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. 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 the adaptive sensing vectors sequentially based on the available information so far at the base station (BS). By recognizing that the AoA posterior distribution is a sufficient statistic for solving the initial access problem, we use the posterior distribution as the input to the proposed DNN for designing the adaptive sensing strategy. However, computing the posterior distribution can be computationally challenging when the channel fading coefficient is unknown. To address this issue, this paper proposes to use an estimate of the fading coefficient to compute an approximation of the posterior distribution. Further, this paper shows that the proposed DNN can deal with practical beamforming constraints such as the constant modulus constraint. Numerical results demonstrate that compared to the existing adaptive and non-adaptive beamforming schemes, the proposed DNN-based adaptive sensing strategy achieves a significantly better AoA acquisition performance.

中文翻译：

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用于毫米波初始对准的自适应波束形成的深度主动学习方法


本文提出了一种深度学习方法，用于解决单路径信道毫米波环境中初始接入阶段的自适应和顺序波束成形设计问题。对于单用户场景，问题相当于设计感知波束形成器的序列来学习主导路径的到达角（AoA），我们提出了一种新颖的深度神经网络（DNN），它基于顺序设计自适应感知向量迄今为止基站（BS）的可用信息。通过认识到 AoA 后验分布对于解决初始访问问题来说是足够的统计量，我们使用后验分布作为所提出的 DNN 的输入来设计自适应感知策略。然而，当信道衰落系数未知时，计算后验分布在计算上可能具有挑战性。为了解决这个问题，本文建议使用衰落系数的估计来计算后验分布的近似值。此外，本文还表明，所提出的 DNN 可以处理实际的波束形成约束，例如恒模约束。数值结果表明，与现有的自适应和非自适应波束形成方案相比，所提出的基于 DNN 的自适应传感策略实现了明显更好的 AoA 采集性能。