Fast EVM Tuning of MIMO Wireless Systems Using Collaborative Parallel Testing and Implicit Reward Driven Learning

Abstract

Modern 5G and projected 6G wireless systems deploy massive MIMO systems with antenna arrays and novel RF transceiver architectures that admit RF beamforming. Testing and tuning of the underlying transceiver arrays on a per-transceiver basis is expensive and can be expedited through the use of parallel testing and tuning techniques that stimulate the entire array transceiver system concurrently. State of the art parallel testing techniques require frequency separation between the tones applied to individual RF chains due to combining of RF signals before down-conversion in analog beamforming MIMO systems. Test schemes that allow some frequency overlap are limited to testing only third order distortion. In this paper, we first present a parallel testing scheme for testing large MIMO transceiver arrays that is amenable to higher order distortion (upto fifth order) in the RF chains considered. Second, we propose a tuning scheme for the entire MIMO array which implicitly tunes for EVM system specifications without explicit knowledge of the relationship between the system test response, the system tuning knobs and the corresponding EVM and SINR specification values. A cost metric is formulated that allows such a solution using reinforcement (multi-arm bandit) learning driven system tuning. Significant yield improvement using this approach is demonstrated by simulation experiments.