21 research outputs found
Performance analysis of compressive sensing recovery algorithms for image processing using block processing
The modern digital world comprises of transmitting media files like image, audio, and video which leads to usage of large memory storage, high data transmission rate, and a lot of sensory devices. Compressive sensing (CS) is a sampling theory that compresses the signal at the time of acquiring it. Compressive sensing samples the signal efficiently below the Nyquist rate to minimize storage and recoveries back the signal significantly minimizing the data rate and few sensors. The proposed paper proceeds with three phases. The first phase describes various measurement matrices like Gaussian matrix, circulant matrix, and special random matrices which are the basic foundation of compressive sensing technique that finds its application in various fields like wireless sensors networks (WSN), internet of things (IoT), video processing, biomedical applications, and many. Finally, the paper analyses the performance of the various reconstruction algorithms of compressive sensing like basis pursuit (BP), compressive sampling matching pursuit (CoSaMP), iteratively reweighted least square (IRLS), iterative hard thresholding (IHT), block processing-based basis pursuit (BP-BP) based onmean square error (MSE), and peak signal to noise ratio (PSNR) and then concludes with future works
Broadband Channel Estimation for Intelligent Reflecting Surface Aided mmWave Massive MIMO Systems
This paper investigates the broadband channel estimation (CE) for intelligent
reflecting surface (IRS)-aided millimeter-wave (mmWave) massive MIMO systems.
The CE for such systems is a challenging task due to the large dimension of
both the active massive MIMO at the base station (BS) and passive IRS. To
address this problem, this paper proposes a compressive sensing (CS)-based CE
solution for IRS-aided mmWave massive MIMO systems, whereby the angular channel
sparsity of large-scale array at mmWave is exploited for improved CE with
reduced pilot overhead. Specifically, we first propose a downlink pilot
transmission framework. By designing the pilot signals based on the prior
knowledge that the line-of-sight dominated BS-to-IRS channel is known, the
high-dimensional channels for BS-to-user and IRS-to-user can be jointly
estimated based on CS theory. Moreover, to efficiently estimate broadband
channels, a distributed orthogonal matching pursuit algorithm is exploited,
where the common sparsity shared by the channels at different subcarriers is
utilized. Additionally, the redundant dictionary to combat the power leakage is
also designed for the enhanced CE performance. Simulation results demonstrate
the effectiveness of the proposed scheme.Comment: 6 pages, 4 figures. Accepted by IEEE International Conference on
Communications (ICC) 2020, Dublin, Irelan