Mamba for Wireless Communications and Networking: Principles and Opportunities

Mamba has emerged as a promising model for efficiently addressing tasks involving temporal and spatial data. Regarding the escalating heterogeneity and dynamics in wireless networks, Mamba holds the potential to advance wireless communication and networking designs by balancing the trade-off between computational efficiency and effectiveness. This article presents a comprehensive overview of Mamba's applications in wireless systems. Specifically, we first analyze the potential 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 computational efficiency and feature enhancement, as reflected by reduced inference time and higher Bilingual Evaluation Understudy (BLEU) scores. Finally, we highlight critical challenges and outline potential research directions for Mamba in wireless communications and networking.