DRL-based intelligent resource allocation for physical layer semantic communication with IRS

被引:4
|
作者
Hu, Bing [1 ]
Ma, Jiaqi [1 ]
Sun, Zhixin [1 ]
Liu, Jian [2 ]
Li, Ran [3 ]
Wang, Lingyi [4 ]
机构
[1] Nanjing Univ Posts & Telecommun, Coll Modern Posts, Nanjing 210003, Peoples R China
[2] Nanjing Univ Finance & Econ, Coll Informat Engn, Nanjing 210023, Peoples R China
[3] Xinyang Normal Univ, Sch Comp & Informat Technol, Xinyang 464000, Peoples R China
[4] Nanjing Univ Posts & Telecommun, Coll Sci, Nanjing 210003, Peoples R China
关键词
Semantic communication; Intelligent reflection surface; Effective semantic spectral efficiency; Deep reinforcement learning; Physical layer communication; DESIGN; SYSTEM;
D O I
10.1016/j.phycom.2023.102270
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
Semantic communication and intelligent reflection surface (IRS) are considered to be promising technologies to solve the scarce spectrum resource problem for the sixth -generation (6G) communication networks. However, there is few research on semantic resource allocation for IRS -enhanced communication networks, which leverages the efficient spectrum utilization of both semantic communication and IRS. In this paper, the resource allocation problem in the IRS -assisted semantic communication network is investigated, and effective semantic spectral efficiency (ES -SE) is defined considering desired semantic similarity for downstream semantic tasks. For the purpose of maximizing the ES -SE, the selection of DeepSCs, allocation of subchannels, reflection array elements of the IRS and transmit beamforming of the base station (BS) are jointly optimized. Considering the necessity of real-time performance and full -link intelligence, a two -stage intelligent approach using dueling double deep Q networks (D3QN)-soft actor critic (SAC) is proposed to tackle the tough resource allocation problem with non-linear programming and coupled variables. Simulation results validate the effectiveness of our designed IRS -assisted semantic communication scheme and demonstrate the superior performance of our proposed intelligent approach.
引用
收藏
页数:11
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