SLL by jointly optimizing the locations and amplitude weights of the nodes is proposed. JSSA organizes the node positions according to the concentric circular antenna array for location optimization. Then, a novel algorithm called variation particle chicken swarm optimization (VPCSO) is proposed to further optimize the amplitude weights of the selected CB nodes. The proposed VPCSO introduces the variation mechanism and location learning mechanism to enhance the performance of the conventional chicken swarm optimization algorithm. Simulations are conducted and the results show that the proposed location optimization approach is effective, and the maximum SLLs of beam patterns obtained by VPCSO are lower than that of other algorithms. Moreover, the energy consumption can be saved by VPCSO. In addition, electromagnetic (EM) simulations are conducted to verify the performance of the proposed JSSA in EM environments.