The advent of 5G communication technology has revolutionized
wireless communication with its high bandwidth, ultra-low latency, and
massive connectivity features. However, the dynamic nature of user
behavior and environmental changes poses significant challenges in
optimizing signal processing for context awareness. Adaptive signal
processing (ASP) offers a promising solution, but traditional methods
struggle to effectively handle real-time, context-sensitive demands. In
this research, we propose a novel reinforcement learning (RL)-based
framework for adaptive signal processing that enhances context
awareness in 5G networks. The problem addressed involves the
optimization of signal parameters, such as power, frequency, and
modulation schemes, to meet varying user demands and environmental
conditions without compromising Quality of Service (QoS). The
proposed method employs RL to adaptively optimize these parameters
in real time. Specifically, a Q-learning algorithm is applied to learn the
optimal policies for signal adaptation based on feedback from the
environment, such as user mobility, interference levels, and network
traffic. Simulation results demonstrate that the RL-based approach
outperforms traditional static models, achieving up to a 30% reduction
in latency and a 20% improvement in overall network throughput,
while maintaining a 95% success rate in meeting user QoS
requirements. This demonstrates the potential of RL for enhancing ASP in 5G systems.
K. Prabhu Chandran1, Sesham Anand2, Subramanya V. Odeyar3, Karra Basheeba Rani4
Prathyusha Engineering College, India1, Maturi Venkata Subba Rao Engineering College, India2, Nagarjuna College of Engineering and Technology, India3, Malla Reddy Institute of Technology, India4
Reinforcement Learning, Adaptive Signal Processing, Context Awareness, 5G, Quality of Service
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| Published By :
ICTACT
Published In :
ICTACT Journal on Communication Technology
( Volume: 15 , Issue: 3 , Pages: 3314 - 3319 )
Date of Publication :
September 2024
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195
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