Locally weighted linear regression based ultra-dense network load balancing optimization method

Abstract

The invention provides a locally weighted linear regression based ultra-dense network load balancing optimization method which jointly regulates cost offset values of all small stations. The locally weighted linear regression based ultra-dense network load balancing optimization method comprises the steps of firstly fitting daily load data collected by a base station by utilizing a locally weighted linear regression method to obtain a load curve of the base station; providing a relatively optimal iterative initial value for a cost based distributed user connection method; and solving the load balancing problem in an ultra-dense heterogeneous network. As a logarithmic function is adopted as a utility function, compromise of opportunity and fairness of resource distribution among users is realized, and users at the edge and in the middle of the base station realize 3.5 times and 2 times data throughput gains respectively. The cost value of each base station is updated through the distributed iteration, and the loads of the base stations in different layers and in the same layer are balanced automatically, and low-complexity load balancing is realized. By setting the initial value through the locally weighted linear regression method and predicting the number of users accessed to the base station at one moment, the time of iterations and the computation complexity are reduced greatly.

Description

technical field [0001] The invention belongs to the technical field of network communication, and relates to a network load balance optimization method, more specifically, to an ultra-dense network load balance optimization method based on local weighted linear regression in a wireless communication system. Background technique [0002] An ultra-dense heterogeneous network with densely deployed low-power small cells at the same frequency within the coverage area of ​​macro cells is an effective method to improve the spectrum utilization and network capacity of the fifth generation mobile communication (5G) network. In the commonly used serving cell selection criterion—the maximum power receiving criterion, each user selects the cell with the strongest received signal power as the serving cell. However, in a heterogeneous network, the power difference between the large station and the small station is relatively large, which will cause load imbalance between layers. In order...

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Problems solved by technology

By relaxing the problem, a low-complexity cost-based distributed method can be obtained to converge to an approximate optimal solution. However, the convergence speed of this cost-based distributed user connection method depends on the selection of iteration parameters. For the complex situation of the actual network, there is no way to adjust the iteration parameters for real-time load changes, and the convergence speed cannot be guaranteed

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