稀疏重建算法及其在大规模MIMO系统中的应用
毕业论文关键词 大规模MIMO 系统，信道估计，稀疏重建算法，波束选择，最大和速率
Title Sparse Reconstruction Algorithm and Its Application in Massive MIMO System
Abstract Massive MIMO system can improve spectrum efficiency, energy efficiency and stability of the system。 It has become one of the most promising research directions in 5G wireless communication。 In this paper, Our important works include the following three aspects: the channel estimation in massive MIMO system, the application of sparse reconstruction algorithm in the theory of compressed sensing to wireless sparse channel estimation in massive MIMO, and the use of beam selection in millimeter wave massive MIMO system。 Beam selection can reduce the number of RF chain without obvious performance loss。 The main works are as follows: (1) We investigate pilot-based methods of the channel estimation which include least squares (LS), minimum mean square error (MMSE), singular-value-decomposition-linear MMSE (SVD-LMMSE)。 We simulate the three methods in OFDM system。 Simulation results show that the mean square error (MSE) and bit error rate (BER) performances of the three algorithms are in decreasing order: MMSE, SVD-LMMSE, and LS, and their complexities are in increasing order: LS、SVD-LMMSE、MMSE。 (2) We study the four channel estimators based on sparse reconstruction algorithms in compressing sensing theory: OMP（Orthogonal Matching Pursuit，OMP）, OLS（Orthogonal Least Squares，OLS）, SP（Subspace Pursuit，SP）。 Simulation results show that their performances of MSE and BER are in decreasing order: OLS, OMP, and SP, and their complexities are in increasing order: OMP, SP, and OLS。 (3) We investigate the beam selection of the millimeter massive MIMO system。 Taking into account the multi-user interference, a low-complexity beam selection scheme is presented to reduce the number of RF chains。 In multipath model, simulation results show that the low-complexity beam selection has higher energy efficiency than conventional approaches and it can achieve near-optimal sum rate。