Structured analysis/synthesis compressive sensing‐based channel estimation in wideband mmWave large‐scale multiple input multiple output systems

2020 ◽  
Vol 31 (7) ◽  
Author(s):  
Ameni Mejri ◽  
Moufida Hajjaj ◽  
Salem Hasnaoui
Keyword(s):  
Channel Estimation ◽  
Compressive Sensing ◽  
Large Scale ◽  
Multiple Input Multiple Output ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Structured Analysis

scholarly journals Unification of Broadband and Broadcast Mobile Services using Cognitive Radio

2021 ◽  
pp. 71-75
Author(s):  
V. Annapoorani ◽  
S. Sureshkumar ◽  
Srisaravanapathimurugesan ◽  
M. Manoj ◽  
K. Prabhu
Keyword(s):  
Cognitive Radio ◽  
Dynamic Spectrum Access ◽  
Large Scale ◽  
High Reliability ◽  
Multiple Input Multiple Output ◽  
Future Generation ◽  
Research Areas ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Overall Performance

The networks in future generation uses the confluence of multi-media, broadband, and broadcast services, Cognitive Radio (CR) networks are located as a preferred paradigm to bring up with spectrum functionality traumatic conditions. CRS addresses the ones troubles via dynamic spectrum access. However, the precept traumatic conditions faced through manner of manner of the CR pertain to accomplishing spectrum overall performance. At the end, spectrum overall performance improvement models based on spectrum sensing and sharing models have attracted quite a few research hobby in modern-day years, which incorporates CR mastering models, network densification architectures, and Massive Multiple Input Multiple Output (MIMO), and beamforming techniques. This paper deals with a survey of modern CR spectrum overall improvement performance models and techniques which helps ultra-high reliability with low latency communications which might be resilient to surges in web page site visitors and competition for spectrum. These models and techniques, mainly speaks about permit a big form of functionality beginning from extra superb mobiliary broadband to large-scale Internet of Things (IoT) type communications. It also provides a research correlation for many of the regular periods of a spectrum block, as well as the realistic statistics rate, the models which are used in this paper are applicable in an ultra-high frequency band. This study provides a super compare of CRs and direction for future investigations into newly identified 5G research areas, such as in business enterprise and academia.

High spatiotemporal radar observation of the polar summer mesosphere using MAARSY in a MIMO configuration

2021 ◽  
Author(s):  
Ralph Latteck ◽  
Jorge Chau ◽  
Miguel Urco ◽  
Juha Vierinen ◽  
Victor Avsarkisov
Keyword(s):  
Large Scale ◽  
Middle Atmosphere ◽  
Multiple Input Multiple Output ◽  
Horizontal Resolution ◽  
Neutral Wind ◽  
Stratified Turbulence ◽  
Turbulent Dissipation ◽  
Radar Echoes ◽  
Multiple Input ◽  
Input Multiple Output

<p>Atmospheric structures due to gravity waves, turbulence, Kelvin Helmholtz instabilities, etc. in the mesosphere are being studied with a varying of ground-based and satellite-based instruments. At scales less than 100 km, they are mainly studied with airglow imagers, lidars, and radars. Typical radar observations have not been able to resolve spatial and temporal ambiguities due to the strength of radar echoes, the size of the system, and/or the nature of the atmospheric irregularities. In this work we observed spatially and temporally resolved structures of PMSE with unprecedented horizontal resolution, using the improved radar imaging accuracy of the Middle Atmosphere Alomar Radar System (MAARSY) with the aid of a multiple-input multiple output (MIMO) technique. The studies are performed in both the brightness of the mesospheric echoes and their Doppler velocities. The resolutions achieved are less than 1 km in the horizontal direction, less than 300m in altitude, and less than 1 minute in time, in an area of ~15km x 15km around 85km of altitude. We present a couple of wavelike monochromatic events, one drifting with the background neutral wind, and one propagating against the neutral wind. Horizontal wavelengths, periods, and vertical and temporal coverage of the events are described and discussed. A theory of stratified turbulence is employed in the present study. In particular, it is shown that the structure that propagates with the background wind is a large-scale turbulent KHI event.  Some important turbulence characteristics, such as a turbulent dissipation rate, buoyancy Reynolds number, and Froude number, support our conclusion.</p>

scholarly journals Deep Scanning—Beam Selection Based on Deep Reinforcement Learning in Massive MIMO Wireless Communication System

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1844
Author(s):  
Minhoe Kim ◽  
Woongsup Lee ◽  
Dong-Ho Cho
Keyword(s):  
Communication Systems ◽  
Large Scale ◽  
Multiple Input Multiple Output ◽  
Base Station ◽  
Q Learning ◽  
Multiple Input ◽  
Beam Selection ◽  
Input Multiple Output ◽  
Optimal Beam ◽  
Resource Allocation Scheme

In this paper, we investigate a deep learning based resource allocation scheme for massive multiple-input-multiple-output (MIMO) communication systems, where a base station (BS) with a large scale antenna array communicates with a user equipment (UE) using beamforming. In particular, we propose Deep Scanning, in which a near-optimal beamforming vector can be found based on deep Q-learning. Through simulations, we confirm that the optimal beam vector can be found with a high probability. We also show that the complexity required to find the optimum beam vector can be reduced significantly in comparison with conventional beam search schemes.

scholarly journals Channel Estimation Performance Analysis of FBMC/OQAM Systems with Bayesian Approach for 5G-Enabled IoT Applications

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Han Wang ◽  
Wencai Du ◽  
Xianpeng Wang ◽  
Guicai Yu ◽  
Lingwei Xu
Keyword(s):  
Channel Estimation ◽  
Compressive Sensing ◽  
Communication Systems ◽  
Channel Model ◽  
Matching Pursuit ◽  
Multiple Input Multiple Output ◽  
Filter Bank Multicarrier ◽  
Channel Parameter ◽  
Input Multiple Output ◽  
Estimation Scheme

A filter bank multicarrier (FBMC) with offset quadrature amplitude modulation (OQAM) (FBMC/OQAM) is considered to be one of the physical layer technologies in future communication systems, and it is also a wireless transmission technology that supports the applications of Internet of Things (IoT). However, efficient channel parameter estimation is one of the difficulties in realization of highly available FBMC systems. In this paper, the Bayesian compressive sensing (BCS) channel estimation approach for FBMC/OQAM systems is investigated and the performance in a multiple-input multiple-output (MIMO) scenario is also analyzed. An iterative fast Bayesian matching pursuit algorithm is proposed for high channel estimation. Bayesian channel estimation is first presented by exploring the prior statistical information of a sparse channel model. It is indicated that the BCS channel estimation scheme can effectively estimate the channel impulse response. Then, a modified FBMP algorithm is proposed by optimizing the iterative termination conditions. The simulation results indicate that the proposed method provides better mean square error (MSE) and bit error rate (BER) performance than conventional compressive sensing methods.

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