Research On Optimization Of MIMO Signal Detection Algorithm Based On Deep Learning

Multiple-input Multiple-output(MIMO)systems are one of the most significant developments in wireless communication and have been greatly considered as one of the key technologies for the fifth generation(5G)mobile communication systems.MIMO systems by equipping multiple antennas at both the transmit side and the receive side can increase the spectral efficiency and the channel capacity without the need of extra channel bandwidth.However,one of the key challenges faced by MIMO systems is that the computational complexity of signal detection algorithm increases with the number of transmit antennas and receive antennas.In general,an efficient MIMO detection algorithm is supposed to strike a balance between the error rate performance and the computational complexity.The purpose of this thesis is to comprehensively investigate the detection algorithms of MIMO systems,and based on that,to develop new methods which can reduce the computational complexity while retain good system performance.In recent years,owing to strong learning ability from data,the combination of deep learning and communication has attracted great interests because of its outstanding performances in modulation recognition,resource allocation,and physical layer design.However,most of the existing works focus on data-driven deep learning approaches,which consider the communication system as a black box and train it by using a huge volume of data.In order to reduce the demand for labeled data and training time,the model-driven deep learning approaches are introduced in this paper.The model-driven deep learning approaches construct the network based on communication domain knowledge.In this thesis,we study the approximate message passing detection algorithm based on factor graphs in MIMO system.Then,we propose a novel detection network based on the model-driven deep learning method.The network,called Trainable-AMP-Net,is inspired by the approximate message passing(AMP)approach for MIMO detection.By unfolding the iterative AMP detection algorithm into a layer-wise structure and adding trainable parameters in each layer,we obtain neural-network-like architectures.Then,the trainable parameters are tuned by mini-batch stochastic gradient descent methods.Simulation results show that the convergence speed,the detection accuracy and the robustness of the detection algorithm are noticeably improved with the aid of fine-tuned parameters.