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Fractional Frequency Reuse techniques can be employed to address interferencein mobile networks, improving throughput for edge users. There is a tradeoffbetween the coverage and overall throughput achievable, as interferenceavoidance techniques lead to a loss in a cell's overall throughput, withspectrum efficiency decreasing with the fencing off of orthogonal resources. Inthis paper we propose MANN, a dynamic multiagent frequency reuse scheme, whereindividual agents in charge of cells control their configurations based oninput from neural networks. The agents' decisions are partially influenced by acoordinator agent, which attempts to maximise a global metric of the network(e.g., cell-edge performance). Each agent uses a neural network to estimate thebest action (i.e., cell configuration) for its current environment setup, andattempts to maximise in turn a local metric, subject to the constraint imposedby the coordinator agent. Results show that our solution provides improvedperformance for edge users, increasing the throughput of the bottom 5% of usersby 22%, while retaining 95% of a network's overall throughput from the fullfrequency reuse case. Furthermore, we show how our method improves on staticfractional frequency reuse schemes.

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