Deep Active Learning Approach to Adaptive Beamforming for mmWave Initial Alignment

Abstract

This paper proposes a deep learning approach to the adaptive and sequential beamforming design problem for the initial access phase in a mmWave environment with a single-path channel model. In particular, for a single-user scenario where the problem is equivalent to designing the sequence of sensing beamformers to learn the angle of arrival (AoA) of the dominant path, we propose a novel deep neural network (DNN) that designs a sequence of adaptive sensing vectors based on the available information so far at the base station (BS). By recognizing that the posterior distribution of the AoA provides sufficient statistic for solving the initial access problem, we consider the AoA posterior distribution as the main component of the input to the proposed DNN for designing the adaptive beamforming strategy. However, computing the AoA posterior distribution can be computationally challenging when the fading coefficient is unknown. To address this issue, this paper proposes to use the minimum mean squared error (MMSE) estimate of the fading coefficient to compute an approximation of the posterior distribution. Numerical results demonstrate that as compared to the existing adaptive beamforming schemes utilizing predesigned hierarchical codebooks, the proposed deep learning-based adaptive beamforming achieves a higher AoA detection performance.

An Optimal Subspace Deconvolution Algorithm for Robust and High-Resolution Beamforming

“…Basically, there are three kinds of algorithms. One is the subspace algorithm for separating signal and noise [ 8 , 9 ], one is the rotation-invariant algorithm for uniform linear array (ULA) [ 10 , 11 ], and the other one is the adaptive algorithm for changing backgrounds [ 12 , 13 ]. The common feature of the three algorithms is that the noise can be suppressed to the greatest extent so that they can obtain high resolution.…”