Mamba for Wireless Communications and Networking: Principles and Opportunities

TL;DR

This paper reviews the application of Mamba models in wireless communications, highlighting their potential to improve signal processing, resource allocation, and decoding efficiency amidst network heterogeneity and dynamics.

Contribution

It introduces two frameworks for applying Mamba in wireless systems and demonstrates their benefits through case studies on resource allocation and decoding.

Findings

Mamba enhances feature extraction in wireless signals.

It improves computational efficiency in resource management.

Case studies show significant performance gains.

Abstract

Mamba has emerged as a powerful model for efficiently addressing tasks involving temporal and spatial data. Regarding the escalating heterogeneity and dynamics in wireless networks, Mamba holds the potential to revolutionize wireless communication and networking designs by balancing the trade-off between computational efficiency and effectiveness. This article presents a comprehensive overview of Mamba' applications in wireless systems. Specifically, we first analyze the potentials of Mamba for wireless signal processing tasks from the perspectives of long-range dependency modeling and spatial feature extraction. Then we propose two application frameworks for Mamba in wireless communications, i.e., replacement of traditional algorithms, and enabler of novel paradigms. Guided by the two frameworks, we conduct case studies on intelligent resource allocation and joint source and channel decoding to demonstrate Mamba's improvements in both feature enhancement and computational efficiency. Finally, we highlight critical challenges and outline potential research directions for Mamba in wireless communications and networking.