Forward transmission network resource allocation method and device

Abstract

One or more embodiments of the present specification provide a forward transmission network resource allocation method, the method comprising the steps of: acquiring current traffic data in a forward transmission network, and inputting the current traffic data into a trained traffic type prediction model to obtain a traffic type of predicted traffic data corresponding to the current traffic data; determining a request queue priority (RQP) value corresponding to the predicted traffic data according to the traffic type of the predicted traffic data; and determining a resource allocation mode corresponding to the predicted traffic data according to the traffic type and the RQP value corresponding to the predicted traffic data. Visibly, the accuracy of the flow prediction type can be improved through flow carding, resource allocation is carried out according to the flow type, the resource utilization rate of the forward transmission network can be effectively improved, and the network congestion rate is reduced, so that targeted resource allocation can be carried out according to different flow types, the resource utilization rate of the forward transmission network is further improved, and the transmission latency is reduced.

Description

technical field [0001] One or more embodiments of this specification relate to the field of communications technologies, and in particular, to a method and device for allocating fronthaul network resources. Background technique [0002] With the continuous advancement of mobile communication networks, more and more network applications, such as smart meters, video surveillance, and healthcare monitoring, have caused rapid growth in mobile communication fronthaul traffic. Due to the complexity and variety of fronthaul traffic, the response of the fronthaul network to traffic requests faces the problems of high latency and low utilization of network resources. Network resource allocation is carried out in accordance with the order of traffic requests, and the diversification and complexity of traffic types will cause some traffic requests with high delay requirements to be in a waiting state in the link, resulting in reduced user satisfaction; traffic with different bandwidth ...

AI Technical Summary

Problems solved by technology

However, simple traffic prediction is not enough for the complex traffic resource allocation of the fronthaul network, because different types of traffic have very different resource requests, and only reserving resources may cause traffic requests with high latency requirements to be blocked in the link

However, due to the diversification of traffic types, failure to sort out traffic may intensify the fragmentation of spectrum resources, resulting in a vicious circle that leads to reduced utilization of fronthaul network resources and network congestion.

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