宽带大规模MIMO-OFDM系统自适应稀疏信道估计方案

大规模MIMO-OFDM系统下行链路利用压缩感知算法获得信道状态信息需要已知信号的稀疏度作为先验条件,然而实际环境中,无线信道的稀疏度是未知的。利用大规模MIMO信道的空时共同稀疏性的特点与不同SNR下设置不同停止迭代阈值的思想改进压缩感知重构算法,目的在于使所提算法不仅提升估计性能,还可以准确获得信道的动态稀疏度。通过实验可知,相比传统的CoSaMP算法和S-CoSaMP算法,SSA-CoSaMP算法在同等信噪比下具有更良好的信道估计性能,并且可以自适应地获取稀疏度,更适合实际工程中应用。

基于稀疏聚类和信任度的协同过滤算法

针对数据稀疏性问题,提出一种基于稀疏子空间聚类和预测评分的协同过滤算法。利用稀疏子空间聚类对用户评分矩阵进行聚类,可以保留更多有用信息。考虑用户评分尺度和用户之间的可信度问题,提出融合信任度的概念,通过计算用户间的信任度,最终使用用户间的信任度与相似度的结合作为新的权重进行推荐。

AGCD： a robust periodicity analysis method based on approximate greatest common divisor

Periodicity is one of the most common phenomena in the physical world. The problem of periodicity analysis （or period detection） is a research topic in several areas, such as signal processing and data mining. However, period detection is a very challenging problem, due to the sparsity and noisiness of observational datasets of periodic events. This paper focuses on the problem of period detection from sparse and noisy observational datasets. To solve the problem, a novel method based on the approximate greatest common divisor （AGCD） is proposed. The proposed method is robust to sparseness and noise, and is efficient. Moreover, unlike most existing methods, it does not need prior knowledge of the rough range of the period. To evaluate the accuracy and efficiency of the proposed method, comprehensive experiments on synthetic data are conducted. Experimental results show that our method can yield highly accurate results with small datasets, is more robust to sparseness and noise, and is less sensitive to the magnitude of period than compared methods.

Channel estimation for multi-panel millimeter wave MIMO based on joint compressed sensing

Channel state information(CSI)is essential for downlink transmission in millimeter wave(mmWave)multiple input multiple output(MIMO)systems.Multi-panel antenna array is exploited in mmWave MIMO system due to its superior performance.Two channel estimation algorithms are proposed in this paper,named as generalized joint orthogonal matching pursuit(G-JOMP)and optimized joint orthogonal matching pursuit(O-JOMP)for multi-panel mmWave MIMO system based on the compressed sensing(CS)theory.G-JOMP exploits common sparsity structure among channel response between antenna panels of base station(BS)and users to reduce the computational complexity in channel estimation.O-JOMP algorithm is then developed to further improve the accuracy of channel estimation by optimal panel selection based on the power of the received signal.Simulation results show that the performance of the proposed algorithms is better than that of the conventional orthogonal matching pursuit(OMP)based algorithm in multi-panel mmWave MIMO system.

Krigings over space and time based on latent low-dimensional structures

We propose a new nonparametric approach to represent the linear dependence structure of a spatiotemporal process in terms of latent common factors.Though it is formally similar to the existing reduced rank approximation methods,the fundamental difference is that the low-dimensional structure is completely unknown in our setting,which is learned from the data collected irregularly over space but regularly in time.Furthermore,a graph Laplacian is incorporated in the learning in order to take the advantage of the continuity over space,and a new aggregation method via randomly partitioning space is introduced to improve the efficiency.We do not impose any stationarity conditions over space either,as the learning is facilitated by the stationarity in time.Krigings over space and time are carried out based on the learned low-dimensional structure,which is scalable to the cases when the data are taken over a large number of locations and/or over a long time period.Asymptotic properties of the proposed methods are established.An illustration with both simulated and real data sets is also reported.