Efficient Deep Joint Source-Channel Coding for Wireless Image Transmission using Visual State Space Model with Channel Adaptation

Core Concepts

MambaJSCC, a novel lightweight and efficient deep joint source-channel coding (JSCC) scheme, utilizes a visual state space model with channel adaptation (VSSM-CA) to effectively encode and decode images over wireless channels, outperforming existing JSCC approaches while significantly reducing computational complexity and model size.

Abstract

The paper proposes a novel deep JSCC scheme called MambaJSCC for efficient wireless image transmission. The key components are: VSSM-CA Block: Integrates 2D images with state space to capture global information. Incorporates channel state information (CSI) via a novel CSI embedding method to adapt to varying channel conditions. Hierarchical Encoder-Decoder Structure: Encoder: Iteratively merges patches and processes them using VSSM-CA blocks. Decoder: Iteratively divides patches and processes them using VSSM-CA blocks. Enables effective feature extraction and encoding/decoding. CSI Embedding Method: Encodes CSI into a vector and injects it into each VSSM-CA block. Allows the model to adapt to dynamic channel conditions without introducing significant complexity. Experimental results show that MambaJSCC outperforms the state-of-the-art SwinJSCC scheme in terms of PSNR, while requiring only 53.3% of the multiply-accumulate operations, 53.8% of the parameters, and 44.9% of the inference delay.

Stats

The proposed 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 the inference delay.

Quotes

"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."

Key Insights Distilled From

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

by Tong Wu,Zhiy... at arxiv.org 05-07-2024

https://arxiv.org/pdf/2405.03125.pdf

Deeper Inquiries

How can the proposed MambaJSCC architecture be further extended or adapted to handle different types of data beyond images, such as video or text

The MambaJSCC architecture can be extended or adapted to handle different types of data beyond images by modifying the input and output layers of the model to accommodate the specific characteristics of the new data types. For video data, temporal information can be incorporated by introducing recurrent connections or 3D convolutional layers to capture motion dynamics. Text data can be processed by converting it into a suitable format for the model, such as using word embeddings or character-level encoding. Additionally, the model's architecture can be adjusted to handle sequential data efficiently, enabling it to learn dependencies and patterns in text sequences.

What are the potential limitations or trade-offs of the CSI embedding method compared to other channel adaptation techniques, and how could these be addressed in future work

The CSI embedding method, while efficient in reducing model size and computational complexity, may have limitations compared to other channel adaptation techniques. One potential limitation is the reliance on accurate CSI information, which may not always be available or may be subject to errors in practical wireless communication scenarios. To address this, future work could explore robust methods for handling imperfect or noisy CSI, such as incorporating uncertainty estimation or adaptive mechanisms to adjust to varying channel conditions. Additionally, investigating the impact of different CSI encoding strategies and embedding techniques could help optimize the performance of the CSI embedding method further.

Given the focus on computational efficiency, how might the MambaJSCC approach be leveraged in resource-constrained edge computing or IoT applications

In resource-constrained edge computing or IoT applications, the MambaJSCC approach can be leveraged by optimizing the model for deployment on devices with limited computational resources. This optimization can involve techniques such as model quantization, pruning, or compression to reduce the model size and computational complexity while maintaining performance. Additionally, implementing hardware accelerators or specialized processors tailored for deep learning tasks can enhance the efficiency of running the MambaJSCC model on edge devices. By tailoring the model architecture and training process to suit the constraints of edge computing environments, MambaJSCC can be effectively utilized for real-time semantic communication tasks in IoT applications.