Evidence map›Paper›PMID 41298847›Full record

ArticleScientific reports2025

Massive MIMO joint beamforming and power allocation via LMS with narwhal lemming optimization and fractional ResNeXt-based control.

Mian Muhammad Kamal, Luo Yinsheng, Tianjun Ma, Husam S Samkari, Mohammed F Allehyani, Muhammad Sheraz, Muhammad Hanif Ahmed Khan Khushik, Mohammad Qatawneh, Teong Chee Chuah

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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Mian Muhammad KamalSchool of Electronics and Communication Engineering, Quanzhou University of Information Engineering, Quanzhou, 362000, China. mianmuhammadkamal@qzuie.edu.cn.
Luo YinshengSchool of Electronics and Communication Engineering, Quanzhou University of Information Engineering, Quanzhou, 362000, China.
Tianjun MaSchool of Electronics and Communication Engineering, Quanzhou University of Information Engineering, Quanzhou, 362000, China.
Husam S SamkariDepartment of Electrical Engineering, University of Tabuk, 47713, Tabuk, Saudi Arabia.
Mohammed F AllehyaniDepartment of Electrical Engineering, University of Tabuk, 47713, Tabuk, Saudi Arabia.
Muhammad SherazCentre for Smart Systems and Automation, CoE for Robotics and Sensing Technologies, Faculty of Artificial Intelligence and Engineering, Multimedia University, Persiaran Multimedia, 63100, Cyberjaya, Selangor, Malaysia.
Muhammad Hanif Ahmed Khan KhushikSchool of Electronics and Communication Engineering, Quanzhou University of Information Engineering, Quanzhou, 362000, China.
Mohammad QatawnehDepartment of Networks and Cybersecurity, Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 11942, Jordan.
Teong Chee ChuahCentre for Smart Systems and Automation, CoE for Robotics and Sensing Technologies, Faculty of Artificial Intelligence and Engineering, Multimedia University, Persiaran Multimedia, 63100, Cyberjaya, Selangor, Malaysia. tcchuah@mmu.edu.my.

Funding

Fujian Provincial Natural Science Foundation 2024CT018Fujian Provincial Natural Science Foundation 2024J011540Multimedia University MMUI/250008National Key Research and Development Program of China 2022YFF0708801Telekom Research & Development Sdn Bhd RDTC/231095Telekom Research & Development Sdn Bhd RDTC/241149
6 · The paper itself

Abstract

Nowadays, next-generation wireless communication networks are used to provide robust and accurate sensing capability, high-quality wireless connections using massive devices, and support high data transmission rates. Beamforming is used in Massive Multi-Input and Multi-Output (mMIMO) systems to effectively cancel the complex interference caused by the non-zero inner product among the transmitted and channel signal matrices. Various techniques are used to significantly maximize the energy efficiency and reduce the Signal-to-Interference-plus-Noise Ratio (SINR) communication requirements. Meanwhile, these techniques did not maximize the sum-rate of all users by jointly optimizing the transmit beamformer. In this paper, a ResNeXt with Fractional Narwhal Lemming Optimization (ResFraNLO) is introduced for joint beamforming and power allocation in mMIMO systems. Here, the adaptive beamforming is performed using Least-Mean-Square (LMS) adaptive algorithm and the weights of LMS is adjusted optimally using Narwhal Lemming Optimization (NLO). After that, the power allocation is executed using the ResNeXt framework. Moreover, the performance of ResNeXt is improved by fine-tuning the weights and bias by Fractional Narwhal Lemming Optimization (FraNLO). Further, the supremacy of ResFraNLO is investigated, and the ResFraNLO attained achievable sum rate of 79.099 Mbps/Hz, Root Mean Squared Error (RMSE) of 1.34 × 10

Indexed as

Artificial lemming algorithmFifth-generationMassive multiple-input multiple-outputNarwhal lemming optimizationResNeXt

Identifiers

PMID41298847
PMCPMC12658091

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