scholarly journals Channel State Information (CSI) based Sparse Reconstruction for Biomedical Applications Using hybrid mm-WAVE MIMO System

2021 ◽  
Vol 12 (3) ◽  
pp. 1557-1568
Author(s):  
Sunil Kumar M Et.al
Keyword(s):  
Channel State Information ◽  
Mimo System ◽  
Signal Reconstruction ◽  
Effective Bandwidth ◽  
Sparse Reconstruction ◽  
Spectrum Utilization ◽  
Channel State ◽  
State Information ◽  
Low Snr ◽  
Multiple Input

The significance of Channel State Information (CSI) is very essential in a hybrid mm-WAVE Multiple Input Multiple Input (MIMO) System due to its direct dependency on medium capacity and energy efficiency of a network. Therefore, a Channel State Information (CSI)-based Sparse Reconstruction (CSISR) technique is adopted for effective evaluation of CSI for future 5G cellular network implementation. A hybrid mm-WAVE MIMO communication system is also employed for effective bandwidth spectrum utilization. Furthermore, a joint sparse coding algorithm is introduced to study the channel matrices of hybrid mm-WAVE MIMO system. The proposed CSISR technique ensure proficient signal reconstruction, signal compression and resource reduction by exploiting sparsity of channel matrix. The proposed CSISR technique under low SNR conditions as well for hybrid mm-WAVE MIMO system with optimization of pre-processors and combiners. The performance throughput of proposed CSISR technique is measured against conventional algorithms considering power consumption, Normalized Mean Square Error (NMSE) and spectral efficiency of the mm-Wave MIMO system. The superiority of proposed CSISR technique is concluded based on simulations considering different system configurations and performance matrices.

scholarly journals Transmission Scheme with Limited Channel State Information Feedback for 3D MIMO System

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Xing Li ◽  
Hui Zhao ◽  
Long Zhao ◽  
Wenxiu Zhao ◽  
Senyao Zheng
Keyword(s):  
Channel State Information ◽  
Mimo System ◽  
Reference Signal ◽  
Base Station ◽  
Information Feedback ◽  
Transmission Scheme ◽  
Channel State ◽  
State Information ◽  
3D Mimo ◽  
Channel State Information Feedback

Three-dimensional (3D) multiple-input multiple-output (MIMO) system can exploit the spatial degree of freedom in vertical dimension and can significantly improve system performance compared with 2D transmission scheme. However, in the actual frequency division duplex (FDD) transmission mode, the large overhead of the reference signal and channel state information (CSI) feedback would become a barrier for performance improvement of 3D MIMO system with the significantly increased number of transmit antennas. To deal with these problems, this paper proposes a new transmission scheme of the channel state information-reference signal (CSI-RS), where the CSI-RS is precoded with 3D beamforming vectors and composed of two components: long-term CSI-RS and short-term CSI-RS. For the purpose of conducting efficient transmission in widely used FDD system, we also propose a corresponding limited channel state information feedback scheme. Moreover, multiuser pairing and scheduling criteria based on the design of the CSI-RS are proposed to realize the multiuser transmission. We have investigated multiple options for 3D MIMO codebook scheme and finally adopt the Kronecker product-based codebook (KPC) for precoding operation at the base station (BS). Simulation results demonstrate that our proposed scheme for the 3D MIMO system achieves a better tradeoff between resource overhead and throughput performance.

scholarly journals Optimization of Sparse Learning Problem of Signals on Hybrid mm-Wave MIMO Systems using Sparse Coding based Reconstruction Learning Mechanism

2021 ◽  
Vol 15 ◽  
pp. 713-721
Author(s):  
M., Sunil Kumar ◽  
C. K., Narayanappa ◽  
M. Nagendra Kumar
Keyword(s):  
Millimeter Wave ◽  
Channel State Information ◽  
Sparse Coding ◽  
Mimo Systems ◽  
Mimo System ◽  
Sparse Learning ◽  
Learning Problem ◽  
Channel State ◽  
State Information ◽  
Wave Technology

Researchers and industry experts are looking for the availability of large bandwidth spectrum due to high market demands and expectations for high data rates. And Millimeter Wave technology possess characteristics to fulfill these requirements. However, due to high power consumption and channel estimation requirements, massive MIMO is utilized in coordination with Millimeter Wave technology. Besides, the performance of mm-WAVE MIMO system is measured by the effective estimation of Channel State Information (CSI) which is a critical and challenging process. Therefore, a Sparse Coding based Reconstruction Learning (SCL) mechanism is presented to efficiently estimate Channel State Information (CSI) for Millimeter-WAVE massive Multiple Input Multiple Output (MIMO) system. For efficiency enhancement, joint sparse learning problem is formulated and a denoised joint sparsity learning matrix is obtained using proposed SCL mechanism. Here, optimization of joint sparse learning problem is summarized by reducing inconsistent and overfitting errors. The proposed SCL mechanism performs well under high as well as low SNR conditions. Moreover, joint sparse coding algorithm is utilized for efficient sparse signal restoration. The performance of proposed SCL mechanism is efficiently measured against several state-of-art-algorithms in terms of energy efficiency, NMSE, channel capacity etc.

scholarly journals Subcarrier-Sniffer：CSI-based Subcarrier-Level Spectrum Sensing Algorithm

2019 ◽  
Author(s):  
Zhenjiang Tan ◽  
Zheng Lu ◽  
Hongyu Sun
Keyword(s):  
Spectrum Sensing ◽  
Channel State Information ◽  
Spectrum Sharing ◽  
Smart Homes ◽  
Experimental Results ◽  
Spectrum Utilization ◽  
Channel State ◽  
State Information ◽  
Level Spectrum ◽  
Iot Devices

Abstract: As the massive deployment of the heterogeneous IoT devices in the coexisting environment such as smart homes，Traditional channel-based spectrum sharing algorithms such as CSMA has great limitations to further optimize spectrum utilization. Therefore, exploring more efficient spectrum sensing algorithm becomes hot topic these years. This paper proposes Subcarrier-Sniffer, which utilizes Channel State Information (CSI) to sense the subcarrier-level detailed status of the spectrum. In order to evaluate the performance of Subcarrier-Sniffer, we implemented Subcarrier-Sniffer by USRP B200min, and the experimental results show that when the distance between Subcarrier-Sniffer and the monitored devices is not great than 7 m, the accuracy of subcarrier-level spectrum sensing could achieve 100% in our settings.

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