Terahertz-Enabled Wireless System for Beyond-5G Ultra-Fast Networks: A Brief Survey

Huq, Kazi Mohammed Saidul; Busari, Sherif Adeshina; Rodrı́guez, Jonathan; Frascolla, Valerio; Bazzi, Wael M.; Sicker, Douglas

doi:10.1109/mnet.2019.1800430

Cited by 183 publications

(116 citation statements)

References 9 publications

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“…As mentioned earlier, 5G systems use mm-wave frequencies, along with sub-1 and 6 GHz spectrum. It is envisaged that B5G networks will use THz frequencies [47], [48]. Compared to the sub-6GHz band, the advantages of the mm-wave band include more available bandwidth and use of small antennas in devices.…”

Section: B Channel Characteristics Of 5g Wireless Systemsmentioning

confidence: 99%

A Survey of Machine Learning Applications to Handover Management in 5G and Beyond

et al. 2021

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Handover (HO) is one of the key aspects of next-generation (NG) cellular communication networks that need to be properly managed since it poses multiple threats to quality-of-service (QoS) such as the reduction in the average throughput as well as service interruptions. With the introduction of new enablers for fifth-generation (5G) networks, such as millimetre wave (mm-wave) communications, network densification, Internet of things (IoT), etc., HO management is provisioned to be more challenging as the number of base stations (BSs) per unit area, and the number of connections has been dramatically rising. Considering the stringent requirements that have been newly released in the standards of 5G networks, the level of the challenge is multiplied. To this end, intelligent HO management schemes have been proposed and tested in the literature, paving the way for tackling these challenges more efficiently and effectively. In this survey, we aim at revealing the current status of cellular networks and discussing mobility and HO management in 5G alongside the general characteristics of 5G networks. We provide an extensive tutorial on HO management in 5G networks accompanied by a discussion on machine learning (ML) applications to HO management. A novel taxonomy in terms of the source of data to be utilized in training ML algorithms is produced, where two broad categories are considered; namely, visual data and network data. The stateof-the-art on ML-aided HO management in cellular networks under each category is extensively reviewed with the most recent studies, and the challenges, as well as future research directions, are detailed. INDEX TERMS Handover, machine learning, mobility management, fifth generation.

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Section: B Channel Characteristics Of 5g Wireless Systemsmentioning

confidence: 99%

A Survey of Machine Learning Applications to Handover Management in 5G and Beyond

et al. 2021

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“…The article covers the THz generation methods, comparison between THz communication over other wireless communication technologies, channel models, and application of THz band. Huq et al [18] provides a brief survey on THz wireless systems for 5G and B5G. The article is published in the third quarter of 2019.…”

Section: Comparison Of Thz Communication Survey Articlesmentioning

confidence: 99%

Antenna Selection and Designing for THz Applications: Suitability and Performance Evaluation: A Survey

et al. 2020

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Imperceptible latency, uninterrupted communication, and the availability of inexhaustible bandwidth are conceptualized as essential milestones to revolutionize the modes by which societies generated, circulate, receive, and perceive information. The exponential increase in wireless data traffic has raised concerns to investigate suitable bands in the radio spectrum to satisfy the intensifying user's data rate requirements. Overall the wireless infrastructure needs development and exploitation to synchronize with the massive capacity and connectivity demands. The Terahertz (THz) frequency band (0.1-10 THz) is considered as a pivotal solution to fulfill the needs of applications and devices requiring the high speed transmission, and have received noticeable attention from the research community. Technologies in this spectrum are facing rapid development and hold high potentials in applications like ultra-fast short-range wireless communications, remote sensing, biological detection, and basic material research. The antenna is one of the critical components to support the THz systems and require a considerable attention in terms of precision. Compact high-gain antennas are desirable for low latency and high data rate THz wireless communication systems, specifically for applications having space limitation, for example, in the high speed interlink inside the high density wireless communication base station (BS). Nevertheless, there still exist many challenges, while designing the antenna for THz communications requiring innovative solutions. This paper serves an introductory guideline to address the challenges and opportunities, while designing a THz enabled antenna.

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“…For simplicity, we utilize a quasistatic deployment scenario for users, such that the location of each user remains unchanged within the considered time duration 1 . 1 We would like to mention that our proposed optimization framework for joint resource allocation and user association is conducted within the considered time duration, which can be interpreted as a specific time slot or a period of time. However, the results about this framework will be easily extendable to the general case for multiple time slots.…”

Section: Network Modelmentioning

confidence: 99%

Resource Allocation for Energy Efficient User Association in User-Centric Ultra-Dense Networks Integrating NOMA and Beamforming,

Zhang¹,

Zhang²,

Zhao³

et al. 2020

Preprint

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A coupling of wireless access via non-orthogonal multiple access and wireless backhaul via beamforming is a promising way for downlink user-centric ultra-dense networks (UDNs) to improve system performance. However, ultra-dense deployment of radio access points in macrocell and user-centric view of network design in UDNs raise important concerns about resource allocation and user association, among which notably is energy efficiency (EE) balance. To overcome this challenge, we develop a framework to investigate the resource allocation problem for energy efficient user association in such a scenario. The joint optimization framework aiming at the system EE maximization is formulated as a large-scale non-convex mixed-integer nonlinear programming problem, which is NP-hard to solve directly with lower complexity. Alternatively, taking advantages of sum-of-ratios decoupling and successive convex approximation methods, we transform the original problem into a series of convex optimization subproblems. Then we solve each subproblem through Lagrangian dual decomposition, and design an iterative algorithm in a distributed way that realizes the joint optimization of power allocation, sub-channel assignment, and user association simultaneously. Simulation results demonstrate the effectiveness and practicality of our proposed framework, which achieves the rapid convergence speed and ensures a beneficial improvement of system-wide EE.<br>

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Terahertz-Enabled Wireless System for Beyond-5G Ultra-Fast Networks: A Brief Survey

Cited by 183 publications

References 9 publications

A Survey of Machine Learning Applications to Handover Management in 5G and Beyond

A Survey of Machine Learning Applications to Handover Management in 5G and Beyond

Antenna Selection and Designing for THz Applications: Suitability and Performance Evaluation: A Survey

Resource Allocation for Energy Efficient User Association in User-Centric Ultra-Dense Networks Integrating NOMA and Beamforming,

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