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Performance analysis of massive multiple input multiple output for high speed railway
2021
International Journal of Power Electronics and Drive Systems (IJPEDS)
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As popularity of high speed train increasing day by day, high data rate wireless communication system for high speed train is extremely required. 5G wireless communication systems must be designed to meet ...
This paper analytically reviews the performance of massive multiple input multiple output (MIMO) system for communication in highly mobility scenarios like high speed Railways. ...
Therefore, it is essential to design new technologies that meet the need of flawless communication technology in high speed train. ...
doi:10.11591/ijece.v11i6.pp5180-5188
fatcat:wohm6klldzdjxij2sqdgwoqryi
Cognitive Learning-Aided Multi-Antenna Communications
[article]
2022
arXiv
pre-print
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These features are especially significant for multi-antenna wireless communications systems, which generate and handle massive data. ...
Multiple antennas may provide multiplexing, diversity, or antenna gains that, respectively, improve the capacity, bit error rate, or the signal-to-interference-plus-noise ratio. ...
Communication Overhead of CL and FL for HB design problem [11] . (PE) [9] . ...
arXiv:2010.03131v3
fatcat:q6bvn4vqzba6fhbvqhq2q26xqa
Microstrip Patch Antenna Parameter Optimization Prediction Model using Machine Learning Techniques
2022
International Journal on Recent and Innovation Trends in Computing and Communication
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The research is continuing going to design and optimization of the antenna for various advance application such as 5G and IOT. ...
Microstrip patch antenna (MPA) plays key role in the wireless communication. ...
The antenna array (multiple similar small structure in same size or dimension) is using for the current wireless communication application. ...
doi:10.17762/ijritcc.v10i9.5691
fatcat:v5ps6zmeujcc7cgzssee4pfecu
Artificial Intelligence Enabled NOMA Towards Next Generation Multiple Access
[article]
2022
arXiv
pre-print
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First, the limitations of current scenario-specific multiple-antenna NOMA schemes are discussed, and the importance of AI for NGMA is highlighted. ...
This article focuses on the application of artificial intelligence (AI) in non-orthogonal multiple-access (NOMA), which aims to achieve automated, adaptive, and high-efficiency multi-user communications ...
communication designs for NGMA. ...
arXiv:2206.04992v3
fatcat:ktdfodlekvfkvgm44axg5mshhm
Support Vector Machine-Based Transmit Antenna Allocation for Multiuser Communication Systems
2019
Entropy
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In this paper, a support vector machine (SVM) technique has been applied to an antenna allocation system with multiple antennas in multiuser downlink communications. ...
Thus, a subset of transmit antennas that can reduce multiuser interference is selected based on such partial channel state information to support multiple users. ...
Concluding Remarks and Future Work In this paper, we introduced a new framework for applying multiclass classification to an antenna allocation system with multiple antennas in multiuser downlink communications ...
doi:10.3390/e21050471
pmid:33267185
fatcat:7geamwlqp5ctzlg7y7pkse5aei
Design of UWB antenna based on Improved Deep Belief Network and Extreme Learning Machine
2021
IEEE Access
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This paper proposes a deep learning model for designing UWB antennas, which determines the model structure of deep belief network (DBN) by particle swarm algorithm (PSO), and then combines DBN and extreme ...
The proposed model can obtain higher feature learning capability and nonlinear function approximation capability, and has been applied to the optimal design of the whole structure of the fractal antenna ...
DBN-ELM model can be used for the optimization design of the antenna. ...
doi:10.1109/access.2021.3111902
fatcat:wu7vmv6yebcinazoz4v3snmg4q
Resource Allocation for Near-Field Communications: Fundamentals, Tools, and Outlooks
[article]
2024
arXiv
pre-print
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2023 pre-print arXiv:2310.17868v1 fatcat:cursrims4nhk3nv3xjnrccmfdq
Finally, several important research directions of near-field communications are pointed out for further investigation. ...
Then, we summarize optimization tools, such as numerical techniques and machine learning methods, for addressing near-field resource allocation, emphasizing their strengths and limitations. ...
Opimization algorithm Problem type Beamforming /Combining /Power control Codebook design Antenna Selection Beam training User grouping /Scheduling Sensing Numerical Optimization based Approach Alternating ...
arXiv:2310.17868v2
fatcat:6rc4uw25evhg3eavq5v4p5o3wi
Optimal binary training sequence design for multiple-antenna systems over dispersive fading channels
2002
IEEE Transactions on Vehicular Technology
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The performance enhancement of multibeam adaptive base-stations antennas for cellular land mobile radio systems," IEEE Trans. Veh. ...
A two-stage hybrid approach for CCI/ISI reduction with space-time processing," IEEE Commun. , "Joint spatial and temporal equalization for channels with ISI and CCI-Theoretical and experimental results ...
ACKNOWLEDGMENT The authors would like to thank the reviewers for their valuable comments and suggestion. ...
doi:10.1109/tvt.2002.800638
fatcat:mnjejx2n7fd7fgm6c3lyotjln4
Power-Allocation Policy and Optimized Design of Multiple-Antenna Systems With Imperfect Channel Estimation
2004
IEEE Transactions on Vehicular Technology
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We here focus on the optimized design and performance of Rayleigh-faded multiple-antenna multiple-inputmultiple-output systems when both transmitter and receiver share imperfect (i.e., error impaired) ...
channel estimates computed via training sequences. ...
In Section VI, we exploit the developed analytical tool for drawing some guidelines about optimized design of multiple-antenna systems with imperfect channel estimates. ...
doi:10.1109/tvt.2003.822025
fatcat:2mhfy5xmfnbtbj4636lb222gly
Power-efficient non-coherent space-time constellations
2003
IEEE International Symposium on Information Theory, 2003. Proceedings.
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This property, together with the facts that the proposed constellations eliminate the need for training sequences and are most suitable for low SNR, makes them a good candidate for uplink communication ...
The interesting result of this work is that the same idea of multilevel constellations can be generalized to design low-power non-coherent constellations for multiple antennas. ...
The transmitter uses some part of the coherence interval to transmit training symbols, and the rest of it for actual data communication. ...
doi:10.1109/isit.2003.1228222
fatcat:z7jmxe3kt5elxbpf4lhb457yh4
Application of Machine Learning in Electromagnetics: Mini-Review
2021
Electronics
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It extensively discusses recent research progress in the development and use of intelligent algorithms for antenna design, synthesis and analysis, electromagnetic inverse scattering, synthetic aperture ...
The potential for ML to solve unpredictable and non-linear complex challenges is attracting researchers in the field of electromagnetics (EM), especially in antenna and antenna-based systems. ...
[64] as a tool for the rapid optimization of EM problems such as antenna design. ...
doi:10.3390/electronics10222752
fatcat:n6jb47p67fh47oyqhxiwldtevi
Cell-Free XL-MIMO Meets Multi-Agent Reinforcement Learning: Architectures, Challenges, and Future Directions
[article]
2023
arXiv
pre-print
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2023 pre-print arXiv:2307.02827v1 fatcat:zd5cjatczzdlhgar2mextdmkya
Cell-free massive multiple-input multiple-output (mMIMO) and extremely large-scale MIMO (XL-MIMO) are regarded as promising innovations for the forthcoming generation of wireless communication systems. ...
Based on the unique near-field characteristics, we propose two categories of the low-complexity design, i.e., antenna selection and power control, to adapt to different cell-free XL-MIMO scenarios and ...
XL-MIMO systems, which is more practical for scalable networks. • Jointly Optimized Design: The jointly optimized design of AS and power control is promising to enhance the robustness of the system, eliminating ...
arXiv:2307.02827v2
fatcat:igj2bucbibbljetp6qm32novqu
Optimal training signals for MIMO OFDM channel estimation in the presence of frequency offset and phase noise
2006
IEEE Transactions on Communications
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We develop robust mean-square error (MSE)-optimal training signal designs for multiple-input multiple-output orthogonal frequency-division multiplexing channel estimation with frequency offset and phase ...
dB for two transmit antennas. ...
Since the same channel-estimation procedure is performed at each receive antenna, we only need to consider one receive antenna in designing optimal training signals. ...
doi:10.1109/tcomm.2006.881358
fatcat:6yinmf5sp5g2xpelzrtbww4i7a
Circularly Polarized Fabry-Perot Cavity Sensing Antenna Design using Generative Model
[article]
2023
arXiv
pre-print
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2022 pre-print arXiv:2209.14550v1 fatcat:npavuascrnhtplfu6qclgnnkyaOther Versions
We consider the problem of designing a circularly polarized Fabry-P'erot cavity (FPC) antenna for S-band sensing applications such as satellite navigation and communication. ...
To this end, we propose a deep generative adversarial network (GAN) for realizing a surrogate model that is trained with input-output pairs of antenna designs and their corresponding patterns. ...
The procedure involves two time-consuming steps before the optimal design is realized: The first step is the synthesis of multiple candidate designs for evaluation. ...
arXiv:2209.14550v2
fatcat:fbimmg3bzzbk3efa7i6i47fwdy
Machine learning with A MIMO Antenna for Wireless Communication Systems
2023
Al-Salam Journal for Engineering and Technology
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Multiple-input multiple-output (MIMO) antenna is a technology that uses multiple antennas at both the transmitter and receiver to improve the performance of wireless communication systems. ...
For example, hexagonal MIMO antennas have been shown to have good performance and can be implemented with a simple design. ...
Multiple-input multiple-output (MIMO) antenna is a technology that uses multiple antennas at both the transmitter and receiver to improve the performance of wireless communication systems. ...
doi:10.55145/ajest.2024.03.01.006
fatcat:eska6sb3hvhpdkae7wocp6lyu4
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