正交频分多址
计算机科学
副载波
资源配置
波束赋形
正交频分复用
人工神经网络
强化学习
信道分配方案
电信线路
无线网络
光谱效率
分布式计算
无线
计算机网络
频道(广播)
人工智能
电信
作者
Xiaoming Wang,Gaoxiang Sun,Yuanxue Xin,Ting Liu,Youyun Xu
出处
期刊:IEEE Transactions on Signal and Information Processing over Networks
日期:2022-01-01
卷期号:8: 815-829
被引量:1
标识
DOI:10.1109/tsipn.2022.3208432
摘要
Orthogonal frequency division multiple access (OFDMA) is one of the promising technologies to satisfy the huge access demand and high data-rate requirement of the fifth generation (5G) networks. In this paper, we study the joint beamforming coordination and resource allocation in the downlink multi-cell multiple-input single-output OFDMA (MISO-OFDMA) systems. First, we divide the allocation framework into beamforming coordination and power allocation (BCPA) module and subcarrier allocation (SA) module. Then, we design a multi-agent deep Q-network (MADQN) algorithm for the allocation framework. Furthermore, we propose a MADQN-based transfer learning framework using knowledge distillation, which is called transfer learning-MADQN (TL-MADQN), to improve the adaptability of neural networks for different wireless schemes. TL-MADQN exploits neural networks and their parameters distilled from pre-trained agents and the experience collected from new agents so that the new agents complete their training process effectively and quickly in the new network environment. Finally, we adjust the allocation policy to maximize the sum data-rate for all users by updating the weights of each neural network. Simulation results show that the proposed MADQN algorithm achieves better performance than the baseline algorithms. Moreover, our TL-MADQN framework further improves the convergence speed and data-rate, which validates its effectiveness and superiority.
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