The invention discloses a fixed subarray space-based millimeter wave beam forming method based on a depth complex network, which simplifies a radio frequency pre-coding matrix and a radio frequency merging matrix through a fixed subarray structure, greatly reduces the requirements of a phase shifter, reduces the hardware dependence and reduces the failure rate compared with the prior art. By constructing the neural network which inputs the real part and the imaginary part of the complex number respectively and is fully connected with the lower-layer neurons, compared with a traditional methodin which the real part and the imaginary part need to be separated into two networks for processing, computing resources are saved; through the design of weight and offset parameter shared imaginary part neuron and real part neuron node expressions, half of network parameters are reduced, the system complexity is greatly reduced, hardware resources are saved, the operation rate is increased, and the real-time performance is improved.

Fixed subarray space-based millimeter wave beam forming method based on depth complex network

The invention provides a low-complexity machine learning assisted robust precoding method and device. In order to consider the common-channel interference performance in a quasi-static channel scene and a quick-change channel scene in a large-scale MIMO downlink system, two different types of precoding matrixes are weighted to obtain a precoding method with robustness. The two precoding matrixes are respectively generated based on an instantaneous channel prediction result and an energy coupling matrix, and the weighting coefficient is obtained through a machine learning method. Compared withan existing robust precoding method, the method has the advantages that the calculation complexity is greatly reduced, and the acceptable sum rate performance can be achieved in a quasi-static channelenvironment and a quick-change channel environment.

Low-complexity machine learning assisted robust precoding method and device

The invention discloses a method for reducing the peak-to-average ratio of a millimeter wave MIMO system. Determining a system model structure and parameters of the millimeter wave MIMO system, determining parameters of a hybrid beam forming matrix of a transmitting end of the millimeter wave MIMO system and parameters of an analog beam forming matrix of a receiving end of the millimeter wave MIMOsystem, and determining parameters of a millimeter wave channel matrix based on ray tracing; constructing an auto-encoder based on a plurality of full connection layers, a layer normalization moduleand a Switch activation layer; training an auto-encoder based on the principle that the bit error rate performance is not deteriorated while the average peak-to-average ratio is minimized to obtain parameters of the auto-encoder; an input signal is encoded by the encoder and subjected to hybrid beam forming at the transmitting end to serve as a transmitting signal, the transmitting signal is transmitted through a channel and then passes through the analog beam forming processor and the decoder at the receiving end, and a final receiving signal is obtained. According to the invention, a betterbit error rate can be maintained while a lower peak-to-average ratio is achieved.

Method for reducing peak-to-average ratio of millimeter wave MIMO system

The invention belongs to the technical field of antennas. The invention relates to an antenna array design method with controllable antenna pattern main lobes. According to the invention, a neural network model is used; the speed of calculating the unit factor is superior to that of the traditional full-wave simulation design; and the extracted unit factors are intermediate units in sub-arrays with sizes of 1*5 (linear array) and 3*3 (area array) respectively, and unit coupling between array antenna units is considered, so that the proposed neural network model reduces the time cost of optimization simulation design on one hand, and can give a more accurate result on the other hand. The method is suitable for a system having specific requirements for radiation characteristics.

Antenna array design method capable of controlling main lobes of antenna pattern

The invention discloses a hybrid beam forming optimization method and system of a multi-carrier MIMO system. A multi-carrier hybrid beamforming neural network is constructed, and the network comprisesa multi-layer full-connection baseband beamforming neural network located at a transmitting end network, a signal parallel-serial conversion module and a single-layer shared analog beamforming neuralnetwork; wherein the single-layer shared analog combined neural network, the signal serial-parallel conversion module and the multi-layer full-connection baseband combined neural network are locatedat a receiving end network. Therefore, the hybrid beam forming system can be completely mapped to a neural network for global optimization; and the consumption problem of the radio frequency link is fully considered, and the analog network is constructed into a single-layer common analog combined neural network. In the training process, a plurality of analog networks are adopted for training respectively, after a certain number of training times, analog network parameters are combined, the optimal analog network parameters are solved in a retraining mode, and the precision of the whole networkmodel is improved.

Hybrid beam forming optimization method and system of multi-carrier MIMO system

The invention discloses a millimeter wave MIMO hybrid beam forming optimization method based on deep learning. According to the millimeter wave MIMO hybrid beam forming optimization method based on deep learning, constraint conditions in a traditional millimeter wave large-scale MIMO hybrid beam forming optimization problem can be mapped into a neural network, and a multi-user hybrid beam formingsystem is completely converted into an equivalent neural network. Therefore, a complex non-convex optimization problem in hybrid beam forming can be converted into end-to-end unsupervised optimizationsimilar to an auto-encoder, and joint optimization can be carried out on a plurality of beam forming matrixes.

Millimeter wave MIMO hybrid beam forming optimization method based on deep learning

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Millimeter wave MIMO hybrid beam forming optimization method based on deep learning