A Multi-Agent Neural Network for Dynamic Frequency Reuse in LTE Networks

• Published in: IEEE/CIC International Conference on Communications in China - Workshops (Online) pp. 1 - 6
• Main Authors: Marinescu, Andrei, Macaluso, Irene, DaSilva, Luiz A.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.05.2018
• Subjects: Bandwidth; Biological neural networks; Geometry; Interference; Measurement; OFDM; Throughput
• ISSN: 2474-9133
• DOI: 10.1109/ICCW.2018.8403663

Fractional Frequency Reuse techniques can be employed to address interference in mobile networks, improving throughput for edge users. There is a tradeoff between the coverage and overall throughput achievable, as interference avoidance techniques lead to a loss in a cell's overall throughput, with spectrum efficiency decreasing with the fencing off of orthogonal resources. In this paper we propose MANN, a dynamic multiagent frequency reuse scheme, where individual agents in charge of cells control their configurations based on input from neural networks. The agents' decisions are partially influenced by a coordinator agent, which attempts to maximise a global metric of the network (e.g., cell-edge performance). Each agent uses a neural network to estimate the best action (i.e., cell configuration) for its current environment setup, and attempts to maximise in turn a local metric, subject to the constraint imposed by the coordinator agent. Results show that our solution provides improved performance for edge users, increasing the throughput of the bottom 5% of users by 22%, while retaining 95% of a network's overall throughput from the full frequency reuse case. Furthermore, we show how our method improves on static fractional frequency reuse schemes.