Sparse K-best detector for generalised space shift keying in large-scale multiple-input–multiple-output systems

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.

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

10.5194/egusphere-egu21-12948 ◽

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.&#160; Some important turbulence characteristics, such as a turbulent dissipation rate, buoyancy Reynolds number, and Froude number, support our conclusion.</p>

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

Electronics ◽

10.3390/electronics9111844 ◽

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.

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3D Multiple Input Multiple Output Large-Scale Antenna Systems

Journal of Southwest Jiaotong University ◽

10.35741/issn.0258-2724.55.6.1 ◽

2020 ◽

Vol 55 (6) ◽

Author(s):

Saif Saad Hameed ◽

Fouad H. Awad ◽

Adnan Yousif Dawod ◽

Ayoob Abdulmunem Abdulhameed

Keyword(s):

Large Scale ◽

Random Noise ◽

Multiple Input Multiple Output ◽

Least Square ◽

Noise Sources ◽

Estimation Algorithms ◽

High Data ◽

Data Rates ◽

Multiple Input ◽

Input Multiple Output

The channel could be evaluated by utilizing several estimation algorithms. The various patterns of pilot arrangements for the channel appreciation are a huge problem in channel appreciation techniques since all the processes depends on it; this paper discusses improvements in channel selection. The Least Square and Least Square Mean methods are common, simple ways to begin to estimate a channel; however, they are less efficient than more complex approaches. Due to the boost in demand with high data rates in communications, developers continue to invent new methods and mechanisms to adjust the capacity and the accuracy of the communication network. One of the primary troubles in wireless communication is the communication channel, which is affected by nonlinear and random noise sources, which decrease the quality of the service on the network; in this case, the channel must be equalized to increase performance with minimal error. In this paper, a Massive Multiple Input Multiple Output was designed and simulated in order to estimate the channel and the performance of the network through using Least Square and Least Square Mean.

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Joint Optimization of Precoder and Decoder in Multiuser MIMO Systems

REV Journal on Electronics and Communications ◽

10.21553/rev-jec.34 ◽

2012 ◽

Vol 2 (1-2) ◽

Cited By ~ 4

Author(s):

M. Raja ◽

Ha H. Nguyen ◽

P. Muthuchidambaranathan

Keyword(s):

Mean Squared Error ◽

Mimo Systems ◽

Multiple Input Multiple Output ◽

Joint Optimization ◽

Phase Shift Keying ◽

Multiuser Mimo ◽

Multiple Input ◽

Shift Keying ◽

Input Multiple Output ◽

Binary Phase Shift Keying

This paper considers the joint optimization of precoder and decoder for both uplink and downlink transmissions in multiuser multiple-input, multiple-output (MU-MIMO) systems. Focusing on the scenario when an improper constellation such as binary phase shift-keying (BPSK) or M-ary amplitude shift-keying (M-ASK) is employed, novel joint linear precoders and decoders are proposed to minimize the total mean squared error (TMSE) of the symbol estimation. The superiority of the proposed transceivers over the previously-proposed designs is thoroughly verified by simulation results.

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