TOAST: Task-Oriented Adaptive Semantic Transmission over Dynamic Wireless Environments

--The evolution toward 6G networks demands a fundamental shift from bit-centric transmission to semantic-aware communication that emphasizes task-relevant information. This work introduces TOAST (T ask-Oriented Adaptive Semantic Transmission), a unified framework designed to address the core challenge of multi-task optimization in dynamic wireless environments through three complementary components. First, we formulate adaptive task balancing as a Markov decision process, employing deep reinforcement learning to dynamically adjust the trade-off between image reconstruction fidelity and semantic classification accuracy based on real-time channel conditions. Second, we integrate module-specific Low-Rank Adaptation (LoRA) mechanisms throughout our Swin Transformer-based joint source-channel coding architecture, enabling parameter-efficient fine-tuning that dramatically reduces adaptation overhead while maintaining full performance across diverse channel impairments including Additive White Gaussian Noise (A WGN), fading, phase noise, and impulse interference. Third, we incorporate an Elucidating diffusion model that operates in the latent space to restore features corrupted by channel noises, providing substantial quality improvements compared to baseline approaches. Extensive experiments across multiple datasets demonstrate that TOAST achieves superior performance compared to baseline approaches, with significant improvements in both classification accuracy and reconstruction quality at low Signal-to-Noise Ratio (SNR) conditions while maintaining robust performance across all tested scenarios. By seamlessly orchestrating reinforcement learning, diffusion-based enhancement, and parameter-efficient adaptation within a single coherent framework, TOAST represents a significant advancement toward adaptive semantic communication systems capable of thriving in the rigorous conditions of next-generation wireless networks. HE emergence of sixth-generation (6G) wireless networks marks a fundamental change in how communication is understood, shifting from Shannon's classical model of reliable bit transmission to a semantic-oriented approach that focuses on meaning and task relevance [1]. This development in Semantic Communication (SemCom) acknowledges that, in many practical scenarios, reconstructing every bit perfectly is neither required nor efficient. Instead, the key is to retain the information necessary for completing specific tasks, such as interpreting a scene, making a decision, or initiating an action [2]. Wang are with the Department of Electrical Engineering and Computer Science, Lassonde School of Engineering, Y ork University, Toronto, ON, Canada (e-mails: ys97@yorku.ca; J. Pei is with the School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan, China (e-mail: jianhuapei@hust.edu.cn).