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Analog beamforming is the predominant approach for millimeter wave (mmWave)communication given its favorable characteristics for limited-resource devices.In this work, we aim at reducing the spectral efficiency gap between analog anddigital beamforming methods. We propose a method for refined beam selectionbased on the estimated raw channel. The channel estimation, an underdeterminedproblem, is solved using compressed sensing (CS) methods leveraging angulardomain sparsity of the channel. To reduce the complexity of CS methods, wepropose dictionary learning iterative soft-thresholding algorithm, whichjointly learns the sparsifying dictionary and signal reconstruction. Weevaluate the proposed method on a realistic mmWave setup and show considerableperformance improvement with respect to code-book based analog beamformingapproaches.

Beyond Codebook-Based Analog Beamforming at mmWave: Compressed Sensing and Machine Learning Methods