A GPT-2 model fine-tuned with LoRA or dynamic prompts predicts future fluid antenna ports from past channel tables and outperforms conventional baselines in 3GPP CDL-D simulations.
Transforming Time-Varying to Static Channels: The Power of Fluid Antenna Mobility
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abstract
This paper addresses the mobility problem with the assistance of fluid antenna (FA) on the user equipment (UE) side. We propose a matrix pencil-based moving port (MPMP) prediction method, which may transform the time-varying channel to a static channel by timely sliding the liquid. Different from the existing channel prediction method, we design a moving port selection method, which is the first attempt to transform the channel prediction to the port prediction by exploiting the movability of FA. Theoretical analysis shows that for the line-ofsight (LoS) channel, the prediction error of our proposed MPMP method may converge to zero, as the number of BS antennas and the port density of the FA are large enough. For a multi-path channel, we also derive the upper and lower bounds of the prediction error when the number of paths is large enough. When the UEs move at a speed of 60 or 120 km/h, simulation results show that, with the assistance of FA, our proposed MPMP method performs better than the existing channel prediction method.
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Port-LLM: A Port Prediction Method for Fluid Antenna based on Large Language Models
A GPT-2 model fine-tuned with LoRA or dynamic prompts predicts future fluid antenna ports from past channel tables and outperforms conventional baselines in 3GPP CDL-D simulations.