Greedy and Transformer-Based Multi-Port Selection for Slow Fluid Antenna Multiple Access
Summary: arXiv:2604.04589v1 Announce Type: new
Abstract
In recent years, the demand for efficient communication systems has surged, particularly in environments where multiple users require simultaneous access to limited bandwidth. The port-selection problem in fluid antenna multiple access (FAMA) systems presents a unique challenge, particularly with multi-port fluid antenna (FA) receivers. Existing methodologies often fall short, either incurring high computational costs for near-optimal spectral efficiency (SE) or offering subpar performance at lower complexity levels.
Introduction
This article explores two innovative strategies designed to tackle the port-selection problem effectively. The proposed solutions aim to bridge the gap between performance and computational efficiency, providing a robust framework for future research in fluid antenna systems.
Proposed Strategies
- GFwd+S: This is a greedy forward-selection method enhanced with swap refinement. It consistently outperforms existing state-of-the-art reference schemes, achieving superior spectral efficiency while still maintaining a manageable computational load.
- Transformer-based Neural Network: This approach utilizes a neural network trained through imitation learning, followed by a Reinforce policy-gradient stage. Remarkably, it approaches the performance levels of GFwd+S but operates at a significantly reduced computational cost, making it a practical alternative.
Performance Evaluation
To evaluate the effectiveness of the proposed methods, extensive simulations were conducted comparing GFwd+S and the Transformer-based model against traditional techniques. The results indicate that both strategies not only enhance spectral efficiency but also reduce the computational burden associated with port selection in FAMA systems.
Conclusion
The advancement of communication technologies necessitates innovative approaches to longstanding challenges in the field. The GFwd+S method and the Transformer-based neural network present viable solutions to the port-selection problem in fluid antenna multiple access systems. By providing high spectral efficiency alongside reduced computational demands, these strategies pave the way for more efficient communication systems capable of meeting the growing demands of modern wireless networks.
Future Work
Looking ahead, further research is warranted to refine these methods, explore their application in different environments, and investigate their integration with emerging technologies in wireless communications. As the landscape of communication continues to evolve, the pursuit of efficient methodologies will be crucial for ensuring robust and scalable systems.