Sparse array design for multiple switched beams using iterative antenna selection method

Digital beamforming (DBF) arrays with a large number of small antennas are extensively employed in millimeter-wave (mmWave) sensing systems. Sparse arrays have been posed as an attractive solution to mmWave systems due to their capability of striking the best compromise between performance and complexity. We propose two iterative antenna selection strategies, referred to as deterministic selection and adaptive selection, to design sparse DBF arrays in this paper. The first strategy assumes a common sparse array associated with different beamforming weights for multiple switched beams, while the other exploits switching networks to adaptively change the sparse array configuration with different beams. To counteract hardware-related issues that arise in practical realizations, we then propose guided adaptive selection and regularized adaptive selection to impose additional constraints for different switching networks. The optimality of sparse DBF arrays is defined in terms of both transmit and receive patterns. Taking that into account, an iterative re-weighted l1-norm is modified to promote boolean sparsity of the selection vector in this work. Simulation results validate the effectiveness of proposed antenna selection methods.