Leveraging Large Language Models for the Design and Optimization of Fluid Antenna Systems

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Abstract

The Fluid Antenna System (FAS), which enables flexible Multiple-Input Multiple-Output (MIMO) communications, introduces new spatial degrees of freedom for next-generation wireless networks. Unlike traditional MIMO, FAS involves joint port selection and precoder design—an NP-hard combinatorial optimization problem. Moreover, fully leveraging FAS requires acquiring Channel State Information (CSI) across its ports, a challenge exacerbated by the system's near-continuous reconfigurability. These factors make traditional nonlinear optimization methods impractical for FAS design due to nonconvexity and prohibitive computational complexity. While deep learning (DL)-based approaches have been proposed for MIMO optimization, their poor generalization and limited fitting ability render them suboptimal for FAS. In contrast, Large Language Models (LLMs) extend DL's capabilities by offering general-purpose adaptability, reasoning, and few-shot learning, overcoming the limitations of task-specific, data-hungry models. This article presents a vision for LLM-driven FAS design, proposing a novel flexible communication framework. To demonstrate the potential, we examine LLM-enhanced FAS in multiuser scenarios, showcasing how LLMs can revolutionize FAS optimization.

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🛠 Set up Virtual Environment

For a Python virtual environment using venv (Linux/macOS):

python3 -m venv ./venv                 # [optional] create virtual environment
source ./venv/bin/activate             # [optional] activate virtual environment

For a Conda virtual environment:

conda create --name myenv python=3.9
conda activate myenv                  # Activate the Conda environment

🔧 Install dependencies

pip install -r requirements.txt

🔑 Set the OpenAI API key

For Linux/macOS, use the following command in your terminal:

export OPENAI_API_KEY=xxxxxxxxxx  # Replace xxxxxxxxxx with your actual API key

For Windows, use the following command in Command Prompt:

set OPENAI_API_KEY=xxxxxxxxxx  # Replace xxxxxxxxxx with your actual API key

🚀 Run the application

python main.py

🔍 Case 1: Multiuser Communications - Minimize the Total Transmit Power

First, we upload an image, and the Agent will provide a description and identify the scene, then guide the user to input the next content.

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Then, the user inputs the description of the transmitter and receiver, and the Agent outputs the channel model accordingly, then guides the user to input the objective function.

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The user inputs the objective function, and the Agent guides the user to input the constraint condition.

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The user inputs the constraint condition, and the Agent provides the final model.

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🔍 Case 2: Secure Communications - Maximize the secrecy rate

First, we upload an image, and the Agent will provide a description and identify the scene, then guide the user to input the next content.

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Then, The user inputs based on the Agent's guidance.

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The user inputs the objective function, and the Agent guides the user to input the constraint condition.

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The user inputs the constraint condition, and the Agent provides the final model.

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🔍 Case 3: Wireless Power Transfer- Maximize the energy efficiency

First, we upload an image, and the Agent will provide a description and identify the scene, then guide the user to input the next content.

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Then, The user inputs based on the Agent's guidance.

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The user inputs the objective function, and the Agent guides the user to input the constraint condition.

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The user inputs the constraint condition, and the Agent provides the final model.

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🔍 Case 4: ISAC - Maximize the sum rate

First, we upload an image, and the Agent will provide a description and identify the scene, then guide the user to input the next content.

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Then, The user inputs based on the Agent's guidance.

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The user inputs the objective function, and the Agent guides the user to input the constraint condition.

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The user inputs the constraint condition, and the Agent provides the final model.

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📝 Details