QoE-Oriented Hybrid Semantic and Bit Communications Under Mismatched Knowledge

Abstract

Semantic Communication (SemCom) has attracted significant attentions due to its potential to enhance communication efficiency and support human-centric services in 6G networks. However, the presence of mismatched background knowledge and dynamic communication channels decreases the performance of SemCom. These issues ultimately lead to a degradation in users’ quality of experience (QoE). To overcome this challenge, a hybrid semantic and bit communication framework is proposed to effectively improve communication performance under mismatched knowledge constraints. Specifically, we design a time division duplex (TDD) SemCom scheme, where the transmitter and the receiver synchronize background knowledge through the uplink transmission to eliminate mismatch constraints. To guide subframe configuration and communication mode selection in the TDD system, a novel QoE model including perceived quality and energy consumption is proposed, and a long-term average QoE maximization problem is further formulated. To solve the proposed NP-hard problem, a joint subframe configuration and communication mode selection algorithm (JSCA) is designed, and the original problem is decomposed into two subproblems. Firstly, the subframe configuration subproblem is transformed into a quasi-concave problem, and the optimal solution is obtained by the bisection method. Secondly, a deep reinforcement learning (DRL)-based approach is designed to select the communication mode for each service. The numerical results validate the effectiveness of JSCA and demonstrate that the proposed hybrid semantic and bit communication scheme can achieve higher QoE compared with fixed schemes, especially in long-term service scenarios.