Towards text semantic communication: two-phase variable bitrate control

Abstract

Bitrate plays a crucial role in the transmission of semantic symbols, necessitating a trade-off between semantic information preservation and transmission efficiency. However, most existing Semantic Communication (SEMCOM) designs employ pre-fixed bitrate schemes that lack the flexibility to adapt to dynamic communication environments. To address this limitation, we propose a novel two-phase varibale bitrate control algorithm designed to dynamically adjust the bitrate, thereby achieving an optimal balance between semantic performance and communication resource consumption. In Phase I, we introduce a Deep Learning (DL) based Imitation Network to model the relationship between semantic performance and bitrate across varying channel conditions, enabling the calculation of environment-specific rewards. Subsequently, Phase II employs a Deep Reinforcement Learning (DRL) agent to predict the optimal bitrate for each communication round through environmental exploration. Experimental results demonstrate that our approach yields at least 10% and 2.3% performance improvements over fixed-length bitrate systems under diverse user requirements and bit budgets.