Heterogeneous network resource allocation method based on reinforcement learning

A heterogeneous network and resource allocation technology, applied in the field of heterogeneous network resource allocation based on reinforcement learning, can solve problems such as unavailability

Active Publication Date: 2021-02-09
NANJING UNIV OF POSTS & TELECOMM
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  • Heterogeneous network resource allocation method based on reinforcement learning
  • Heterogeneous network resource allocation method based on reinforcement learning
  • Heterogeneous network resource allocation method based on reinforcement learning

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[0069] The present invention will be further described below in conjunction with the accompanying drawings.

[0070] Such as figure 1 The two-layer heterogeneous cellular network shown includes M base stations and N mobile users, in which the macro base station MBS has , micro base station PBS has and satisfy . Each cell base station is located in the center of each cell, and its authorized mobile users are randomly distributed in the cell. It is assumed that there is an overlapping area between every two adjacent small cells. It is assumed that each communication terminal is equipped with an antenna for signal transmission. In order to maximize the use of radio resources and avoid trivial cases, the frequency reuse factor is set to 1. To avoid intra-cell interference, it is assumed that each user in each cell is allocated only one subcarrier, so all signals are on the same subcarrier The cells in the carrier are orthogonal. The N orthogonal subcarriers used in a cel...

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Abstract

The invention discloses a heterogeneous network resource allocation method based on reinforcement learning, and the method comprises the steps: firstly deploying a DNN frame at each base station, enabling the frame to be based on an ADMM algorithm, and taking channel information as the weight of a network; giving an optimal resource allocation strategy in the current state according to the data obtained by the base station, namely the current user association information and the average interference power; taking each base station as an independent main body, and the state of the base stationas a modeling environment; enabling a plurality of agent programs to observe the same heterogeneous network environment and take action, wherein the agent programs communicate with one another throughrewards of the environment; and enabling agents to adjust policies according to rewards; the resource allocation method provided by the invention is based on the deep learning network, a resource allocation scheme can be given without all CSI information, the spectral efficiency is considered at the same time, and the spectral efficiency function is set as the award of the agent, so that the spectral efficiency can be ensured while the throughput of the system is ensured.

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...

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Application Information

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IPC IPC(8): H04W16/10H04W52/24H04W52/34H04W72/04
CPCH04W16/10H04W52/244H04W52/346H04W72/0453H04W72/0473
Inventor 孙君吴锡
Owner NANJING UNIV OF POSTS & TELECOMM
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