Genetic Algorithm Based Weight Optimization for Minimizing Sidelobes in Distributed Random Array Beamforming

This paper proposes solution to optimize the peak side lobes level (PSLL) in a distributed random antenna array (RAA) when locations of the nodes in the array cannot be manipulated. Using the conventional beam forming method, RAA produces a poor beam pattern with high side lobe level, which greatly reduces the performance and the efficiency of the antenna. Existing literature focuses on finding the best position of antenna placement in RAA to lower the side lobes. This is not feasible when the user has no autonomy over the position of the antenna elements. Our proposed solution achieves beam pattern with much lower PSLL regardless of the array size and number of nodes in the array. The proposed method also enables up to 40% of energy savings when the size of array is small and 20% of savings when bigger array size is considered.