Gibbs sampling-based ultra-dense heterogeneous network optimal cell range expansion bias adjustment method

Abstract

The invention discloses a Gibbs sampling-based ultra-dense heterogeneous network optimal cell range expansion bias adjustment method. The method includes the following steps that: inter-cell bias influence relation diagram is constructed; small cells are grouped based on the bias influence relation diagram; a user judges candidate service cells; user number, signal to interference plus noise power and effectiveness update information are interacted between adjacent cells; and cell range expansion bias sampling is carried out based on Gibbs sampling. With the method adopted, ultra-dense heterogeneous network optimal cell range expansion bias, with optimizing any rate related system effectiveness adopted as a target, can be obtained with linear complexity and minimal iterations.

Description

technical field [0001] The invention belongs to the technical field of wireless access in mobile communication, and in particular relates to a Gibbs sampling-based ultra-dense heterogeneous network cell range extension offset adjustment method in a wireless communication system. Background technique [0002] Ultra-dense heterogeneous network is a key technology in the next-generation mobile communication system. It improves network spectrum utilization and network capacity by densely deploying low-power small cells within the coverage of macro cells. However, the power difference between macro cells and small cells will limit the coverage of small cells, leading to problems such as limited network capacity gain and inter-cell load balancing. Traditional user access optimization methods are sensitive to network topology changes and need to be iteratively run to adapt to network changes. The computational complexity is too high, and it is not suitable for large-scale ultra-den...

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Problems solved by technology

However, the power difference between macro cells and small cells will limit the coverage of small cells, leading to problems such as limited network capacity gain and inter-cell load balancing.

Traditional user access optimization methods are sensitive to changes in network topology and require continuous iterative operation to adapt to network changes. The computational complexity is too high, and they are not suitable for large-scale ultra-dense heterogeneous network scenarios with a large number of sites and complex topologies.

The cell range extension technology can simply force users to access the small cell to expand the coverage of the small cell by setting a specific cell range extension bias for each small cell, and effectively solve the problems of ultra-dense heterogeneous network capacity and load balancing. However, how to adjust the cell range expansion bias to maximize network utility is a non-deterministic polynomially difficult (NP-hard) problem

In addition, the existing user access and cell range expansion methods only aim to improve a certain rate-related system utility, such as improving network capacity or load balancing, and there is no general method to solve the optimization problem of all rate-related utility functions

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