Inter-Cell Interference Mitigation for Cellular-Connected UAVs Using MOSDS-DQN

Research output: Contribution to journalArticlepeer-review

Abstract

In 5G and beyond, UAVs are integrated into cellular networks as new aerial mobile users to support many applications and provide higher probability of line-of-sight (LoS) transmission to base stations (BSs). Nevertheless, due to limited frequency bandwidth and spectrum resource reuse when BSs serving terrestrial users (TUEs) and UAVs, it causes severe downlink interference to TUEs, especially when the network has a heavy load. Thus, in this paper, we study the performance of radio connectivity of UAVs and TUEs in an urban area and introduce a downlink inter-cell interference coordination mechanism. Then, we propose adaptive cell muting interference and resource allocation scheduling schemes. A value function approximation solution (VFA), Tabular-Q, and Deep-Q Network (DQN) are proposed to maximize the long-term network throughput of TUEs while guaranteeing the data rate requirements of UAVs. With increasing number of UAVs and TUEs and dynamic wireless environment, we further propose a Muting Optimization Scheme and Dynamic time-frequency Scheduling (MOSDS) algorithm to increase throughput and satisfactory level for both UAVs and TUEs. Simulation results show that the proposed algorithms achieve 80% performance improvement of throughput of UAV and TUE networks and mitigate the interference among them. Also, the proposed MOSDS-DQN shows 18% improvement compared to the DQN algorithm.

Original languageEnglish
Pages (from-to)1596-1609
Number of pages14
JournalIEEE Transactions on Cognitive Communications and Networking
Volume9
Issue number6
DOIs
Publication statusPublished - 1 Dec 2023

Keywords

  • Antennas
  • Autonomous aerial vehicles
  • Cellular Networks
  • Deep Reinforcement Learning
  • Downlink
  • Dynamic scheduling
  • Heuristic algorithms
  • Interference
  • Interference Management
  • Three-dimensional displays
  • Throughput
  • UAV

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