Abstract
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.
Published Version
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