摘要：

为了支持各类移动用户实时数据传输和稳定连接, 采用空地协同超密集网络(UDN)来提升网络服务能力。在这种新型网络结构中, 空中基站能够更好地支持用户移动性, 而地面UDN能够实现更充分的频谱复用。然而, 差异化的信道条件与覆盖范围使得干扰与频谱分配问题面临着新的挑战。因此, 面向新型的空地协同UDN, 针对该网络模型下移动场景中的干扰协调与频谱效率平衡问题, 提出基于图神经网络(GNN)的深度强化学习(DRL)算法Graph-DRL, 动态调度频谱资源并提高算法的实时性。首先, 利用GNN构建基站间邻小区的用户移动相关性模型, 通过共享本小区的需求信息来加强合作, 确定带宽复用度; 其次, 设计基于价值的学习网络, 以网络中的平均吞吐量为优化目标, 考虑用户移动性、用户密度和频谱复用度等因素, 利用训练好的Q值网络进行资源分配。仿真结果表明, 所提出的算法相较于对比算法显著提高了高移动性用户和小区边缘用户的传输速率。

关键词: 无线通信, 空地协同网络超密集网络, 频谱资源分配, 干扰协调, 基于图神经网络的深度强化学习

Abstract:

To support real-time data transmission and maintain stable connectivity for diverse mobile users, an air-ground cooperative Ultra-Dense Network (UDN) is adopted to enhance network service capabilities. In this novel network architecture, airborne base stations can better support user mobility, while terrestrial UDNs enable more efficient spectrum reuse. However, differentiated channel conditions and coverage disparities introduce new challenges in interference and spectrum allocation. Therefore, this paper focuses on a novel air-ground cooperative UDN and addresses the challenges of interference coordination and spectrum efficiency balancing in mobile scenarios within this network model. A Graph Neural Network (GNN)-based Deep Reinforcement Learning (DRL) algorithm, Graph-DRL, is proposed to dynamically schedule spectrum resources and improve the real-time performance of the algorithm. First, a GNN is utilized to model the user movement correlations among neighboring base station cells. By sharing the demand information of the local cell, cooperation is enhanced to determine the bandwidth reuse factor. Second, a value-based learning network is designed, with the average throughput of the network as the optimization objective. Considering factors such as user mobility, user density, and spectrum reuse degree, a trained Q value network is employed for resource allocation. Simulation results demonstrate that, compared to benchmark algorithms, the proposed algorithm significantly improves the transmission rates for highly mobile users and cell-edge users.

Key words: wirless communication, air-ground cooperative Ultra-Dense Network (UDN), spectrum resource allocation, interference coordination, Deep Reinforcement Learning (DRL) based on Graph Neural Network (GNN)