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Direction-of-arrival (DOA) information is vital formultiple-input-multiple-output (MIMO) systems to complete localization andbeamforming tasks. Switched antenna arrays have recently emerged as aneffective solution to reduce the cost and power consumption of MIMO systems.Switch-based array architectures connect a limited number of radio frequencychains to a subset of the antenna elements forming a subarray. This paperaddresses the problem of antenna selection to optimize DOA estimationperformance. We first perform a subarray layout alignment process to removesubarrays with identical beampatterns and create a unique subarray set. Byusing this set, and based on a DOA threshold region performance approximation,we propose two antenna selection algorithms; a greedy algorithm and adeep-learning-based algorithm. The performance of the proposed algorithms isevaluated numerically. The results show a significant performance improvementover selected benchmark approaches in terms of DOA estimation in the thresholdregion and computational complexity.

Antenna Selection in Switch-Based MIMO Arrays via DOA threshold region Approximation