MIMO user selection algorithm based on MMSE precoding and simulated annealing algorithm

Abstract

The invention discloses an MIMO user selection algorithm based on MMSE precoding and a simulated annealing algorithm. According to the MIMO user selection algorithm, MMSE precoding is firstly used, so that communication interference between multiple users is effectively restrained. Compared with a linear precoding method used usually, MMSE precoding can support more receiving side users and reduce power waste. Then the simulated annealing algorithm and the concept that the local optimal solution can jump out in a probabilistic mode, and finally global optimum is tended to are used for reference, the new MIMO user selection algorithm is provided, while low algorithm complexity is kept, the good channel capacity and error rate performance are achieved, and therefore the MIMO user selection algorithm is suitable for practical application.

Description

technical field [0001] The invention relates to a MIMO user selection algorithm based on MMSE precoding and simulated annealing algorithm. Background technique [0002] The MIMO (multi-input multi-output) technology has attracted more and more attention because it can double the channel capacity, and has become one of the key technologies of the new generation of mobile communication. In recent years, the research hotspot of MIMO system has gradually expanded from single user to point-to-multipoint multi-user system. In a multi-user MIMO system, the number of users that the base station can serve at the same time is limited, so user selection is required to select an appropriate user set for transmission to obtain multi-user diversity gain and ensure good system performance. [0003] In recent years, people have conducted in-depth research on multi-user MIMO user selection algorithms. Among them, the representative finite search selection algorithm, the maximum norm select...

AI Technical Summary

Problems solved by technology

The poor search selection algorithm selects the user set with the largest channel capacity by traversing all possible user sets. This algorithm can obtain the optimal channel capacity performance, but the computational complexity is too high; the maximum norm selection algorithm calculates each user The norm of the channel matrix is ​​sorted from large to small, and the top K are selected as the final user set. This algorithm has low computational complexity, but the performance of the selected user set is also low; the greedy selection algorithm uses iterative operations, and each step Select the local optimal set, and generate the final user set after the iteration is completed. The computational complexity of the greedy selection algorithm increases linearly with the number of users, and the complexity growth rate is much lower than that of the poor search algorithm, but the performance is far from the minimum Excellent performance still has some room for improvement

Moreover, because the above three algorithms do not use precoding technology, the channels between various users will interfere with each other, reducing the total channel capacity.

Although the user selection algorithm based on linear precoding can completely eliminate the interference between users, it causes a waste of transmit power and has a strict limit on the number of users at the receiving end.

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