Lightweight and efficient neural network models for deep joint source-channel coding (JSCC) are crucial for semantic communications. In this paper, we propose a novel JSCC architecture, named MambaJSCC, that achieves state-of-the-art performance with low computational and parameter overhead. MambaJSCC utilizes the visual state space model with channel adaptation (VSSM-CA) blocks as its backbone for transmitting images over wireless channels, where the VSSM-CA primarily consists of the generalized state space models (GSSM) and the zero-parameter, zero-computational channel adaptation method (CSI-ReST). We design the GSSM module, leveraging reversible matrix transformations to express generalized scan expanding operations, and theoretically prove that two GSSM modules can effectively capture global information. We discover that GSSM inherently possesses the ability to adapt to channels, a form of endogenous intelligence. Based on this, we design the CSI-ReST method, which injects channel state information (CSI) into the initial state of GSSM to utilize its native response, and into the residual state to mitigate CSI forgetting, enabling effective channel adaptation without introducing additional computational and parameter overhead. Experimental results show that MambaJSCC not only outperforms existing JSCC methods (e.g., SwinJSCC) across various scenarios but also significantly reduces parameter size, computational overhead, and inference delay.

翻译：轻量化且高效的深度联合信源信道编码（JSCC）神经网络模型对于语义通信至关重要。本文提出一种新颖的JSCC架构，命名为MambaJSCC，该架构以较低的计算和参数开销实现了最先进的性能。MambaJSCC利用具有信道自适应能力的视觉状态空间模型（VSSM-CA）块作为其骨干网络，用于在无线信道上传输图像。VSSM-CA主要由广义状态空间模型（GSSM）和零参数、零计算开销的信道自适应方法（CSI-ReST）构成。我们设计了GSSM模块，利用可逆矩阵变换来表达广义扫描扩展操作，并从理论上证明两个GSSM模块能够有效捕获全局信息。我们发现GSSM本身具备适应信道的能力，这是一种内生智能。基于此，我们设计了CSI-ReST方法，该方法将信道状态信息（CSI）注入GSSM的初始状态以利用其固有响应，并注入残差状态以缓解CSI遗忘，从而在不引入额外计算和参数开销的情况下实现有效的信道自适应。实验结果表明，MambaJSCC不仅在多种场景下优于现有JSCC方法（例如SwinJSCC），还显著减少了参数量、计算开销和推理延迟。