ZF and MMSE Detectors Performances of a Massive MIMO System Combined with OFDM and M-QAM Modulation

Riadi, Abdelhamid; Boulouird, Mohamed; Hassani, Moha M’Rabet

doi:10.1007/s11277-020-07848-4

Cited by 20 publications

(6 citation statements)

References 27 publications

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“…Rayleigh, Rician and Nakagami fading channels have been considered. The performance of MMSE, ZF and V‐BLAST detectors in [32] was analysed in term of in term of BER for uplink massive MIMO system. Uplink massive MIMO system was combined with M‐QAM modulation and OFDM technique.…”

Section: Literature Reviewmentioning

confidence: 99%

Performance analysis of linear detection for uplink massive MIMO system based on spectral and energy efficiency with Rayleigh fading channels in 3D plotting pattern

Soufy

Nashwan

Al-Kamali

et al. 2023

The Journal of Engineering

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Massive multiple-input multiple-output (MIMO) is a critical component of 5G cellular networks, which utilizes large numbers of antennas at both the transmitter and receiver to enhance throughput and radiated energy efficiency. Various linear detection techniques are employed with massive MIMO to counteract path loss and interference, and maximize throughput. The first aim of this paper is to analyse the performance of uplink massive MIMO system for different linear detection techniques including: Maximum ratio combining (MRC), zero-forcing (ZF), regularized ZF (RZF) and minimum mean squared error (MMSE) over Rayleigh channel model. The second aim is to jointly investigate the optimal values of signal-to-noise ratio (SNR), the number of antennas M and the number of users K for maximizing the spectral efficiency (SE) and energy efficiency (EE) through simulation using MATLAB and 3D plotting patterns. The obtained results show that the best SE and EE are achieved by uplink massive MIMO setup while using optimal values of SNR, M and K. It is observed that MMSE achieved the best performance. However, it requires estimation of average SNR at BS. Therefore, the best choice is ZF or RZF without any need for SNR estimation.

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Section: Literature Reviewmentioning

confidence: 99%

Performance analysis of linear detection for uplink massive MIMO system based on spectral and energy efficiency with Rayleigh fading channels in 3D plotting pattern

Soufy

Nashwan

Al-Kamali

et al. 2023

The Journal of Engineering

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show abstract

“…Zero forcing is a detection criterion used to create the filtering matrix; the method drawback is the noise enhancement and diversity loss. Otherwise, the MMSE consider the noise enhancement problem, hence, the MMSE shows better results compared to ZF [33]. ZF is a linear detection technique, which force the interference to zero without including the noise, however, the MMSE minimizes the mean square error between transmitted and received data, both uses the matrix of linear transformation.…”

Section: Detection Techniques With Equalizationmentioning

confidence: 99%

Virtual Mimo for Vehicular Communication Systems

Shokry

Abdelfattah

Mabrouk

2022

Journal of Al-Azhar University Engineering Sector

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This paper provides a simulation study for the quality of vehicle communication system by using a Virtual Multiple-Input Multiple-Output (VMIMO) used in Ultrawide Band (UWB) wireless communication, merged with orthogonal pulse shaping using modified Hermite polynomial. The simulation compares between Zero-Forcing (ZF) and Minimum Square Error (MMSE) Detection Techniques performance through bit-error-rate analysis. The simulation includes different scenarios for vehicular communication: The Vehicle to Vehicle (V2V) and the Vehicle to Infrastructure (V2I). Firstly, the Non-Line-of-Sight (NLOS), secondly the Lineof-Sight (LOS) scenarios. Moreover, the simulation considers the effect of path-loss and noise on transmitted signal, besides the comparison with QAM (Quadratic amplitude modulation), QPSK (quadratic pulse shift keying) and (Binary pulse shift keying) BPSK modulations. The simulation result is useful for the design of vehicle communication system including several cases of vehicles communication such as the V2V and V2I. The final results recommend BPSK-MMSE communication for both the LOS and NLOS scenarios for the V2V and V2I.

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“…In recent years, Deep Learning (DL) has achieved great success in a certain fields such as image recognition [9], natural language processing [10], and speech recognition [11]. Its application in wireless communication systems has also garnered significant attention from the industry, particularly in the context of signal detection for MIMO systems.…”

Section: Introductionmentioning

confidence: 99%

The application of large-scale MIMO detection algorithm in 5G and beyond 5G

Tang

2024

Fourth International Conference on Signal Processing and Machine Learning (CONF-SPML 2024)

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In recent years, the rapid advancement of 5G technology has brought to the forefront the pivotal role of Multiple-Input Multiple-Output (MIMO) system algorithms. This paper delves into a comprehensive exploration of two distinct algorithmic approaches within the context of 5G applications for massive MIMO systems. These two approaches are matrix transformation and machine learning, and the following paragraphs will shed light on their respective attributes and intricacies. Matrix transformation is a fundamental technique in MIMO systems, which aims to optimize the transmission of signals by manipulating the channel matrices. This method, while established and reliable, exhibits certain limitations in accommodating the dynamic and complex nature of 5G environments. On the other hand, machine learning algorithms, with their adaptability and capacity for self-improvement, have gained prominence in recent years. They offer a promising avenue for addressing the challenges presented by 5G MIMO systems, such as handling interference and optimizing resource allocation. In this paper, we provide concrete examples to analyze the strengths and weaknesses of both matrix transformation and machine learning in the context of 5G applications. Furthermore, we explore potential directions for the application of these algorithms and propose areas for improvement, with the ultimate goal of enhancing the efficiency and performance of massive MIMO systems in the evolving landscape of 5G technology.

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ZF and MMSE Detectors Performances of a Massive MIMO System Combined with OFDM and M-QAM Modulation

Cited by 20 publications

References 27 publications

Performance analysis of linear detection for uplink massive MIMO system based on spectral and energy efficiency with Rayleigh fading channels in 3D plotting pattern

Performance analysis of linear detection for uplink massive MIMO system based on spectral and energy efficiency with Rayleigh fading channels in 3D plotting pattern

Virtual Mimo for Vehicular Communication Systems

The application of large-scale MIMO detection algorithm in 5G and beyond 5G

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