Off-Grid Aware Channel and Covariance Estimation in mmWave Networks
Off-Grid Aware Channel and Covariance Estimation in mmWave Networks
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DOI：
10.1109/tcomm.2020.2980829
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发表时间：
2020-06-01
影响因子：
8.3
通讯作者：
Guvenc, Ismail
中科院分区：
文献类型：
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作者：
Anjinappa, Chethan Kumar;Gurbuz, Ali Cafer;Guvenc, Ismail
6GHz以下频谱的稀缺性使得毫米波（mmWave）频段成为下一代无线网络的关键组成部分。虽然毫米波频谱提供了极大的传输带宽来适应不断增长的数据速率，但这种新频谱的独特特性需要特别考虑，以实现承诺的网络吞吐量。在这项工作中，我们考虑了毫米波通信的离网目标（基础失配）问题。离网效应自然出现在采用离散化方法表示角域的压缩感知（CS）技术中。这种方法产生了一个有限的离散角点，这是一个连续的角度空间的近似，因此降低了相关参数估计的精度。为了应对离网效应，我们提出了一种新的参数扰动框架，以有效地估计毫米波网络的信道和协方差。所提出的算法采用智能扰动机制结合低复杂度贪婪框架的同时正交匹配追踪（SOMP），并共同解决离网参数和权重。数值结果表明，通过我们的新框架作为处理离网效应的结果，这是完全忽略了在传统的稀疏毫米波信道或协方差估计算法的显着性能改善。
The spectrum scarcity at sub-6 GHz spectrum has made millimeter-wave (mmWave) frequency band a key component of the next-generation wireless networks. While mmWave spectrum offers extremely large transmission bandwidths to accommodate ever-increasing data rates, unique characteristics of this new spectrum need special consideration to achieve the promised network throughput. In this work, we consider the off-grid targets (basis mismatch) problem for mmWave communications. The off-grid effect naturally appears in compressed sensing (CS) techniques adopting a discretization approach for representing the angular domain. This approach yields a finite set of discrete angle points, which are an approximation to the continuous angular space, and hence degrade the accuracy of related parameter estimation. In order to cope with the off-grid effect, we present a novel parameter-perturbation framework to efficiently estimate the channel and the covariance for mmWave networks. The proposed algorithms employ a smart perturbation mechanism in conjunction with a low-complexity greedy framework of simultaneous orthogonal matching pursuit (SOMP), and jointly solve for the off-grid parameters and weights. Numerical results show a significant performance improvement through our novel framework as a result of handling the off-grid effects, which is totally ignored in the conventional sparse mmWave channel or covariance estimation algorithms.