학술논문

Power Control in a modified Strict Frequency Reuse Algorithm Utilizing Q-Learning
Document Type
Conference
Source
2022 IEEE Ninth International Conference on Communications and Electronics (ICCE) Communications and Electronics (ICCE), 2022 IEEE Ninth International Conference on. :48-52 Jul, 2022
Subject
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Fields, Waves and Electromagnetics
Photonics and Electrooptics
Power, Energy and Industry Applications
Signal Processing and Analysis
Cellular networks
Time-frequency analysis
Machine learning algorithms
Q-learning
Costs
Spectral efficiency
Simulation
random cellular network
coverage probability
throughput
strict frequency reuse
Deep Q network
Language
Abstract
In this paper, Strict Frequency Reuse (FR) algorithm is studied for a single tier indoor Spatial Point Poisson cellular network under a 3GPP multi-slope path loss model. While the regular Strict FR algorithm assumes that the Cell-Edge Sub-band Group (CEG) is only reused in a group of the adjacent cells, this modified algorithm of this work allows all BSs share the CEG to improve the spectral efficiency. The machine learning, particularly Deep Q Network, is utilized to optimize the transmission power on Cell-Center Sub-band Group (CCG) and CEG. Instead of assuming that the ratio of the transmission power on CCG and CEG is a fixed number, the transmission ratio is determined by DQN. The simulation results indicate that the modified Strict FR algorithm can achieve significantly higher performance compared to Full FR.