A Goal-Oriented Context-Aware Adaptive Semantic Communication Scheme Using a Semantic Mask Module

By extracting and transmitting semantic information (SI), semantic communications (SC) can achieve communication goal and reliable transmission with much lower data rate among devices. However, devices in SC typically need to perform concurrent tasks, subject to varying available computational resources. Additionally, dynamic communication environments could dramatically deteriorate communication performance. To achieve robust SC with aforementioned uncertainties, this paper proposes a context-aware, adaptive rate, multitask SC scheme (CAAR-MTSC) to optimize the latent representation of transmitted data. The proposed scheme comprehensively considers the user’s perceptual needs, current channel conditions, and task-relevant information within the data. Specifically, we introduce a Semantic Mask Module (SMM) that dynamically controls the data rate based on channel conditions and user-defined perceptual metrics to obtain the best trade-off between task performance and data rate. To enhance multi-task performance, we formulate a triple trade-off information bottleneck (IB) optimization problem using rate-distortion perception. We further incorporate a knowledge distillation (KD) strategy to reduce the model size while maintaining performance. Simulation results indicate that, in low-SNR conditions, the proposed framework achieves up to a 12% improvement in SSIM and a 3.4% increase in classification accuracy while reducing data rate by 11%, thereby demonstrating its efficacy under resource-constrained scenarios.