Face recognition methods analysis

Abstract

In future 5G wireless systems with wide-band MIMO and ultra-high-density cells, the reduced delay back and forth between mobile device and base station requires 1ms primary band processing delay. In a large-scale MIMO downlink(DL) system, An orthogonal pilot sequence is send to base station from user for channel estimation of uplink training. The advanced MIMO downlink TDD systems technology has been developed taking into account the effects of experimental pollution on channel estimation with consideration of Uplink(UL). The pre-coding system is widely applied with experimental reuse factors to mitigate the effects of experimental pollution. This achieves a higher transfer rate than traditional methods. The first PLS technology considered in this proposed method is a possible beam forming technique using MIMO. In addition to beam formation, the artificial noise (AN) technology is also validated. AN also reduces the transmitter's listening ability to detect and decode information signals addressed to the intended receiver by generating random noise signals in addition to information signals. This proposed work provides current channel estimation and MIMO detection algorithms for MIMO on a large scale to reduce the computational cost of broadband matrix computing. Channel reciprocity allows the base station only to obtain channel status information (CSI) from UL pilots. Two current algorithms have been introduced for each LS, MMSE, ZF and MMSE procedure based on a simplified uplink model, performance of this traditional approach has been measured in MSE and BER. MATLAB simulation is performed based on the proposed massive MIMO and the results are presented.