Flow unloading method based on reference signal received power (RSRP)

Abstract

The invention discloses a flow unloading method based on reference signal received power (RSRP). The method includes the following steps: S1 initializing, generating a network deployment diagram, generating base stations and users and generating macro cell users and pico cell users; S2 partially multiplexing frequency resources of a heterogeneous network; S3 building loading tables and RSRP tables received by the users; S4 selecting the users meeting the unloading condition that the unloading condition of macro cells is rk and i+RSRP0 >/=rk, 0, and the unloading condition of pico cells is that rk, j, j belongs to [1, 2, 3, 4, 5, 6]/i, rk, j>/=rk, i; S5 selecting target pico cells switched by the users; S6 updating the loading tables of all cells and the RSRP tables of all the users. By means of the method, system energy efficiency, access capacity and covering range are improved by conducting flow unloading on the macro cells and the pico cells meeting the unloading conditions, system spectrum efficiency can be improved, and interference received by edge users of the pico cells and from the macro cells and the neighborhood pico cells can be reduced.

Description

technical field [0001] The invention relates to an RSRP-based traffic unloading method. Background technique [0002] With the rapid development of mobile Internet and smart terminals, data services are also growing explosively. However, the rapid development of mobile Internet has also brought great challenges to the communication industry. The deployment of a large number of communication equipment will inevitably bring about a sharp increase in energy consumption. , At present, the communication industry with high energy consumption has become one of the main industries that emit greenhouse gases. In addition, in some specific scenarios, such as dense urban areas, the traditional macro base station cell splitting technology can no longer guarantee the service quality of the growing number of users, and the cost of deploying macro base stations is too high. Therefore, the overlapping deployment of low-power base stations in the coverage area of ​​macro base stations under...

AI Technical Summary

Problems solved by technology

Therefore, unloading the traffic of macro base stations through low-power base stations is a feasible solution in terms of energy efficiency, capacity, and coverage of communication systems. The deployment of low-power base stations in heterogeneous networks can improve the energy efficiency of the system, and at the same time It will reduce the quality of service (QoS) of users at the edge of the cell

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