Technology Category
Search and Optimization: Sampling/Simulation-based SearchPlanning, Routing, and Scheduling: Optimization of Spatio-temporal SystemsMultiagent Systems: Mechanism Design
Greedy and Transformer-Based Multi-Port Selection for Slow Fluid Antenna Multiple Access
🤖 AI Summary
This work addresses the port selection problem for multi-port receivers in Fluid Antenna Multiple Access (FAMA) systems, where existing approaches struggle to balance spectral efficiency and computational complexity. To tackle this challenge, two complementary methods are proposed: first, a greedy forward selection algorithm enhanced with swap-based refinement (GFwd+S) that significantly improves spectral efficiency; second, a novel application of the Transformer architecture to this task, which leverages imitation learning for pretraining and Reinforce-based policy gradient fine-tuning to achieve performance close to GFwd+S while drastically reducing computational overhead. Together, these approaches establish a new trade-off between performance and complexity, substantially enhancing the practicality of FAMA systems.
Technology Category
Application Category
📝 Abstract
We address the port-selection problem in fluid antenna multiple access (FAMA) systems with multi-port fluid antenna (FA) receivers. Existing methods either achieve near-optimal spectral efficiency (SE) at prohibitive computational cost or sacrifice significant performance for lower complexity. We propose two complementary strategies: (i) GFwd+S, a greedy forward-selection method with swap refinement that consistently outperforms state-of-the-art reference schemes in terms of SE, and (ii) a Transformer-based neural network trained via imitation learning followed by a Reinforce policy-gradient stage, which approaches GFwd+S performance at lower computational cost.
Problem
Research questions and friction points this paper is trying to address.
port selection
fluid antenna
multiple access
spectral efficiency
computational complexity
Innovation
Methods, ideas, or system contributions that make the work stand out.
fluid antenna
multi-port selection
greedy algorithm
Transformer-based neural network
imitation learning
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