Reseach On Several Key Technologies In Millimeter Wave Communication

Millimeter-wave communication operating in the 30-300 GHz frequency range defines a new era of wireless communication.Compared with the traditional wireless carrier frequency band,it provides a larger order of magnitude bandwidth and higher data transmission rate,but the millimeter wave transmission frequency will cause serious path loss.Therefore,for the ever-increasing demand of users for ubiquitous access to large amounts of wireless data,and the explosive growing demand for wireless communication spectrum efficiency in the future,the combination of millimeter-wave communication and large-scale MIMO has become a general trend.However,with the constraints of limited RF chains,this combination seems full of challenges.At the same time,millimeter-wave communication is also facing radio frequency damage caused by component defects of the analog front end.I/Q imbalance,as one of them,greatly reduces system performance.Therefore,this paper focuses on the estimation and compensation of I/Q imbalance and the relief of the number of RF chains in millimeter-wave massive MIMO systems.Aiming at the frequency-dependent I/Q imbalances at the transceiver end,an improved EM iterative algorithm is proposed under the single carrier frequency-domain equalization system working in the millimeter wave band,which realizes the separate estimation of the channel,I/Q imbalances at the transmitter and the receiver.In the Estep,based on the structural sparsity of the channel,an iterative process similar to sparse Bayesian learning is embedded to estimate the channel;in the M-step,the Newton method is used to maximize the objective function and then to estimate the I/Q imbalance parameters in stages.Finally,according to the obtained estimates,the compensation of I/Q imbalances is realized based on the MMSE criterion.For the problem of RF chains limitation in millimeter-wave massive MIMO systems,the introduction of a lens antenna array to reduce the number of RF chains is considered,and the realization of this goal is based on the correct estimation of the channel.Utilizing the channel sparsity in beam space,we first transform the channel estimation problem into a framework of the sparse signal recovery,and then give two different channel estimation methods.Method one directly uses the EM algorithm to learn the parameters in the framework,method two implements the parameter estimation by combining the OMP and the EM algorithm.Finally,we replace the parameter estimates with channel estimates.The second method is based on the first one,but with much lower complexity.For the above-mentioned algorithms,simulation verification shows its feasibility and effectiveness.At the same time,compared with other existing methods,they have better performance under the premise of considerable computational complexity.