User selecting method of multi-user MIMO communication system based on codebook

Abstract

The invention provides a user selecting method of a multi-user MIMO communication system based on a codebook. An e Node B groups and selects dispatch users according to a PMI and a CQI feedback of users in a cell. The dispatch users are grouped according to the quasi orthogonality among codebook vectors expressed by the PMI. When the system runs, the e Node B ensures a signal path area which belong to each user according to the PMI feedback by a UE, selects a user with the maximal CQI to be taken as an optimum user of the area in the signal path subarea according to the CQI reported by the UE, computes the combined spectrum efficiency of each paired user group according to a previously set quasi orthogonality relationship, and selects one group of users with the largest combined spectrum efficiency to be taken as the current paired user group. The method can greatly simplify the computing complexity selected by the user in the multi-user MIMO communication system and can obtain the throughput performance near the optimum.

Description

technical field [0001] The invention belongs to the technical field of multi-user equipment scheduling in the wireless communication field, and in particular relates to a low-complexity multi-user equipment scheduling method based on a codebook in a multi-user equipment MIMO system. Background technique [0002] In a wireless communication system, multiple antennas are used on the transmitter and receiver to form a multiple-input multiple-output (MIMO) system. This transmission method can enhance the reliability of wireless transmission and increase the capacity of the wireless communication system. Theory proves that in a MIMO multi-user equipment system, the system can obtain multiplexing gain through linear precoding technology, that is, at a time, it can send signals to several user equipments at the same time. Linear precoding technology is currently a relatively practical and concerned technology. This technology uses an orthogonal precoding matrix for each user equipm...

AI Technical Summary

Problems solved by technology

However, this method cannot guarantee to obtain the optimal quasi-orthogonal user equipment subset, and at the same time, in SUS, multiple searches and comparisons of user equipment are required, so the computational complexity is still high

In addition, in the SUS algorithm, the transmitter needs channel state information for multi-user equipment scheduling, but in the actual system, the transmitter cannot accurately know the channel state information

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