Two Stage Signal Processing of Channel Valuation and Recognition for millimeter MIMO Systems

In millimeter multi-input multi-output (MIMO) systems, signal processing is critical to support the detection and estimation of next generation millimeter wave communication channels. Due to the large number of antenna arrays used in transmitter and receiver, as well as mixed signal and radio frequency power limitations, new MIMO communication signal processing techniques are required. Two realistic millimeter waves can be seen as key targets. •The first is the massive MIMO channel matrix, which is necessary in millimeter wave systems because they require more displays at source and recipient to achieve adequate connection edge. •Another point is that the sum of RF manacles that can be used in the set of transmitters and receivers is limited due to power and cost limitations. From mmWave MIMO, this research provides an accurate two-stage channel valuation system. First we use basic Bayesian learning to get a rough estimate of the channel. By maximizing the probability function in the second stage, the performance of mmMIMO is improved in the second stage. Compared with competitors, the performance of the proposed system is significantly better, and the computational complexity is moderate.