Group Relative Policy Optimization for Robust Blind Interference Alignment with Fluid Antennas

TL;DR

This paper introduces a novel deep reinforcement learning framework, GRPO, for optimizing fluid antenna positions to enhance robust blind interference alignment in wireless systems with imperfect CSI.

Contribution

It proposes the first robust fluid antenna-driven BIA framework using GRPO, a new RL algorithm that improves performance and reduces computational complexity.

Findings

GRPO outperforms PPO by 4.17% in sum-rate maximization.

Pre-trained PPO with 100K steps improves by 30.29%.

GRPO exceeds heuristic methods by over 200%.

Abstract

Fluid antenna system (FAS) leverages dynamic reconfigurability to unlock spatial degrees of freedom and reshape wireless channels. Blind interference alignment (BIA) aligns interference through antenna switching. This paper proposes, for the first time, a robust fluid antenna-driven BIA framework for a K-user MISO downlink under imperfect channel state information (CSI). We formulate a robust sum-rate maximization problem through optimizing fluid antenna positions (switching positions). To solve this challenging non-convex problem, we employ group relative policy optimization (GRPO), a novel deep reinforcement learning algorithm that eliminates the critic network. This robust design reduces model size and floating point operations (FLOPs) by nearly half compared to proximal policy optimization (PPO) while significantly enhancing performance through group-based exploration that escapes bad local optima. Simulation results demonstrate that GRPO outperforms PPO by 4.17%, and a 100K-step pre-trained PPO by 30.29%. Due to error distribution learning, GRPO exceeds heuristic MaximumGain and RandomGain by 200.78% and 465.38%, respectively.