Resource allocation method for reinforcement learning in ultra-dense network

Abstract

A resource allocation method for reinforcement learning in an ultra-dense network is provided. The invention relates to the field of ultra-dense networks in 5G (fifth generation) mobile communications and provides a method for allocating resources between a home node B and a macro node B, between a home node B and another home node B and between a home node B and a mobile user in a dense deployment network; the method is implemented through power control, each femotcell is considered as an intelligent body to jointly adjust transmitting powers of home node Bs, the densely deployed home node Bs are avoided causing severe jamming to a macro node B and an adjacent B when transmitting at maximum powder, and system throughput is maximized; user delay QoS is considered, and traditional 'Shannon capacity' is replaced with 'available capacity' that may ensure user delay; a supermodular game model is utilized such that whole network power distribution gains Nash equilibrium; the reinforcement learning method Q-learning is utilized such that the home node B has learning function, and optimal power distribution can be achieved; by using the resource allocation method, it is possible to effectively improve the system capacity of an ultra-dense network at the premise of satisfying user delay.

Description

technical field [0001] This article relates to the field of mobile communication, in particular, the present invention is a resource allocation method for an ultra-dense heterogeneous network (Ultra Dense Network, UDN) in a fifth-generation (5th-generation) mobile communication system. Background technique [0002] Mobile networks have now entered a stage of rapid popularization. At the same time, countries around the world are actively researching 5G technology, and 5G standards have also begun to emerge. It is a distinctive feature of 5G to take the lead in using cognitive radio technology to automatically determine the frequency band provided by the network to achieve multi-network integration. my country's 5G work has also achieved initial results. The main goal of the 5G network is user experience, and the network needs to be redesigned and optimized in terms of capacity, speed, and delay. At the same time, the 5G network needs to accommodate a large number of termina...

AI Technical Summary

Problems solved by technology

In order to make full use of spectrum resources, the two-tier network shares the same frequency band, but also introduces co-frequency interference, and the existing technology cannot well solve the problem of cross-layer interference in the 5G network. Based on this, the present invention focuses on Research on applying self-optimization technology in self-organizing network to ultra-dense network to realize self-organizing allocation of resources

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