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Bayesian Beamforming for Mobile Millimeter Wave Channel Tracking in the Presence of DOA Uncertainty

机译:DOA不确定性存在的移动毫米波沟道跟踪的贝叶斯波束成形

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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.
机译: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,这对于毫米波(MMWAVE)多输入多输出(MIMO)系统的不确定或错误的到达(DOA)估计是强大的。因为相移器的分辨率是有限的并且通常通过数字控制调节,所以DOA可以用在离散的候选DOAS上定义的先前分布的离散随机变量来建模,并且可以引入该分布的方差描述不确定性的水平。由此将DOA的估计问题作为先前观察到的DOA值的加权和,其中根据<斜体XMLNS:MML =“http://www.w3.org/1998/math/mathml”xmlns选择权重: XLink =“http://www.w3.org/1999/xlink”> doa的后验概率密度函数(pdf)。为了减轻计算复杂性和成本,我们呈现了一个运动轨迹约束的<斜体XMLNS:mml =“http://www.w3.org/1998/math/mathml”xmlns:xlink =“http://www.w3 .org / 1999 / xlink“>先验概率近似方法。它表明,在特定的空间区域内,方向估计可以接近真正的DOA,具有很高的概率并且足以确保可靠性。我们表明所提出的方法具有对不确定DOA的鲁棒性的优点,并且通过将贝叶斯方法与期望最大化(EM)算法结合来解决光束跟踪问题。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测试。

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