Khan Imran, Zafar Mohammad Haseeb, Jan Mohammad Tariq, Lloret Jaime, Basheri Mohammed, Singh Dhananjay
Department of Electrical Engineering, University of Engineering and Technology, Peshawar 814, Pakistan.
Department of Physics, Kohat University of Science and Technology (KUST), Kohat 26000, Pakistan.
Entropy (Basel). 2018 Jan 30;20(2):92. doi: 10.3390/e20020092.
Uplink and Downlink channel estimation in massive Multiple Input Multiple Output (MIMO) systems is an intricate issue because of the increasing channel matrix dimensions. The channel feedback overhead using traditional codebook schemes is very large, which consumes more bandwidth and decreases the overall system efficiency. The purpose of this paper is to decrease the channel estimation overhead by taking the advantage of sparse attributes and also to optimize the Energy Efficiency (EE) of the system. To cope with this issue, we propose a novel approach by using Compressed-Sensing (CS), Block Iterative-Support-Detection (Block-ISD), Angle-of-Departure (AoD) and Structured Compressive Sampling Matching Pursuit (S-CoSaMP) algorithms to reduce the channel estimation overhead and compare them with the traditional algorithms. The CS uses temporal-correlation of time-varying channels to produce Differential-Channel Impulse Response (DCIR) among two CIRs that are adjacent in time-slots. DCIR has greater sparsity than the conventional CIRs as it can be easily compressed. The Block-ISD uses spatial-correlation of the channels to obtain the block-sparsity which results in lower pilot-overhead. AoD quantizes the channels whose path-AoDs variation is slower than path-gains and such information is utilized for reducing the overhead. S-CoSaMP deploys structured-sparsity to obtain reliable Channel-State-Information (CSI). MATLAB simulation results show that the proposed CS based algorithms reduce the feedback and pilot-overhead by a significant percentage and also improve the system capacity as compared with the traditional algorithms. Moreover, the EE level increases with increasing Base Station (BS) density, UE density and lowering hardware impairments level.
在大规模多输入多输出（MIMO）系统中，上行链路和下行链路信道估计是一个复杂的问题，因为信道矩阵维度不断增加。使用传统码本方案的信道反馈开销非常大，这会消耗更多带宽并降低系统整体效率。本文的目的是利用稀疏属性来减少信道估计开销，并优化系统的能量效率（EE）。为了解决这个问题，我们提出了一种新颖的方法，使用压缩感知（CS）、块迭代支持检测（Block-ISD）、出发角（AoD）和结构化压缩采样匹配追踪（S-CoSaMP）算法来减少信道估计开销，并将它们与传统算法进行比较。CS利用时变信道的时间相关性在时隙上相邻的两个信道冲激响应（CIR）之间产生差分信道冲激响应（DCIR）。DCIR比传统的CIR具有更高的稀疏性，因为它可以很容易地被压缩。Block-ISD利用信道的空间相关性来获得块稀疏性，从而降低导频开销。AoD对路径出发角变化比路径增益慢的信道进行量化，并利用这些信息来减少开销。S-CoSaMP利用结构化稀疏性来获得可靠的信道状态信息（CSI）。MATLAB仿真结果表明，与传统算法相比，所提出的基于CS的算法显著降低了反馈和导频开销，还提高了系统容量。此外，随着基站（BS）密度、用户设备（UE）密度的增加以及硬件损伤水平的降低，能量效率水平会提高。
