Spatial Deep Learning-Based Dynamic TDD Control for UAV-Assisted 6G Hotspot Networks

Abstract

Compared to static time-division duplexing (TDD), dynamic TDD (D-TDD) has significantly increased the spectral efficiency of cellular networks. However, conventional systems operate based on exact channel state information, resulting in high communication overhead and delay. Spatial deep learning refers to using spatial geographical information as the training data. This study investigates a spatial deep learning-based D-TDD scheme for 6G hotspot networks. First, we represent geographical location information in forms of traffic demand density grid matrices. Second, we use spatial convolution filters to extract discriminative features of uplink and downlink service gains and harms, taking the traffic demand density grid matrices as the input. Subsequently, extracted feature matrices are processed with sparse convolution blocks to reduce computation cost for the classification. Finally, we develop novel deep dueling neural networks, leveraging the extracted features to efficiently learn the near-optimal radio slot configurations for all base stations. Numerical results show that the proposed approach improves average rate per user by 2.5%, 6%, and 523.3% over those achieved in state-of-the-art centralized D-TDD, the competitive reinforcement learning, and greedy approaches, respectively. In addition, the proposed approach achieves up to 98.7% of the data rate performance of the optimum scheme with an exhaustive search algorithm. IEEE