Hybrid Digital-Analog Semantic Communications

TL;DR

This paper introduces HDA-DeepSC, a hybrid digital-analog semantic communication system that improves robustness and quality in multimedia transmission by combining novel allocation, fusion, loss functions, and diffusion-based detection techniques.

Contribution

It presents a new hybrid digital-analog framework with innovative modules and loss functions, enhancing semantic communication performance over existing methods.

Findings

Outperforms benchmarks in PSNR and SSIM metrics

Demonstrates robustness to channel variations

Supports diverse communication scenarios

Abstract

Digital and analog semantic communications (SemCom) face inherent limitations such as data security concerns in analog SemCom, as well as leveling-off and cliff-edge effects in digital SemCom. In order to overcome these challenges, we propose a novel SemCom framework and a corresponding system called HDA-DeepSC, which leverages a hybrid digital-analog approach for multimedia transmission. This is achieved through the introduction of digital-analog allocation and fusion modules. To strike a balance between data rate and distortion, we design new loss functions that take into account long-distance dependencies in the semantic distortion constraint, essential information recovery in the channel distortion constraint, and optimal bit stream generation in the rate constraint. Additionally, we propose denoising diffusion-based signal detection techniques, which involve carefully designed variance schedules and sampling algorithms to refine transmitted signals. Through extensive numerical experiments, we will demonstrate that HDA-DeepSC exhibits robustness to channel variations and is capable of supporting various communication scenarios. Our proposed framework outperforms existing benchmarks in terms of peak signal-to-noise ratio and multi-scale structural similarity, showcasing its superiority in semantic communication quality.