A Heterogeneous Network Resource Allocation Method Based on Reinforcement Learning

Abstract

The invention discloses a heterogeneous network resource allocation method based on reinforcement learning. Firstly, a DNN framework is deployed in each base station. The framework is based on the ADMM algorithm, and the channel information is regarded as the weight of the network; according to the data obtained by the base station, that is The current user association information and average interference power give the best resource allocation strategy in the current state; each base station is regarded as an independent subject, and the state of the base station is used as a modeling environment; several agents observe the same heterogeneous network environment and Take actions, and at the same time, the agents communicate with each other through the rewards of the environment; the agents adjust the policy according to the rewards; the resource allocation method provided by the present invention is based on the deep learning network, and the resource allocation plan can be given without all CSI information, while considering the spectrum efficiency , setting the spectral efficiency function as the reward of the agent can guarantee the spectral efficiency while ensuring the system throughput.

Description

technical field [0001] The invention relates to the technical field of wireless communication, and mainly relates to a method for allocating heterogeneous network resources based on reinforcement learning. Background technique [0002] With the rapid growth of mobile devices and the emergence of the Internet of Things, next-generation wireless networks face the great challenge of coping with the proliferation of wireless applications. The most promising solution is to augment existing cellular networks with picocells and femtocells with various transmission powers and coverages. These heterogeneous networks (HetNets) can transfer user equipment (UE) from macro base stations (MBS) to pico base stations (PBS), which differ in transmission power and coverage. Furthermore, in order to achieve high spectral efficiency in heterogeneous networks, PBS can reuse MBS and share the same channel with MBS. Therefore, heterogeneous networks are considered to be a good strategy to increa...

AI Technical Summary

Problems solved by technology

However, these methods require nearly complete information, which may not often be available

Therefore, for the above methods, it is challenging to achieve an optimal solution without such complete information

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