数字通信与网络(英文)2024，Vol.10Issue(1) ：109-116.DOI:10.1016/j.dcan.2023.02.018

Channel assignment and power allocation for throughput improvement with PPO in B5G heterogeneous edge networks

Xiaoming He Yingchi Mao Yinqiu Liu Ping Ping Yan Hong Han Hu
数字通信与网络(英文)2024，Vol.10Issue(1) ：109-116.DOI:10.1016/j.dcan.2023.02.018
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  • 万方数据

Channel assignment and power allocation for throughput improvement with PPO in B5G heterogeneous edge networks

Xiaoming He 1Yingchi Mao 2Yinqiu Liu 3Ping Ping 2Yan Hong 4Han Hu5
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作者信息

  • 1. The College of Computer and Information,Hohai University,Nanjing,China
  • 2. The College of Computer and Information,Hohai University,Nanjing,China;The Key Laboratory of Water Big Data Technology of Ministry of Water Resources,Hohai University,Nanjing,China
  • 3. The School of Computer Science and Engineering,Nanyang Technological University,Singapore
  • 4. The College of Textile and Clothing Engineering,Soochow University,Suzhou,China
  • 5. The College of Internet of Things,Nanjing University of Posts and Telecomurunications,Nanjing,China
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Abstract

In Beyond the Fifth Generation(B5G)heterogeneous edge networks,numerous users are multiplexed on a channel or served on the same frequency resource block,in which case the transmitter applies coding and the receiver uses interference cancellation.Unfortunately,uncoordinated radio resource allocation can reduce system throughput and lead to user inequity,for this reason,in this paper,channel allocation and power allocation problems are formulated to maximize the system sum rate and minimum user achievable rate.Since the construction model is non-convex and the response variables are high-dimensional,a distributed Deep Reinforcement Learning(DRL)framework called distributed Proximal Policy Optimization(PPO)is proposed to allocate or assign resources.Specifically,several simulated agents are trained in a heterogeneous environment to find robust behaviors that perform well in channel assignment and power allocation.Moreover,agents in the collection stage slow down,which hinders the learning of other agents.Therefore,a preemption strategy is further proposed in this paper to optimize the distributed PPO,form DP-PPO and successfully mitigate the straggler problem.The experimental results show that our mechanism named DP-PPO improves the performance over other DRL methods.

Key words

B5G/Heterogeneous edge networks/PPO/Channel assignment/Power allocation/Throughput

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基金项目

Key Research and Development Program of China(2022YFC3005401)

Key Research and Development Program of China,Yunnan Province(202203AA080009)

Key Research and Development Program of China,Yunnan Province(202202AF080003)

Postgraduate Research & Practice Innovation Program of Jiangsu Province(KYCX21_0482)

出版年

2024
数字通信与网络(英文)

数字通信与网络(英文)

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参考文献量29
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