Load balancing method based on integral optimization of user connection and interference management in ultra-dense heterogeneous network

Abstract

The invention discloses a load balancing method based on integral optimization of user connection and interference management in an ultra-dense heterogeneous network. The load balancing method comprises the following steps that: an optimization model is established; a load balancing model for integrally optimizing the user connection and an ABS (Almost Blank Subframe) rate is established targeting the sum of all user logarithmic rates in a maximal ultra-dense heterogeneous network; a user connection method is that optimal user connection is solved by using a discrete dual decomposition method under a fixed ABS rate; an ABS rate calculation method is that a calculation expression of an optimal ABS rate is solved under a fixed user connection condition; then, a final result is obtained; and a global second-best solution is obtained through a method by solving the user connection and the ABS rate through iteration. By adopting the method disclosed by the invention, the balance of a load and the fairness of the user rate in the ultra-dense heterogeneous network can be improved; and simultaneously, the utilization rate of ultra-dense heterogeneous network resources can be improved.

Description

technical field [0001] The invention relates to the problem of user connection in an ultra-dense heterogeneous network, and belongs to the technical field of network in wireless communication. Background technique [0002] In the heterogeneous network of the Long Term Evolution and its subsequent evolution (LTE-A, Long Term Evolution-Advanced) system, deploying high-density small cells has become a common strategy, thereby forming an ultra-dense heterogeneous network. [0003] In an ultra-dense heterogeneous network, base stations of different types (for example, they can be distinguished according to transmission power) form different layers, and base stations of the same type are in the same layer. For example, a base station with low transmission power is called a small station, and all small stations form a small station layer, a base station with high transmission power is called a macro station, and all macro stations form a macro station layer. Due to the difference ...

AI Technical Summary

Problems solved by technology

However, in an ultra-dense heterogeneous network, due to the large difference in transmit power between large and small stations, users offloaded from macro stations to small stations will be subject to strong interference from macro stations, causing the user's signal-to-interference-plus-noise ratio (SINR , SignaltoInterferenceplusNoiseRatio) is lower

As a result, the throughput obtained by users from the small site decreases, and the resources of the small site cannot be fully utilized

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