Concurrent built in test and tuning of beamforming MIMO systems using learning assisted performance optimization

Future 5G wireless systems will deploy massive MIMO systems with large numbers of transmit and receive antennas and novel RF transceiver architectures that admit RF beamforming. Such systems will need to be designed with built-in test and post-manufacture self-tuning capability for yield enhancement and in-field tuning. A key issue is the lack of observability into internal circuit nodes due to the convergence of multiple RF beamforming chains into a combined baseband signal and the need to decouple individual RF chain behaviors from combined baseband signals. A second problem is that of testing and tuning as many RF chains in parallel as possible across a diverse range of specifications and modes of operation (beam steering angles) using optimized test stimulus. The interdependence between phase shift and gain of phase-shifter/amplifier configurations complicates the latter. Also, in relation to SISO/MIMO omnidirectional systems, large numbers of tuning knobs are involved. To solve the above, we propose novel algorithms for parallel testing and tuning of massive MIMO beamforming systems. Machine learning assisted coarse tuning is first performed followed by a fine-tuning procedure utilizing gradient descent. Tuning for EVM and signal-to-interference ratio is performed under power constraints. Simulation results prove the viability of the proposed techniques.