Millimeter wave MIMO user increment cooperative beam selection method based on wide learning

Abstract

The invention discloses a millimeter wave MIMO user incremental cooperative beam selection method based on wide learning, which comprises the following steps that: aiming at the downlink beam selection problem of a multipoint cooperative millimeter wave large-scale MIMO scene, each user collects downlink wide beam response and transmission narrow beam response, trains a local wide learning network, and transmits the downlink wide beam response to the local wide learning network; and beam selection is carried out based on the predicted narrow beam response. Furthermore, the training problem of the local network of each user is modeled into a distributed optimization problem with consistency constraint, and effective sharing of training data can be realized by utilizing D2D communication between adjacent users. Furthermore, an incremental updating mode of the local network of the user in the cooperation mode is designed, so that the training complexity of the network can be effectively reduced. According to the method, the capability of mining the relationship between the multi-base-station wide beam response and the transmission narrow beam response under the small sample condition of distributed wide learning is fully utilized, and low-complexity and low-overhead beam selection of the fast time-varying scene multipoint cooperation millimeter wave large-scale system can be realized.

Description

technical field [0001] The present invention relates to the field of wireless communication network optimization and intelligent communication, in particular to a wide learning-based millimeter wave MIMO user incremental cooperative beam selection method. Background technique [0002] Due to the large bandwidth advantage of the millimeter wave frequency band, and at the same time massive MIMO beamforming can effectively compensate for the fading of millimeter wave propagation by using the array gain, the millimeter wave massive MIMO technology is considered to be one of the key technologies for 5.5G mobile communication systems to achieve higher spectral efficiency. . Then, since mmWave Massive MIMO requires the use of narrow beams for data transmission, it is very sensitive to shadowing effects that exist in the channel. To solve the above problems, cooperative multipoint transmission is considered as the main mode for mmWave massive MIMO systems to achieve their performan...

AI Technical Summary

Problems solved by technology

[0006] In view of this, the object of the present invention is to provide a method for millimeter-wave MIMO user incremental cooperative beam selection based on wide learning, to solve the problem that the current low-overhead beam training method based on wide beam response cannot be directly applied to applications where there is no uplink and downlink channels Reciprocal mmWave multi-point cooperative downlink transmission system is a problem, while optimizing the user-side ML model design, adapting to the weaker computing power, storage capacity and multi-user interaction capability of the user side

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