Adaptive Sampling and Joint Semantic-Channel Coding under Dynamic Channel Environment

TL;DR

This paper introduces an adaptive joint sampling and semantic-channel coding framework that dynamically optimizes data acquisition and reconstruction in changing channel conditions, improving robustness and efficiency in semantic communications.

Contribution

It proposes a novel semantic-aware sampling method and an attention-based channel adaptive module to enhance robustness and reduce data acquisition in dynamic environments.

Findings

Reduces data acquisition without performance loss

Improves robustness to channel variations

Achieves high-quality reconstruction in dynamic channels

Abstract

Deep learning enabled semantic communications are attracting extensive attention. However, most works normally ignore the data acquisition process and suffer from robustness issues under dynamic channel environment. In this paper, we propose an adaptive joint sampling-semantic-channel coding (Adaptive-JSSCC) framework. Specifically, we propose a semantic-aware sampling and reconstruction method to optimize the number of samples dynamically for each region of the images. According to semantic significance, we optimize sampling matrices for each region of the most individually and obtain a semantic sampling ratio distribution map shared with the receiver. Through the guidance of the map, high-quality reconstruction is achieved. Meanwhile, attention-based channel adaptive module (ACAM) is designed to overcome the neural network model mismatch between the training and testing channel environment during sampling-reconstruction and encoding-decoding. To this end, signal-to-noise ratio (SNR) is employed as an extra parameter input to integrate and reorganize intermediate characteristics. Simulation results show that the proposed Adaptive-JSSCC effectively reduces the amount of data acquisition without degrading the reconstruction performance in comparison to the state-of-the-art, and it is highly adaptable and adjustable to dynamic channel environments.