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Treating the codewords in a beamforming codebook as the arms in a multi-armedbandit problem, we propose a best arm identification multi-armed banditalgorithm in the fixed-confidence setting for mmWave beam alignment initialaccess called Successive Subtree Elimination (SSE). The algorithm performanceapproaches that of state-of-the-art Bayesian algorithms at a fraction of thecomplexity and without requiring channel state information. The algorithmsimultaneously exploits the benefits of hierarchical codebooks and theapproximate unimodality of rewards to achieve fast beam steering, in a sensethat we precisely define to provide fair comparison with existing algorithms.In particular, SSE outperforms other low-complexity algorithms in the lowSignal-to-Noise Ratio (SNR) regime. The performance of SSE is demonstratedthrough the derivation of a closed-form sample complexity, which enablesoffline tuning of design parameters, as well as extensive simulations over slowfading channels.

mmWave Beam Alignment using Hierarchical Codebooks and Successive Subtree Elimination