Optimization of base station to user equipment association with the aid of Federated Learning for supporting Mobile Augmented Reality

• Hudson de P. Romualdo UFG
• Luciano de S. Fraga UFG
• Paulo F. da Conceição UFG
• Flávio Geraldo C. Rocha UFG
• Kleber V. Cardoso UFG

DOI: https://doi.org/10.5753/sbrc.2024.1448

Abstract

Immersive applications, such as Mobile Augmented Reality (MAR), depend on adequate communication infrastructure support to meet users’ expectations. In this work, we investigate the problem of associating MAR users with base stations that operate in sub-6 GHz and millimeter waves. The objective is to maximize the immersion of multiple users competing for communication resources, minimizing uplink latency, and maximizing uplink and downlink throughput. Forecasting the position and orientation of each user can significantly contribute to this decision process. To this end, three approaches based on machine learning are evaluated: Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Echo State Network (ESN). However, user information is scattered across base stations due to mobility and contains errors due to sensor inaccuracy. Federated Learning (FL) is then used to receive each user’s local models in a central node, build global models with greater accuracy and use them in association decisions. Finally, due to the complexity of the user association optimization problem, we propose an efficient heuristic for the solution. We observed that ESN presents greater accuracy, in general, while GRU converges faster during training.

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