Active and Dynamic Beam Tracking UnderStochastic Mobility
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We consider the problem of active and sequential beam tracking at mmWave frequencies and above. We focus on the dynamic scenario of a UAV to UAV communications where we formulate the problem to be equivalent to tracking an optimal beamforming vector along the line-of-sight path. In this setting, the resulting beam ideally points in the direction of the angle of arrival with sufficiently high resolution. Existing solutions account for predictable movements or small random movements using filtering strategies or by accounting for predictable mobility but must resort to re-estimation protocols when tracking fails due to unpredictable movements. We propose an algorithm for active learning of the AoA through evolving a Bayesian posterior probability belief which is utilized for a sequential selection of beamforming vectors. We propose an adaptive pilot allocation strategy based on a trade-off of mutual information versus spectral efficiency. Numerically, we analyze the performance of our proposed algorithm and demonstrate significant improvements over existing strategies.
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