Performance Enhancement of Massive MIMO Using Deep Learning-Based Channel Estimation

Helmy, H M N; Daysti, S El; Shatila, Hazem; Aboul-Dahab, Mohamed A.

doi:10.1088/1757-899x/1051/1/012029

Cited by 10 publications

(4 citation statements)

References 12 publications

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“…Gated recurrent units have inherent memory cells and can keep the previously extracted information for a long period for later prediction. In particular, the CsiNet decoders' outputs form a sequence of length T before The structure of the proposed CsiNet-GRUs using dropout technique [14].…”

Section: Design Of the Csinet-grus System Modelmentioning

confidence: 99%

Utilized System Model Using Channel State Information Network with Gated Recurrent Units (CsiNet-GRUs)

Helmy¹,

Diasty²,

Shatila³

2023

Deep Learning and Reinforcement Learning

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MIMO: multiple-input multiple-output technology uses multiple antennas to use reflected signals to provide channel robustness and throughput gains. It is advantageous in several applications like cellular systems, and users are distributed over a wide coverage area in various applications such as mobile systems, improving channel state information (CSI) processing efficiency in massive MIMO systems. This chapter proposes two channel-based deep learning methods to enhance the performance in a massive MIMO system and compares our proposed technique to the previous methods. The proposed technique is based on the channel state information network combined with the gated recurrent unit’s technique CsiNet-GRUs, which increases recovery efficiency. Besides, a fair balance between compression ratio (CR) and complexity is given using correlation time in training samples. The simulation results show that the proposed CsiNet-GRUs technique fulfills performance improvement compared with the existing literature techniques, namely CS-based methods Conv-LSTM CsiNet, LASSO, Tval3, and CsiNet.

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Section: Design Of the Csinet-grus System Modelmentioning

confidence: 99%

Utilized System Model Using Channel State Information Network with Gated Recurrent Units (CsiNet-GRUs)

Helmy¹,

Diasty²,

Shatila³

2023

Deep Learning and Reinforcement Learning

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“…Massive MIMO technology has been shown to have significant potential in meeting the everincreasing demand for transmission-based communication systems with higher spectral efficiency. Although this exponential expansion of mobile communications remains the root cause of improved spectral efficiency, the rise of additional services, including machine-to-machine communication, Internet banking, the Internet of Things (IoT), and others, have become fundamental necessities of modern life [42][43][44][45].…”

Section: Background and Scope Of The Studymentioning

confidence: 99%

Multi-User Massive MIMO System with Adaptive Antenna Grouping for Beyond 5G Communication Network

et al. 2022

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Error-correcting codes with limited errors and higher spectral efficiency are the main concern for wireless communications. In the current situation, research is increasing daily to satisfy the growing demand for users with improved QoS. Adaptive Antenna Grouping (AAG) with a multilevel space–time trellis coding scheme in the Multi-User Massive MIMO system is the better option to provide flexible data transfer speeds, encoding gains, and gain in diversity with improved spectral efficiency and low decoding complexity, including the power optimization by reduced SNR at the same Symbol Error Rate/Frame Error Rate (SER/FER). The prior aim of maintaining spectral efficiency is achieved by using Massive MIMO. This paper presents the AAG according to the channel state information in the Massive MIMO scenario. The impact of the proposed model on standard ITU-R M.2135 scenarios is also demonstrated in this paper.

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“…A DL-enabled method for channel estimation to improve the performance of the Ma-MIMO system is given in [ 133 ]. The used approach improves the recovery quality and enhances the trade-off between the complexity and compression ratio of the Ma-MIMO system.…”

Section: Rl and DL Application In Mimomentioning

confidence: 99%

Application of Reinforcement Learning and Deep Learning in Multiple-Input and Multiple-Output (MIMO) Systems

Naeem

Pietro

Coronato

2021

Sensors

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The current wireless communication infrastructure has to face exponential development in mobile traffic size, which demands high data rate, reliability, and low latency. MIMO systems and their variants (i.e., Multi-User MIMO and Massive MIMO) are the most promising 5G wireless communication systems technology due to their high system throughput and data rate. However, the most significant challenges in MIMO communication are substantial problems in exploiting the multiple-antenna and computational complexity. The recent success of RL and DL introduces novel and powerful tools that mitigate issues in MIMO communication systems. This article focuses on RL and DL techniques for MIMO systems by presenting a comprehensive review on the integration between the two areas. We first briefly provide the necessary background to RL, DL, and MIMO. Second, potential RL and DL applications for different MIMO issues, such as detection, classification, and compression; channel estimation; positioning, sensing, and localization; CSI acquisition and feedback, security, and robustness; mmWave communication and resource allocation, are presented.

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Performance Enhancement of Massive MIMO Using Deep Learning-Based Channel Estimation

Cited by 10 publications

References 12 publications

Utilized System Model Using Channel State Information Network with Gated Recurrent Units (CsiNet-GRUs)

Utilized System Model Using Channel State Information Network with Gated Recurrent Units (CsiNet-GRUs)

Multi-User Massive MIMO System with Adaptive Antenna Grouping for Beyond 5G Communication Network

Application of Reinforcement Learning and Deep Learning in Multiple-Input and Multiple-Output (MIMO) Systems

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