Mamba for Wireless Communications and Networking: Principles and Opportunities

2025-08-01

AI Summary

In dynamic heterogeneous wireless networks, achieving both high computational efficiency and strong modeling capability remains challenging. Method: This paper systematically investigates the application of the Mamba architecture—characterized by linear-time complexity and long-range dependency modeling—to core wireless tasks, including signal processing, resource allocation, and joint source-channel decoding. We propose two novel frameworks: (i) replacing conventional iterative or heuristic algorithms with Mamba for low-complexity, high-accuracy solutions; and (ii) establishing a state-space-based sequential modeling paradigm that overcomes limitations of classical modular designs. Contribution/Results: The proposed methods achieve significant improvements in intelligent resource allocation and joint decoding: average performance gains of 12.7% and 3.2× acceleration in inference speed. These results validate Mamba’s feasibility and superiority as a foundational intelligence model for next-generation wireless systems.

Technology Category

• Machine Learning: Hardware-aware ML
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Mamba improves wireless signal processing efficiency
• Mamba enhances long-range dependency modeling in networks
• Mamba enables novel paradigms in wireless communications

Innovation

• Mamba for temporal and spatial data tasks
• Balancing computational efficiency and effectiveness
• Application frameworks for wireless communications