Elimination of Noise Distortion for OFDM Systems by Compressed Sensing Based on Distance Metric

Li, Ning; Huang, Mengjia; Deng, Zhongliang

doi:10.1109/access.2020.3044306

Cited by 1 publication

(1 citation statement)

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“…In [50], CS is applied to recover the original signal after the clipping issue by using l 1 -norm optimization which has a high computational complexity. However, the OMP is often used as a CS algorithm to reconstruct the linear signal due to its simplicity and less complexity than Basis Pursuit (BP) algorithms [51][52][53].…”

Section: Related Workmentioning

confidence: 99%

BER Analysis and CS-Based Channel Estimation and HPA Nonlinearities Compensation Technique for Massive MIMO System

et al. 2022

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Massive Multiple Input Multiple Output (MIMO) system has gathered lately a huge interest from researchers and it has been known as a backbone technology for the 5th generation because it can greatly improve the capacity of wireless communication and it can potentially provide energy efficiency and security. Before ensuring the full advantages, massive MIMO system has to overcome many challenges. One of them is to accurate an exact estimation of the Channel Impulse response (CIR) between each transmitreceive antenna pair. The other problem arises when implementing such a system in presence of High Power Amplifiers (HPA) which imposes a nonlinear distortion on the transmitted signals. This paper proposes contributions in three key areas. Firstly, we investigate the nonlinear effects of memoryless HPA, which is modeled using Saleh model, by deriving its Bit Error Rate expression (BER) in massive MIMO Orthogonal Frequency Division Multiplexing (OFDM) system. Secondly, we highlight the necessity of providing the accurate channel characteristics by estimating it using Compressive Sensing (CS) techniques. Moreover, we propose also a CS compensation algorithm based on Orthogonal Matching Pursuit (OMP) to mitigate the nonlinear distortion in the receiver. The proposed CS-based compensation technique has been compared to the Neural Network (NN) pre and post compensation technique and it has shown a good performance not only in terms of BER but also in terms of complexity.

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