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Last Updated: 10 May 2022

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Application Research of Music Education to Improve the Mental Health of College Students under the Background of 5G

As the culture shifts with the times, fifth-generation communication technologies, with its high speed, low latency, and broad coverage, will open up new possibilities for the expansion of online collaborative learning. This research recommends a way of integrating soft computing technologies into the advancement of music education to improve college students' mental stability in the conditions of 5G in order to keep music education up to date with the latest trends in this modern age. Both conventional control methods and more sophisticated mathematical model control strategies are ineffective at accurately assessing student's mental stability. This report uses an intelligent fuzzy network as the control center and provides an assessment method based on an advanced fuzzy neural network to determine the effects of music instruction in improving mental stability of the students.

Source link: https://doi.org/10.1155/2022/2395275


Application Layer-Forward Error Correction Raptor Q Codes in 5G Mobile Networks for Factory of the Future

Industrial automation's future communication requirements would differ notably from that of recent technologies. The most significant challenge of the NOMA communication system in the factory of the future is the demand for ultra-high availability as high as 99. 999999 percent and as low as 1 ms end-to-end latency. The Raptor Q codes were not only able to produce good results, e. g. , packet reception rate PRR=0. 9 of 10 m or 3. 4 percent in various scenarios, but they were also able to achieve PRR=0. 9 in the mobility scenario at ten kilometers per hour, depending on different scenarios. Thus, the Raptor Q codes can be used as a good candidate for obtaining results within a narrow range of conditions set by URC communications for the factory of the future replacing RLNC.

Source link: https://doi.org/10.1155/2022/2257338


5G radio access networks: A survey

The fifth generation technology enhances the user experience and opens new opportunities for a variety of fields, including transportation, device-to-device communications, agriculture, and manufacturing. Hence, the 5G network must be updated, and the radio access network for the 5G system must be modified. This research explores the various RAN architectures, including cloud-RAN, heterogeneous cloud-RAN, and fog-RAN.

Source link: https://doi.org/10.1016/j.array.2022.100170


Wide Band Raised Printed Monopole for Automotive 5G Wireless Communications

This paper describes a compact Multiple input Multiple output antenna system for vehicular use in the sub-6GHz 5G networks that operate in the middle and high frequency bands from 1. 71GHz to 5GHz. With Partial Ground Plane structure, two symmetrical raised printed monopoles on Flame Retardant 4 dielectric material can be combined in a single design to increase bandwidth sensitivity and achieve greater isolation across the operating frequency range.

Source link: https://doi.org/10.1109/OJAP.2022.3170799


IEEE Access Special Section Editorial: Fog Radio Access Networks (F-RANS) for 5G: Recent Advances and Future Trends

For the fifth-generation mobile communication technology, a complete set of performance criteria has been developed to meet the ever-improving demands for high-speed data applications and large internet access requirements of various Internet-of-thing devices. A model of fog radio access networks has emerged as a promising evolution path for 5G network architecture, owing to the need for network architecture expansion. 5G is facing new challenges as it integrates with artificial intelligence and other new emerging technologies, and F-RANs' study is also in a new era.

Source link: https://doi.org/10.1109/ACCESS.2020.3036141


Polarization Combining and Equalization in 5G Mobile-to-Mobile Systems

This paper explores maximum likelihood detection for post-FFT processing of dual-polarized antenna outputs with a cyclic-prefix OFDM waveform for a frequency selective multipath fading in a 5G mobile-to-mobile environment. A combiner applied to the channel matched filter outputs is the recommended maximum likelihood detector, followed by a decision rule based on correlation and signal strength. The difference between MLD and maximum ratio combining, MLC+FDE; and selection diversity with frequency-domain equalization is 2 dB, according to the simulation results. An error floor is observed when channel estimations are used in place of accurate channel knowledge.

Source link: https://doi.org/10.1109/ACCESS.2022.3170841


Dielectric Metasurface Inspired Directional Multi-Port Luneburg Lens as a Medium for 5G Wireless Power Transfer—A Design Methodology

A novel dielectric multi-beam directional Luneburg lens is designed as a wireless power transfer medium at 5G mm-wave wavelengths in this paper. The new structure can be a potential candidate to harvest ambient energy from a wide coverage area of around 160° and yield a power conversion efficiency of about 76% for an input power of 14. 9 dBm at 24 GHz.

Source link: https://doi.org/10.1109/JPHOT.2022.3169711


Efficient Channel Prediction Technique Using AMC and Deep Learning Algorithm for 5G (NR) mMTC Devices

Through the significant decrease in bit error rate, the quality of information bits is guaranteed by efficient use of adaptive modulation and coding. Channel estimation under the 5G New Radio is not feasible for large machine-type communication systems under the 5G New Radio scheme. Image restoration and image super-resolution are two key aspects of a deep learning-based pipelining scheme. The results of BER by parametric estimation alongside the DL approach is ~66% more cost-effective than the conventional MMSE method for BPSK mapping.

Source link: https://doi.org/10.1109/ACCESS.2022.3167442

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions