Joint design of sub-channel assignment and power control in D2D aided cellular system: a novel GNN and DRL based approach

IF 4.6 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computer Networks Pub Date : 2025-09-13 DOI:10.1016/j.comnet.2025.111708

Zhongyu Ma , Ning Zhang , Heng Zhang , Yan Zhang , Zhaobin Li , Qun Guo

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引用次数: 0

Abstract

Device-to-Device (D2D) communication integrated with cellular networks is viewed as a promising network technology for enhancement of power efficiency and spectral utilization in the proximity-based wireless applications scenarios. However, co-channel interference caused by simultaneous sharing of wireless resources between the concurrent links including D2D links and cellular links poses a significant challenge for this system. To this end, a novel graph neural network (GNN) and deep reinforcement learning (DRL) combined resource allocation framework is proposed in this paper. Firstly, the joint design of sub-channel assignment and power control in the D2D overlapped cellular system is investigated as intractable nonlinear programming, where the long-term sum-of-rate (LSR) of cellular links and the transmission success rate (TSR) of D2D links are simultaneously maximized under the constraints such as concurrent interference and traffic demands, etc. Secondly, a GNN and DRL combined resource allocation framework (GD-CRAF) is proposed, where the GNN based graph sampling and aggregation (GraphSAGE) is designed to efficiently exploit the interference features from the incomplete global interference graph constructed with local interference, and the double deep Q-network (DDQN) based sub-channel assignment and power control is proposed under the DRL framework. Finally, the superiority of the proposed GD-CRAF framework is verified in diversified scenarios, where the convergence and effectiveness of the GD-CRAF are demonstrated. It is shown from the experimental results that the LSR and TSR of the GD-CRAF are superior to that of other references such as DQN based scheme, Q-Learning based scheme and random allocation based scheme.

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D2D辅助蜂窝系统中子信道分配和功率控制的联合设计：一种基于GNN和DRL的新方法

与蜂窝网络集成的设备到设备（D2D）通信被视为一种有前途的网络技术，可在基于邻近的无线应用场景中提高功率效率和频谱利用率。然而，包括D2D链路和蜂窝链路在内的并发链路之间同时共享无线资源所引起的同信道干扰对该系统提出了重大挑战。为此，本文提出了一种新的图神经网络（GNN）和深度强化学习（DRL）相结合的资源分配框架。首先，将D2D重叠蜂窝系统中子信道分配和功率控制的联合设计作为一种棘手的非线性规划问题进行了研究，该问题在并行干扰和流量需求等约束条件下，使蜂窝链路的长期速率和（LSR）和D2D链路的传输成功率（TSR）同时最大化。其次，提出了一种GNN和DRL组合资源分配框架（GD-CRAF），其中设计了基于GNN的图采样和聚合（GraphSAGE），有效地利用由局部干扰构成的不完全全局干扰图的干扰特征，并在DRL框架下提出了基于双深q网络（DDQN）的子信道分配和功率控制。最后，在多种场景下验证了所提出的GD-CRAF框架的优越性，证明了GD-CRAF的收敛性和有效性。实验结果表明，gd - craft的LSR和TSR均优于其他参考方案，如基于DQN的方案、基于Q-Learning的方案和基于随机分配的方案。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computer Networks 工程技术-电信学

CiteScore

10.80

自引率

3.60%

发文量

434

审稿时长

8.6 months

期刊介绍： Computer Networks is an international, archival journal providing a publication vehicle for complete coverage of all topics of interest to those involved in the computer communications networking area. The audience includes researchers, managers and operators of networks as well as designers and implementors. The Editorial Board will consider any material for publication that is of interest to those groups.