Random Forest-Based Adaptive Radio System for Specialized Wireless Networks
DOI:
https://doi.org/10.15662/IJEETR.2026.0802234Keywords:
SpecNets, Random Forest, Machine Learning, Wireless Network Optimization, Cognitive Radio, Adaptive Wireless Systems.Abstract
Future wireless networks must support highly diverse applications such as extended reality, automated industrial systems, and emerging technologies including terahertz communications. Conventional wireless networks are designed to operate adequately across multiple scenarios, but they lack the adaptability required to meet stringent application-specific requirements.
To overcome these limitations, wireless specialized networks, referred to as SpecNets, have been introduced. SpecNets incorporate cognitive capabilities that allow them to dynamically adapt network mechanisms according to contextual information. Recent advances in artificial intelligence and machine learning serve as a key enabler for SpecNets by allowing continuous learning and autonomous decision-making
By integrating ML functionalities, SpecNets leverage ML-driven radio interfaces capable of dynamically configuring radio parameters. In this project, an ML-driven radio interface based on a Random Forest algorithm is proposed for dataset-based wireless adaptation. The system learns optimal radio decisions from wireless datasets containing parameters such as SNR, interference, bandwidth, and delay.
The proposed approach enables intelligent modulation and channel configuration for WLAN scenarios. Performance evaluation demonstrates improved throughput, reduced latency, and enhanced reliability compared to traditional IEEE 802.11 systems. The results highlight the autonomous adaptability and efficiency of SpecNets across diverse wireless scenarios.
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