MaReSPS for energy efficient spectral precoding technique in large scale MIMO-OFDM

Khan, Arfat Ahmad; Almuzaini, Khalid K.; Macedo, Víctor Daniel Jiménez; Ojo, Stephen; Minchula, Vinodh Kumar; Roy, Vandana

doi:10.1016/j.phycom.2023.102057

Cited by 11 publications

References 37 publications

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Enhanced channel prediction in large‐scale 5G MIMO‐OFDM systems using pyramidal dilation attention convolutional neural network

Rathish,

Manojkumar,

Thanga Mariappan

et al. 2024

Internet Technology Letters

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In order to enhance communication while minimizing complexity in 5G and beyond, MIMO‐OFDM systems need accurate channel prediction. In order to enhance channel prediction, decrease Error Vector Magnitude, Peak Power, and Adjacent Channel Leakage Ratio, this study employs the Pyramidal Dilation Attention Convolutional Neural Network (PDACNN). Simplified clipping with filtering (SCF) reduces PAPR data, and this technique employs a PDACNN trained with the reduced data. By combining attention techniques with pyramidal dilated convolutions, the suggested PDACNN architecture is able to extract OFDM channel parameters across several scales. Attention approaches enhance channel prediction by allowing the model to dynamically concentrate on essential information. The primary objective is to make use of the network's ability to comprehend intricate spatial–temporal connections in OFDM channel data. The goal of these techniques is to make channel forecasts more accurate and resilient while decreasing concerns about EVM, Peak Power, and ACLR. To confirm the effectiveness of the suggested CP‐LSMIMO‐OFDM‐PDACNN, we measure its spectral efficiency, peak‐to‐average power ratio, bit error rate (BER), signal‐to‐noise ratio (SNR), and throughput. Throughput gains of 23.76%, 30.45%, and 18.97% are achieved via CP‐LSMIMO‐OFDM‐PDACNN, while bit error rates of 20.67%, 12.78%, and 19.56% are reduced. PAPRs of 21.66%, 23.09%, and 25.11% are also decreased.

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Enhanced channel prediction in large‐scale 5G MIMO‐OFDM systems using pyramidal dilation attention convolutional neural network

Rathish,

Manojkumar,

Thanga Mariappan

et al. 2024

Internet Technology Letters

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A Chaos BSA-Based Optimization Approach for Task Planning to Improve Resource Deployment in Cloud Computing

Mantri

2023

SN COMPUT. SCI.

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An Autoregressive Dragonfly Optimization-Based Assignment Planning Procedures for Refining Enactment and Data Security in Mobile Cloud Computing

Mishra

2023

SN COMPUT. SCI.

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MaReSPS for energy efficient spectral precoding technique in large scale MIMO-OFDM

Cited by 11 publications

References 37 publications

Enhanced channel prediction in large‐scale 5G MIMO‐OFDM systems using pyramidal dilation attention convolutional neural network

Enhanced channel prediction in large‐scale 5G MIMO‐OFDM systems using pyramidal dilation attention convolutional neural network

A Chaos BSA-Based Optimization Approach for Task Planning to Improve Resource Deployment in Cloud Computing

An Autoregressive Dragonfly Optimization-Based Assignment Planning Procedures for Refining Enactment and Data Security in Mobile Cloud Computing

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