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

Yong Li,Hao Sun,Lijian Xin,Xingyu Chen,Xiang Zhang

doi:10.1155/2019/1392129

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.

Highlights

To efficiently evaluate the performance of multiple-input multiple-output (MIMO) capable devices, the over-the-air (OTA) radiated testing has evolved to be capable of emulating realistic spatial channel characteristics in a controlled manner [1]
In [2], spatial correlation was selected as the criterion to determine probe antenna weights, which aims to minimize the difference between the emulated spatial correlations and the target spatial correlations
In our proposed method, the main computational overhead is the gradient calculation at each iteration. us, the complexity of the proposed method can be given by O(SN), where S denotes the size of the minibatch. erefore, the proposed method has a much lower computational complexity compared with the multishot algorithm and is more suitable for 3D probe selection application

Summary

Introduction

To efficiently evaluate the performance of multiple-input multiple-output (MIMO) capable devices, the over-the-air (OTA) radiated testing has evolved to be capable of emulating realistic spatial channel characteristics in a controlled manner [1]. Since not all probe antennas are required to be active to synthesize a particular channel model due to the fact that practical channel models are typically directional, a flexible setup which selects a subset of candidate probe antennas to be active may be more costefficient by reducing the number of required hardware resources of the channel emulator, which constitutes a major cost factor for the MPAC setup. The brute force algorithm will perform convex optimization for all possible combinations of active antennas and becomes computationally prohibitive when the number of candidate antennas is large; the one-shot algorithm selects a plurality of probes with the largest power weights after convex optimization is performed with all candidate probes, which is the simplest but obviously provides the worse performance; the multishot algorithm and the successive probe cancellation algorithm remove the probe(s) with the least and the most contributions, respectively, and perform convex optimization repeatedly with the remaining probes. This work is the first reported effort to introduce the NN technique into multiprobe OTA testing

Probe Selection and Power Weighting

NN-Based Approach

Simulations and Discussions

Conclusions