4 research outputs found
A Multi-Agent Neural Network for Dynamic Frequency Reuse in LTE Networks
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
An application of reinforcement learning for efficient spectrum usage in next-generation mobile cellular networks
This paper proposes reinforcement learning as a foundational stone of a framework for efficient spectrum usage in the context of nextgeneration mobile cellular networks. The objective of the framework is to efficiently use the spectrum in a cellular orthogonal frequency-division multiple access network while unnecessary spectrum is released for secondary spectrum usage within a private commons spectrum accessmodel. Numerical results show that the proposed framework obtains the best performance compared with other approaches for spectrum assignment. Moreover, the framework is relatively simple to implement in terms of computational requirements and signaling overhead
An application of reinforcement learning for efficient spectrum usage in next-generation mobile cellular networks
This paper proposes reinforcement learning as a foundational stone of a framework for efficient spectrum usage in the context of nextgeneration mobile cellular networks. The objective of the framework is to efficiently use the spectrum in a cellular orthogonal frequency-division multiple access network while unnecessary spectrum is released for secondary spectrum usage within a private commons spectrum accessmodel. Numerical results show that the proposed framework obtains the best performance compared with other approaches for spectrum assignment. Moreover, the framework is relatively simple to implement in terms of computational requirements and signaling overhead