While the MambaJSCC architecture presents significant advancements in deep joint source-channel coding, it is not without limitations:
Scalability: The architecture may face challenges when scaling to larger datasets or higher resolutions. As the complexity of the input data increases, the computational overhead may also rise, potentially negating the benefits of the low-complexity design. Future research could explore hierarchical or modular designs that allow for dynamic scaling of the architecture based on the input size, ensuring efficient processing without excessive resource consumption.
Generalization: The performance of MambaJSCC may be highly dependent on the specific training conditions and datasets used. If the model is trained on a narrow range of channel conditions or data types, its generalization to unseen scenarios may be limited. To address this, future work could focus on developing robust training methodologies that incorporate diverse datasets and channel conditions, enhancing the model's adaptability and performance across various scenarios.
Interpretability: As with many deep learning models, the interpretability of the MambaJSCC architecture may be a concern. Understanding how the model makes decisions and processes information is crucial for trust and reliability in communication systems. Future research could investigate techniques for improving the interpretability of the GSSM and CSI-ReST components, potentially through visualization methods or attention mechanisms that highlight the model's decision-making processes.