Problem
Research questions and friction points this paper is trying to address.
Energy Efficiency
User-Centric Cell-Free Massive MIMO
Serving Cluster Formulation
WIP: Energy-Efficient LLM-Based Serving Cluster Formulation in Cell-Free Massive MIMO
📝 Abstract
One way to increase the Energy Efficiency (EE) of 6G wireless networks is to utilize existing network infrastructure more efficiently. This can be achieved by introducing User-Centric Cell-Free Massive Multiple-Input-Multiple-Output (UCCF MMMIMO), which allows for simultaneously serving a single user by multiple Base Stations (BSs). From this perspective, the key challenge is to decide which BSs should serve a given user, known as the Serving Cluster Formulation (SCF). In this paper, we propose to deal with this problem by using an Artificial Intelligence (AI) agent based on a Large Language Model (LLM), targeting improvement of EE. We evaluated the proposed AI agent using a complex, 3D Ray Tracer-based, cellular network simulator, comparing a few GPT models and state-of-the-art algorithms. The results show up to 32% gain in EE of the proposed AI agent compared to the baseline.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Large Language Model
Energy Efficiency
User-Centric Cell-Free Massive MIMO
Serving Cluster Formulation
6G Wireless Networks
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2024-03-28Conference on Computer Communications WorkshopsCitations: 0
