  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.
