Deep Reinforcement Learning Based Resource Allocation for URLLC User-Centric Network

被引：0

作者：

Hu, Fajin ^{[1
]}

Zhao, Junhui ^{[1
,2
]}

Liao, Jieyu ^{[1
]}

Zhang, Huan ^{[1
]}

机构：

[1] Beijing Jiaotong Univ, Sch Elect & Informat Engn, Beijing, Peoples R China

[2] East China Jiaotong Univ, Sch Informat Engn, Nanchang, Peoples R China

来源：

2022 14TH INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS AND SIGNAL PROCESSING, WCSP | 2022年

基金：

中国国家自然科学基金; 北京市自然科学基金;

关键词：

Resource allocation; user-centric; URLLC; deep reinforcement learning;

D O I：

10.1109/WCSP55476.2022.10039329

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this paper, we solve the resource allocation problem by deep reinforcement learning (DRL) for diverse ultra-reliable low-latency communication (URLLC) services under the user-centric downlink transmission. Firstly, to meet the constraint of reliability, we model the channel decoding error rate by using the finite blocklength coding (FBC) according to the short packet characteristics of URLLC services. Then, we model the queue of different URLLC services in the temporal dimension to describe the delay violation problem. Furthermore, we adopt the DRL scheme that transforms the maximizing system availability and transmission efficiency problem into maximizing system reward problems. Simulation results show that the proposed algorithm achieves superior availability for diverse URLLC services compared with the baselines.

引用

页码：522 / 526

页数：5

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