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This paper aims to provide a comprehensive scheme with limited feedback for downlink millimeter wave (mmWave) multiuser multiple-input multiple-output (MIMO) nonorthogonal multiple access (NOMA) system. Based on the feedback of the best beam and the channel quality information (CQI) on this beam, the users are grouped into a cluster having the same or coherent best beam and the maximal CQI-difference. To further reduce the intercluster interference, only the candidate cluster can join the cluster set whose intercluster correlation with the existing clusters is lower than threshold. Based on the results of clustering, mmWave hybrid beamforming is designed. To improve the user experience, each cluster selects the best beam of the user with the higher guaranteed rate requirement as the analog beamforming vector. For digital beamforming, the weak user applies the block diagonalization algorithm based on the strong user’s effective channel to reduce its intracluster interference. Finally, an intracluster power allocation algorithm is developed to maximize the power difference in each cluster which is beneficial to improve the successive interference cancelation (SIC) performance of the strong user. Finally, simulation results show that the proposed MIMO-NOMA scheme offers a higher sum rate than the traditional orthogonal multiple access (OMA) scheme under practical conditions.

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To efficiently make use of the temperature information in near-infrared (NIR) spectra, a new hybrid algorithm named as WP-WNPLS is proposed to improve the prediction ability of partial least square (PLS) based regression model. In WP-WNPLS, the discrete wavelet packet transform (DWPT) was firstly applied to decompose the 3-D NIR spectra into a series of frequency components. In each frequency component, a sub-model was obtained through using N-way PLS (NPLS) regression. Then, the weighted strategy was employed to take the advantage of multi-scale properties, and all the sub-models were mixed together to build the final weighted-prediction model. To validate the WP-WNPLS algorithm, it was applied to measure the fat concentration of milk using NIR spectra at different temperatures. The experimental results showed that the prediction ability of model obtained was superior to that obtained using conventional PLS algorithm, and the root mean square error of prediction can improve by up to 18.1%, indicating that it is a promising tool for NIR spectra regression model development. Principle and Method N-way Partial Least Squares (NPLS) Algorithm Assume X is a m×n×l data matrix with m samples (first order), n measurements in the second order and l measurements in the third order. In addition, assume Y is the m×p concentration matrix with p calibration properties in m samples. NPLS [5] algorithm is the high dimensional version of PLS. In this paper, the emphasis is on the 3-D space. The method of applying NPLS algorithm to 3-D space can be described as: