Home / Current Issue / Paper 1724143
Artificial Neural Network-Assisted Remote Radio Control for Adaptive Data Transmission Optimization in Wireless Communication Networks
Subject area: Science,Engineering and Technology · Area of research: Electronic and Communication Engineering
DOI: 10.64388/IREV10I4-1724143
Abstract
The increasing heterogeneity, traffic intensity, and dynamic operating conditions of modern wireless communication networks limit the effectiveness of conventional fixed radio-control strategies. This study develops an Artificial Neural Network (ANN)-assisted Remote Radio Control (RRC) framework for adaptive optimization of wireless data transmission. The proposed framework was implemented and evaluated using MATLAB R2021a/Simulink. A conventional wireless network model was characterized using throughput, signal-to-interference-plus-noise ratio (SINR), bit error rate (BER), packet delivery ratio (PDR), and latency. The characterized network parameters were subsequently used as feedback inputs to the ANN controller for adaptive radio-control decisions. Simulation parameters included a 2.4 GHz carrier frequency, 20 MHz bandwidth, 23 dBm transmit power, QPSK modulation, coding rate of 1/2, 1024-bit packets, 20 users, and an additive white Gaussian noise (AWGN)-plus-multipath channel. Results indicate that ANN-assisted RRC improved throughput from 2.00 to 2.60 Mbps (30.0%) and SINR from 4.70 to 6.11 dB (approximately 30.0%). BER decreased from 0.00053 to 0.00046 (13.2%), while PDR increased from 65.0% to 84.5%, representing a 30.0% relative improvement. Furthermore, latency decreased from 114.4 to 88.0 ms, corresponding to a 23.08% reduction. These findings demonstrate the potential of ANN-assisted RRC to enhance multiple wireless-network performance indicators simultaneously. The proposed simulation framework provides a foundation for intelligent radio control in evolving 5G, beyond-5G, and 6G communication networks.
Keywords
artificial neural network; adaptive radio control; remote radio control; wireless communication; throughput; SINR; packet delivery ratio; latency
How to cite this paper
@article{1724143,
author = {Anyanwu Macdonald Chima, Iloh Johnpaul Izuchukwu, Achebe Patience Nkiruka},
title = {Artificial Neural Network-Assisted Remote Radio Control for Adaptive Data Transmission Optimization in Wireless Communication Networks},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {4},
pages = {2955-2967},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1724143.pdf},
abstract = {The increasing heterogeneity, traffic intensity, and dynamic operating conditions of modern wireless communication networks limit the effectiveness of conventional fixed radio-control strategies. This study develops an Artificial Neural Network (ANN)-assisted Remote Radio Control (RRC) framework for adaptive optimization of wireless data transmission. The proposed framework was implemented and evaluated using MATLAB R2021a/Simulink. A conventional wireless network model was characterized using throughput, signal-to-interference-plus-noise ratio (SINR), bit error rate (BER), packet delivery ratio (PDR), and latency. The characterized network parameters were subsequently used as feedback inputs to the ANN controller for adaptive radio-control decisions. Simulation parameters included a 2.4 GHz carrier frequency, 20 MHz bandwidth, 23 dBm transmit power, QPSK modulation, coding rate of 1/2, 1024-bit packets, 20 users, and an additive white Gaussian noise (AWGN)-plus-multipath channel. Results indicate that ANN-assisted RRC improved throughput from 2.00 to 2.60 Mbps (30.0%) and SINR from 4.70 to 6.11 dB (approximately 30.0%). BER decreased from 0.00053 to 0.00046 (13.2%), while PDR increased from 65.0% to 84.5%, representing a 30.0% relative improvement. Furthermore, latency decreased from 114.4 to 88.0 ms, corresponding to a 23.08% reduction. These findings demonstrate the potential of ANN-assisted RRC to enhance multiple wireless-network performance indicators simultaneously. The proposed simulation framework provides a foundation for intelligent radio control in evolving 5G, beyond-5G, and 6G communication networks.},
keywords = {artificial neural network; adaptive radio control; remote radio control; wireless communication; throughput; SINR; packet delivery ratio; latency},
month = {October},
doi = {https://doi.org/10.64388/IREV10I4-1724143}
}