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

TL;DR

TOAST is a task-oriented semantic transmission framework for 6G wireless networks that adaptively balances multiple tasks, efficiently fine-tunes models, and restores corrupted features, significantly improving performance under noisy conditions.

Contribution

This work introduces a unified adaptive framework combining deep reinforcement learning, LoRA-based fine-tuning, and diffusion models for robust multi-task semantic communication in dynamic wireless environments.

Findings

Outperforms baseline methods in classification accuracy and reconstruction quality.

Maintains robustness across diverse channel impairments.

Achieves significant improvements at low SNR conditions.

Abstract

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 (Task-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 (AWGN), 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.