MambaJSCC: Deep Joint Source-Channel Coding with Visual State Space Model

TL;DR

MambaJSCC introduces a novel deep joint source-channel coding scheme utilizing a visual state space model with channel adaptation, achieving superior image transmission performance with reduced complexity and delay.

Contribution

The paper proposes MambaJSCC, a new JSCC scheme that integrates a visual state space model with channel adaptation for efficient wireless image transmission.

Findings

Outperforms SwinJSCC in PSNR by 0.48 dB.

Reduces parameters and computational overhead by over 46%.

Significantly lowers inference delay, enhancing real-time performance.

Abstract

Lightweight and efficient deep joint source-channel coding (JSCC) is a key technology for semantic communications. In this paper, we design a novel JSCC scheme named MambaJSCC, which utilizes a visual state space model with channel adaptation (VSSM-CA) block as its backbone for transmitting images over wireless channels. The VSSM-CA block utilizes VSSM to integrate two-dimensional images with the state space, enabling feature extraction and encoding processes to operate with linear complexity. It also incorporates channel state information (CSI) via a newly proposed CSI embedding method. This method deploys a shared CSI encoding module within both the encoder and decoder to encode and inject the CSI into each VSSM-CA block, improving the adaptability of a single model to varying channel conditions. Experimental results show that MambaJSCC not only outperforms Swin Transformer based JSCC (SwinJSCC) but also significantly reduces parameter size, computational overhead, and inference delay (ID). For example, with employing an equal number of the VSSM-CA blocks and the Swin Transformer blocks, MambaJSCC achieves a 0.48 dB gain in peak-signal-to-noise ratio (PSNR) over SwinJSCC while requiring only 53.3% multiply-accumulate operations, 53.8% of the parameters, and 44.9% of ID.