Role of Millimeter Wave for Future 5G Mobile Networks: Its Potential, Prospects and Challenges

Mohanty, Sujata; Agarwal, Arun; Agarwal, Kabita; Mali, Sabita; Misra, Gourav

doi:10.1109/odicon50556.2021.9429017

Cited by 11 publications

(8 citation statements)

References 10 publications

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“…Millimeter wave (mmWave) communication technology has attracted much attention in 5G cellular systems, and it provides a wide range of spectrum with multiple access multiplexing technology that can greatly improve channel capacity, which is undoubtedly attractive in tight spectrum resources. Besides, the reliability of mmWave communications system is extremely high, and it can provide a stable transmission channel [1][2][3][4][5][6][7]. To compensate for the severe path losses in millimeter wave signal propagation, millimeter wave communication systems are usually equipped with massive multiple-input multiple-output (MIMO) antenna arrays [8,9].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Based Low-Complexity Channel State Information Estimation

Meng

Wei

Zhang

et al. 2022

Preprint

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In 5G communications, the acquisition of accurate channel state information (CSI) is of great importance to the hybrid beamforming of millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) system. In classical mmWave MIMO channel estimation methods, the exploitation of inherent sparse or low-rank structures has demonstrated to improve the performance. However, most high-accurate CSI estimators incur a high computational complexity and require the prior channel information, which hence present the major challenges in the practical deployment. In this work, we leverage machine learning to design the low-complexity and high-performance channel estimator. To be specific, we first formulate the CSI estimation, in the case of sparse structure, as one classical least absolute shrinkage and selection operator (LASSO) problem. In order to reduce the time complexity of existing compressed sensing (CS) methods, we then approximate the original optimization problem to another one, by imposing the other low-rank constraint that was barely considered by CS. We thus solve this new approximated problem and attain the near-optimal solution of the original problem. One new method excludes any prior channel information, and greatly improves the estimation performance, which only incurs a low time complexity. Simulation results demonstrate the superiority of our proposed method both in the estimation accuracy and time complexity.

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Section: Introductionmentioning

confidence: 99%

Machine Learning Based Low-Complexity Channel State Information Estimation

Meng

Wei

Zhang

et al. 2022

Preprint

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“…This will ensure that the current infrastructures can meet the expected increase in connected devices and data transmission. The implementation of 5G wireless mobile internet technology is one of the most modern solutions available to address this problem (Azzahra et al, 2017;Singh et al, 2018;Herschfelt et al, 2021;Mohanty et al, 2021;objetconnecte. net., 2022objetconnecte.…”

Section: Introductionmentioning

confidence: 99%

“…5G technology has up to 10 more data rates than the older 4G technology. This throughput improvement enables all IoT devices the ability to communicate and share data more efficiently than ever before (Azzahra et al, 2017;Singh et al, 2018;Herschfelt et al, 2021;Mohanty et al, 2021;objetconnecte. net., 2022objetconnecte.…”

Section: Introductionmentioning

confidence: 99%

“…Wireless Local Area Networks (or WLANs) are founded on a standard that constitutes the integrated solutions suitable for networking in such a way that offers mobility, flexibility, installation ease, and low cost for the implementation of the 5G wireless IoT technology (Azzahra et al, 2017;Singh et al, 2018;Herschfelt et al, 2021;Mohanty et al, 2021;objetconnecte. net., 2022objetconnecte.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the growing need for higher frequencies has caused many scientists to prioritize mm-Waves at 60 GHz. The 60 GHz band is a good prospect for future high-speed WLAN-IoT communication systems, it offers a cost and time advantage for the device manufacturers to guarantee the quality of the consumers (Azzahra et al, 2017;Singh et al, 2018;Herschfelt et al, 2021;Mohanty et al, 2021;objetconnecte. net., 2022objetconnecte.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Wireless Lan for Iot 5g Applications in a Congested Indoor Environment: Improved Primary Signal Sensing at Millimeter-Wave v-Band [40 – 75 Ghz]

Adardour

Kameche

2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Presently, the IEEE 802.11ad wireless network standard supports the communication capability within the V band of millimeter-Waves (or mm-Waves) [40 – 75 GHz] for Internet of Things (or IoT) technology. This paper presents an algorithm to improve the Primary Signal 5G (or PS5G) sensing at 60 GHz in WLANIoT-5G (or Wireless Local Area Network for IoT-5G). To achieve that, we aim at investigating and assessing the sensing performances of the PS5G from the Access Point of WLANIoT-5G (or APWLANIoT-5G) by the Secondary User IoT-5G (or SUIoT-5G) in a congested environment using a simple energy detector (or SED) algorithm and a simple recursive estimator (or SRE). In addition, the SUIoT-5G is regarded as a mobile user in WLANIoT-5G by employing the Gauss-Markov Random Mobility Model (or GMRMM). Through various scenario simulations, the performances and the robustness of the proposed algorithm are proved.

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A survey on increasing the capacity of 5G Fronthaul systems using RoF

B.S¹,

Azeem²

2022

Optical Fiber Technology

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Role of Millimeter Wave for Future 5G Mobile Networks: Its Potential, Prospects and Challenges

Cited by 11 publications

References 10 publications

Machine Learning Based Low-Complexity Channel State Information Estimation

Machine Learning Based Low-Complexity Channel State Information Estimation

Wireless Lan for Iot 5g Applications in a Congested Indoor Environment: Improved Primary Signal Sensing at Millimeter-Wave v-Band [40 – 75 Ghz]

A survey on increasing the capacity of 5G Fronthaul systems using RoF

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