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Bayesian Beamforming for Mobile Millimeter Wave Channel Tracking in the Presence of DOA Uncertainty
IEEE Transactions on Communications ( IF 8.3 ) Pub Date : 2020-12-01 , DOI: 10.1109/tcomm.2020.3026377
Yan Yang , Shuping Dang , Miaowen Wen , Shahid Mumtaz , Mohsen Guizani

This paper proposes a Bayesian approach for angle-based hybrid beamforming and tracking that is robust to uncertain or erroneous direction-of-arrival (DOA) estimation in millimeter wave (mmWave) multiple input multiple output (MIMO) systems. Because the resolution of the phase shifters is finite and typically adjustable through a digital control, the DOA can be modeled as a discrete random variable with a prior distribution defined over a discrete set of candidate DOAs, and the variance of this distribution can be introduced to describe the level of uncertainty. The estimation problem of DOA is thereby formulated as a weighted sum of previously observed DOA values, where the weights are chosen according to a posteriori probability density function (pdf) of the DOA. To alleviate the computational complexity and cost, we present a motion trajectory-constrained a priori probability approximation method. It suggests that within a specific spatial region, a directional estimate can be close to true DOA with a high probability and sufficient to ensure trustworthiness. We show that the proposed approach has the advantage of robustness to uncertain DOA, and the beam tracking problem can be solved by incorporating the Bayesian approach with an expectation-maximization (EM) algorithm. Simulation results validate the theoretical analysis and demonstrate that the proposed solution outperforms a number of state-of-the-art benchmarks.

中文翻译:

在存在 DOA 不确定性的情况下用于移动毫米波信道跟踪的贝叶斯波束成形

本文提出了一种基于角度的混合波束成形和跟踪的贝叶斯方法,该方法对毫米波 (mmWave) 多输入多输出 (MIMO) 系统中不确定或错误的到达方向 (DOA) 估计具有鲁棒性。由于移相器的分辨率是有限的,并且通常通过数字控制进行调整,因此可以将 DOA 建模为离散随机变量,其先验分布定义在一组离散的候选 DOA 上,并且可以将该分布的方差引入到描述不确定性的程度。因此,DOA 的估计问题被表述为先前观察到的 DOA 值的加权和,其中根据 DOA 的后验概率密度函数 (pdf) 选择权重。为了降低计算复杂度和成本,我们提出了一种运动轨迹约束的先验概率近似方法。这表明在特定空间区域内,方向估计可以以高概率接近真实 DOA,并且足以确保可信度。我们表明,所提出的方法具有对不确定 DOA 的鲁棒性优势,并且可以通过将贝叶斯方法与期望最大化 (EM) 算法相结合来解决波束跟踪问题。仿真结果验证了理论分析,并证明所提出的解决方案优于许多最先进的基准。我们表明,所提出的方法具有对不确定 DOA 的鲁棒性优势,并且可以通过将贝叶斯方法与期望最大化 (EM) 算法相结合来解决波束跟踪问题。仿真结果验证了理论分析,并证明所提出的解决方案优于许多最先进的基准。我们表明,所提出的方法具有对不确定 DOA 的鲁棒性优势,并且可以通过将贝叶斯方法与期望最大化 (EM) 算法相结合来解决波束跟踪问题。仿真结果验证了理论分析,并证明所提出的解决方案优于许多最先进的基准。
更新日期:2020-12-01
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