Resource allocation method

Abstract

The invention provides a resource allocation method. The resource allocation method includes that a correlation degree interval is equally divided into N correlation degree subintervals in advance, before the operation of a system, through simulation or actual measurement methods, the signal-to-noise ratio attenuation value delta SINRmax from a single-user mode to a multi-user mode, which each correlation degree subinterval corresponds to, is determined, when the system operates, an equivalent channel matrix of a downlink channel matrix of each user to be scheduled in a cell is obtained, as for each resource in a system resource set, the priority of users is calculated on each resource, a user with highest priority is selected as a main user, and the signal-to-noise ratio attenuation value delta SINRmax from a single-user multiple in multiple out transmission mode to a multi-user multiple in multiple out transmission mode, which each subinterval corresponds to, is utilized so as to select an optimum pairing user from the users to be scheduled of the cell according to the instantaneous speed and the equivalent channel matrixes of the users. By means of the resource allocation method, the accuracy of user pairing during the resource allocation can be improved, and the complexity is low.

Description

technical field [0001] The invention relates to mobile communication technology, in particular to a resource allocation method when a multi-user multiple-input multiple-output (MU-MIMO) technology is introduced into an LTE system. Background technique [0002] Downlink multiple input multiple output (MIMO) technology, as one of the key LTE downlink technologies, can utilize space diversity and multiplexing technologies to improve transmission reliability of the LTE system and increase system capacity. Compared with point-to-point single-user MIMO, point-to-multipoint multi-user MIMO (MU-MIMO) adopts space division multiple access (SDMA) technology, which can simultaneously transmit data for multiple users on the same time-frequency resource through SDMA , and eliminate the inter-stream interference of different spatial sub-channels through precoding at the transmitter; multi-user MIMO can bring more multi-user diversity gain, and in the scenario where there are many users in...

AI Technical Summary

Problems solved by technology

[0010] In the above pairing scheme, the greedy algorithm needs to traverse each user in the process of pairing users, and calculate the ZF beamforming matrix of the candidate pairing user and the main user, so a large number of matrix pseudo-inverse operations are required, which greatly increases the computational complexity; In contrast, the semi-orthogonal pairing algorithm can greatly reduce the computational complexity, but since the channel correlation threshold is a fixed value, the pairing process is only for users whose correlation is lower than the threshold. In this way, some channels have better quality but lower correlation. Large users will not get the chance to pair, and the inaccuracy of this pairing may lead to performance degradation, especially in the case of a small number of users in the cell

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