Spectral and Energy Efficient Low-Overhead Uplink and Downlink Channel Estimation for 5G Massive MIMO Systems.
Imran Khan, Mohammad Haseeb Zafar, Mohammad Tariq Jan, Jaime Lloret, Mohammed Basheri, Dhananjay Singh
Author Information
Imran Khan: Department of Electrical Engineering, University of Engineering and Technology, Peshawar 814, Pakistan. ORCID
Mohammad Haseeb Zafar: Department of Electrical Engineering, University of Engineering and Technology, Peshawar 814, Pakistan.
Mohammad Tariq Jan: Department of Physics, Kohat University of Science and Technology (KUST), Kohat 26000, Pakistan.
Jaime Lloret: Instituto de Investigación para la Gestión Integrada de Zonas Costeras, Universitat Politècnica de València, 46022 Camino de Vera, Spain. ORCID
Mohammed Basheri: Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Dhananjay Singh: Department of Electronics Engineering, Hankuk University of Foreign Studies, Yongin 449-791, Korea. ORCID
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.
