Deep Q-Learning-Driven Power Control for Enhanced Noma User Performance
Bach Hung Luu, Sinh Cong Lam, Nam Hoang Nguyen

TL;DR
This paper introduces a UAV-assisted cellular network with deep Q-learning-based power control to improve data rates for cell-edge users, demonstrating notable performance gains through simulation.
Contribution
It proposes a novel distance-based UAV relay assistance scheme combined with deep Q-learning for power control, enhancing cell-edge user performance in cellular networks.
Findings
Peak average rate of 2.28 bps/Hz at 400m reference distance
3.6% improvement over no UAV assistance
0.9% improvement over full UAV support
Abstract
Cell-edge users (CEUs) in cellular networks typically suffer from poor channel conditions due to long distances from serving base stations and physical obstructions, resulting in much lower data rates compared to cell-center users (CCUs). This paper proposes an Unmanned Aerial Vehicles (UAV)-assisted cellular network with intelligent power control to address the performance gap between CEUs and CCUs. Unlike conventional approaches that either deploy UAVs for all users or use no UAV assistance, our model uses a distance-based criterion where only users beyond a reference distance receive UAV relay assistance. Each UAV operates as an amplify-and-forward relay, enabling assisted users to receive signals from both the base station and the UAV simultaneously, thereby achieving diversity gain. To optimize transmission power allocation across base stations, we employ a Deep Q-Network (DQN)…
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Taxonomy
TopicsUAV Applications and Optimization · IoT Networks and Protocols · Mobile Ad Hoc Networks
