We propose a new approach based on compressive sensing (CS) for channel estimation for MIMO-OFDM systems equipped with 2-Dimensional (2D) active antenna arrays. A path-based channel model which is described by delay, ...
详细信息
ISBN:
(纸本)9781479973392
We propose a new approach based on compressive sensing (CS) for channel estimation for MIMO-OFDM systems equipped with 2-Dimensional (2D) active antenna arrays. A path-based channel model which is described by delay, angle of arrival (AOA), and attenuation factor is used in this article. It is popular to assume such MIMO channels are sparse both in the delay-domain and angle-domain, and CS based method can be applied to solve the sparse channel estimation problem. The proposed sparse channel estimation algorithm is divided into three stages. We first find the positions of non-zero taps in time domain and then estimate elevation angle and azimuth angle on each tap jointly. At last, attenuation factor on each tap is obtained. Simulation results show that the proposed method achieves more effective channel estimation performance compared to least square (LS) estimation.
Massive multiple input multiple output (MIMO) is a promising technology for the next generation communication system to increase data rate and throughput. To fully enhance the performance of massive MIMO and improve t...
详细信息
ISBN:
(纸本)9781467376884
Massive multiple input multiple output (MIMO) is a promising technology for the next generation communication system to increase data rate and throughput. To fully enhance the performance of massive MIMO and improve the quality of service, accurate channel state information (CSI) is required for coherent detection. However, due to the overwhelming pilot overhead, conventional pilot aided channel estimation (PACE) approaches are not suitable for massive MIMO systems, especially for frequency-division duplexing (FDD) systems. In this paper, we consider the channel estimation problem in FDD multi-user massive MIMO systems. A spatial correlated channel is first modeled. By exploiting the spatial correlation, the channel can be represented in a sparse form in spatial-frequency domain. Then, the theory of compressive sensing (CS) is applied to develop an effective method for channel estimation. Moreover, based on the inherent common sparsity in the user channel matrices, this paper proposes an improved sparse channel estimation orthogonal matching pursuit (omp) algorithm to reduce the pilot overhead and improve the channel estimation accuracy. Simulation results demonstrate that the proposed algorithm can significantly reduce the pilot overhead and have the superior performance in greatly elevating the accuracy of channel estimation.
This paper proposes a novel solution to realize super-resolution and de-noising for portrait images. Considering that compressive sensing has a good performance on protecting and extracting information in images, it i...
详细信息
ISBN:
(纸本)9780769549231;9781467348935
This paper proposes a novel solution to realize super-resolution and de-noising for portrait images. Considering that compressive sensing has a good performance on protecting and extracting information in images, it is involved to improve super-resolution. Image blocking is carried out in the process of establishing over-complete dictionaries. After vectorizing all blocks of training samples with different resolutions, the low-resolution and high-resolution over-complete dictionaries turn out by means of placing vectors by pair and correspondingly. On this basis, sparse coefficients of each low-resolution image can be worked out through measurement and omp algorithm. Depending upon these coefficients, the desired high-resolution image can be constructed. Additionally a well-chosen sparsity is always an important factor that simplifies calculations and gets rid of noises. The experimental result illustrates the effectiveness and robustness of the novel solution.
Compressed Sensing(CS) can project a high dimensional signal to a low dimensional signal by a random measurement matrix. As the projection calculation is time-consuming in the process of reconstruction, the reconstruc...
详细信息
ISBN:
(纸本)9783037853788
Compressed Sensing(CS) can project a high dimensional signal to a low dimensional signal by a random measurement matrix. As the projection calculation is time-consuming in the process of reconstruction, the reconstruction speed is greatly affected. In order to improve the reconstruction speed, some improvement in the selection of the measurement matrix and the design of the reconstruction algorithm is made. The wavelet transform is used to sparse decompose the image, and the very sparse random projection matrix is used as the measurement matrix, after the image block processing we use the omp algorithm to reconstruct the image. The experimental result shows that this method could reduce the algorithm time and improved the reconstruction speed greatly.
This letter introduces a novel algorithm to construct sensing and measurement dictionaries in compressive sensing using alternating projection method. The cumulative and mutual cross coherence of the constructed dicti...
详细信息
This letter introduces a novel algorithm to construct sensing and measurement dictionaries in compressive sensing using alternating projection method. The cumulative and mutual cross coherence of the constructed dictionaries are lower than those of Gaussian random dictionary. The concept of General Restricted Isometry Constant (GRIC) is introduced. Low cumulative cross coherence puts bound on GRIC and small GRIC improves successful recovery rate of omp algorithm. Experiments demonstrate that omp algorithm performs better using dictionaries constructed by the proposed algorithm than Gaussian random dictionaries and those constructed by Schnass' algorithm.
暂无评论