Aiming at the problem that pilot overhead required by downlink channel estimation of FDD (Frequency-Division Duplexing) massive MIMO (Multiple-Input Multiple-Output) was unaffordable, a pseudo-random pilot optimization scheme based on Compressive Sensing (CS) techniques with non-orthogonal pilot at the base station and the objective to minimize the cross correlation of the measurement matrix was proposed firstly. Then, a crossover and mutation judgment mechanism and an inner loop and outer loop mechanism were introduced to ensure the optimization of pilot sequence. Secondly, a Channel State Information (CSI) estimation algorithm based on CS techniques by utilizing the spatially common sparsity and temporal correlation in wireless MIMO channels was presented. Matrix estimation is performed by using LMMSE (Linear Minimum Mean Square Error) algorithm to accurately obtain CSI. Analysis and simulation results show that compared with random search pilot optimization scheme, location-based optimization scheme, local common support algorithm, Adaptive Structured Subspace Pursuit (ASSP) algorithm, Orthogonal Matching Pursuit (OMP) algorithm and Stepwise Orthogonal Matching Pursuit (StOMP) algorithm, the proposed algorithm can significantly achieve good channel estimation performance in the case of low pilot overhead ratio and low Signal-to-Noise Ratio (SNR).

Concerning the demands of large capacity and high spectrum utilization in future mobile communication system, a method for optimizing the capacity of secondary user system in Multiple-Input Multiple-Output (MIMO) cognitive networks based on Non-Orthogonal Multiple Access (NOMA) was proposed. Firstly, the transmitted signals were pre-coded, and then the cognitive users were clustered according to channel gains. Secondly, the power allocation was performed for users after clustering. Finally, the Non-deterministic Polynomial-hard (NP-hard) multi-cluster objective function was transformed into solving the capacity of each sub-cluster. Meanwhile, taking into account Quality of Service (QoS) of cognitive users and requirement of Successive Interference Cancellation (SIC), the optimal power allocation coefficient, which is a constant between 0 and 1, was solved by using Lagrange function and Karush-Kuhn-Tucker (KKT) condition. The simulation results show that, the proposed method outperforms the average power allocation method. And when the channel quality is poor, compared with the MIMO cognitive network based on Orthogonal Multiple Access (OMA), the proposed method has improved the capacity of secondary user system significantly.