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There has been recent work on the design of antenna arrays for beamforming in dynamic evolving environments such as in vehicle-to-vehicle communication systems. A key problem is that of determining how to optimally use a large antenna array to communicate with multiple spatially located vehicles in dynamically changing channel conditions with minimal co-channel interference while minimizing overall power consumption of the wireless system. We envision disjoint subsets of antennas in the array being used to direct beams concurrently to different vehicles. The number of antennas, gain and phase of each RF-chain driving an antenna are optimized dynamically using a constrained quadratic cost formulation encompassing channel quality, interference and power consumption. This quadratic optimization problem is solved using behavior constrained bandit algorithm, a reinforcement learning based technique. A gaussian kernel is used to perform data clustering of vehicle environment and resulting solutions, allowing quick bootstrapping of the bandit solver to find optimal array configurations in real-time vehicle environments. Simulation studies prove the viability of the proposed scheme. 

Emerging wireless technologies employ MIMO beamforming antenna arrays to improve channel Signal-to-Noise Ratio (SNR). The increased dynamic range of channel SNR values that can be accommodated, creates power stress on Radio Frequency (RF) electronic circuitry. To alleviate this, we propose an approach in which the circuitry along with other transmission coding parameters can be dynamically tuned in response to channel SNR and beam-steering angle to either minimize power consumption or maximize throughput in the presence of manufacturing process variations while meeting a specified Bit Error Rate (BER) limit. The adaptation control policy is learned online and is facilitated by information obtained from testing of the RF circuitry before deployment. 

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. 

Test stimulus generation algorithms for analog/RF circuits rely on iterative simulation of the circuits concerned and are extremely computation-intensive. Our objective is to speed up test stimulus generation while allowing tests to be optimized dynamically (adapted) across diverse process corners that a device under test (DUT) is experiencing during manufacturing without compromising test quality. To achieve this, we propose to dynamically recognize devices from unknown process corners during manufacturing test and create booleanized models of these devices from measurements performed on hardware. The cumulative ensemble of booleanized models across different devices is used to (re-) optimize tests depending on observed performance statistics. The use of booleanized models for test generation allows orders of magnitude speedup in test computation time while allowing emulation of devices long after they have shipped to the customer. The method is demonstrated using the alternative test methodology developed in prior research and allows the tests concerned to adapt to process shifts in a dynamic manner during device manufacture. Simulation results and hardware measurements are used to demonstrate the efficacy of the proposed techniques.