Large Language Models (LLMs) Assisted Wireless Network Deployment in Urban Settings

TL;DR

A novel Reinforcement Learning (RL) based framework that leverages LLMs for network deployment in wireless communications and suggests that LLM-assisted models can outperform CNN-based models in some cases while performing at least as well in others.

Abstract

The advent of Large Language Models (LLMs) has revolutionized language understanding and human-like text generation, drawing interest from many other fields with this question in mind: What else are the LLMs capable of? Despite their widespread adoption, ongoing research continues to explore new ways to integrate LLMs into diverse systems. This paper explores new techniques to harness the power of LLMs for 6G (6th Generation) wireless communication technologies, a domain where automation and intelligent systems are pivotal. The inherent adaptability of LLMs to domain-specific tasks positions them as prime candidates for enhancing wireless systems in the 6G landscape. We introduce a novel Reinforcement Learning (RL) based framework that leverages LLMs for network deployment in wireless communications. Our approach involves training an RL agent, utilizing LLMs as its core, in an urban setting to maximize coverage. The agent's objective is to navigate the complexities of urban environments and identify the network parameters for optimal area coverage. Additionally, we integrate LLMs with Convolutional Neural Networks (CNNs) to capitalize on their strengths while mitigating their limitations. The Deep Deterministic Policy Gradient (DDPG) algorithm is employed for training purposes. The results suggest that LLM-assisted models can outperform CNN-based models in some cases while performing at least as well in others.

Figures (8)

• Figure 1: A conceptual diagram of the DDPG algorithm, showcasing the interactions between the actor and critic networks, the policy behavior, and the value functions.
• Figure 2: The 'Munich' environment from different angles.
• Figure 3: In all of these scenarios, we want all the users to get the maximal signal reception.
• Figure 4: A visualization of the coverage map that is obtained during the training.
• Figure 5: The network architecture of critic model in DDPG is depicted. The left branch processes the coverage map as the state observation with consecutive CNN layers. The reason of using two consecutive CNN layers with reducing sizes is to avoid having too many parameters in the following fully connected layer. The right branch processes the action decided by the actor model. Both branches use fully connected layers to return outputs in the same size, which in our case is 256. The outputs of both branches are added up and passed through another fully connected layer to give a scalar State-Action Value.