Adaptive Resource Allocation for Semantic Communication Networks
Lingyi Wang Wei Wu Fuhui Zhou Zhaohui Yang Zhijin Qin
Semantic communication, recognized as a promising technology for future
intelligent applications, has received widespread research attention. Despite
the potential of semantic communication to enhance transmission reliability,
especially in low signal-to-noise (SNR) environments, the critical issue of
resource allocation and compatibility in the dynamic wireless environment
remains largely unexplored. In this paper, we propose an adaptive semantic
resource allocation paradigm with semantic-bit quantization (SBQ) compatibly
for existing wireless communications, where the inaccurate environment
perception introduced by the additional mapping relationship between semantic
metrics and transmission metrics is solved. In order to investigate the
performance of semantic communication networks, the quality of service for
semantic communication (SC-QoS), including the semantic quantization efficiency
(SQE) and transmission latency, is proposed for the first time. A problem of
maximizing the overall effective SC-QoS is formulated by jointly optimizing the
transmit beamforming of the base station, the bits for semantic representation,
the subchannel assignment, and the bandwidth resource allocation. To address
the non-convex formulated problem, an intelligent resource allocation scheme is
proposed based on a hybrid deep reinforcement learning (DRL) algorithm, where
the intelligent agent can perceive both semantic tasks and dynamic wireless
environments. Simulation results demonstrate that our design can effectively
combat semantic noise and achieve superior performance in wireless
communications compared to several benchmark schemes. Furthermore, compared to
mapping-guided paradigm based resource allocation schemes, our proposed
adaptive scheme can achieve up to 13% performance improvement in terms of
SC-QoS.