Probe Selection and Power Weighting in Multiprobe OTA Testing: A Neural Network-Based Approach

Abstract

Over-the-air (OTA) radiated testing is an efficient solution to evaluate the performance of multiple-input multiple-output (MIMO) capable devices, which can emulate realistic multipath channel conditions in a controlled manner within lab environment. In a multiprobe anechoic chamber- (MPAC-) based OTA setup, determining the most appropriate probe locations and their power weights is critical to improve the accuracy of channel emulation at reasonable system costs. In this paper, a novel approach based on neural networks (NNs) is proposed to derive suitable angular locations as well as power weights of OTA probe antennas; in particular, by using the regularization technique, active probe locations and their weights can be optimized simultaneously with only one training process of the proposed NN. Simulations demonstrate that compared with the convex optimization-based approach to perform probe selection in the literature, e.g., the well-known multishot algorithm, the proposed NN-based approach can yield similar channel emulation accuracy with significantly reduced computational complexity.