Spectrum Allocation With Adaptive Sub-Band Bandwidth for Terahertz Communication Systems

Shafie, Akram; Yang, Nan; Alvi, Sheeraz A.; Han, Chong; Durrani, Salman; Jornet, Josep Miquel

doi:10.1109/tcomm.2021.3139887

Cited by 24 publications

(17 citation statements)

References 42 publications

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“…THz Spectrum 1) Spectrum of Interest: We define the regions which generally exhibit an decreasing and increasing molecular absorption coefficient behaviour within an ultra-wideband THz transmission window as the negative absorption coefficient slope region (NACSR) and the positive absorption coefficient slope region (PACSR), respectively, as depicted in Fig. 1 [10]. It is noted that at the THz band, the available bandwidths in each NACSR and PACSR are in the order of tens of GHz [10, Table 1].…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Motivated by this, the multi-band-based spectrum allocation with ASB was first proposed in our previous studies [10], [11]. We note that the design in [10] is only applicable when the molecular absorption coefficient within the to-be-allocated spectrum is simple such that it can be modeled as a piecewise exponential function of frequency with minimal approximation errors. In many spectrum regions within the THz band, the molecular absorption coefficient varies in a highly non-linear manner; thus, it cannot be modeled as a piecewise exponential function of frequencies in such spectrum regions.…”

Section: Introductionmentioning

confidence: 99%

“…In many spectrum regions within the THz band, the molecular absorption coefficient varies in a highly non-linear manner; thus, it cannot be modeled as a piecewise exponential function of frequencies in such spectrum regions. It follows that the designs in [10], [11] cannot be adopted in such spectrum regions. This motivates the current work.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An Unsupervised Learning Approach for Spectrum Allocation in Terahertz Communication Systems

Shafie¹,

Li²,

Yang³

et al. 2022

Preprint

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We propose a new spectrum allocation strategy, aided by unsupervised learning, for multiuser terahertz communication systems. In this strategy, adaptive sub-band bandwidth is considered such that the spectrum of interest can be divided into sub-bands with unequal bandwidths. This strategy reduces the variation in molecular absorption loss among the users, leading to the improved data rate performance.We first formulate an optimization problem to determine the optimal sub-band bandwidth and transmit power, and then propose the unsupervised learning-based approach to obtaining the near-optimal solution to this problem. In the proposed approach, we first train a deep neural network (DNN) while utilizing a loss function that is inspired by the Lagrangian of the formulated problem. Then using the trained DNN, we approximate the near-optimal solutions. Numerical results demonstrate that comparing to existing approaches, our proposed unsupervised learning-based approach achieves a higher data rate, especially when the molecular absorption coefficient within the spectrum of interest varies in a highly non-linear manner.

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Section: System Model and Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Unsupervised Learning Approach for Spectrum Allocation in Terahertz Communication Systems

Shafie¹,

Li²,

Yang³

et al. 2022

Preprint

Self Cite

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“…Specifically, benefited from the distance-adaptive modulations as discussed in V-A, the LUCW principle intelligently allocates the center spectrum of the spectral windows to the longdistance users first, and then the side spectrum to the shortdistance users [160]. We further develop this multi-band-based spectrum allocation idea with adaptive sub-band bandwidth by allowing the spectrum of interest to be divided into sub-bands with unequal bandwidths [236].…”

Section: A Medium Access Controlmentioning

confidence: 99%

TeraHertz Band Communication: An Old Problem Revisited and Research Directions for the Next Decade

Akyildiz¹,

Han²,

Hu³

et al. 2021

Preprint

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TeraHertz (THz) band communications are envisioned as a key technology for 6G and Beyond. As a fundamental wireless infrastructure, THz communication can boost abundant promising applications. In 2014, our team published two comprehensive roadmaps for the development and progress of THz communication networks [1], [2], which helped the research community to start research on this subject afterwards. In particular, this topic became very important and appealing to the research community due to 6G wireless systems design and development in recent years. Many papers are getting published covering different aspects of wireless systems using the THz band. With this paper, our aim is looking back to the last decade and revisiting the old problems and pointing out what has been achieved in the research community so far. Furthermore, in this paper still to be investigated new research challenges for the THz band communication systems are presented by covering diverse subtopics such as from perspectives of devices, channel behavior, communication and networking problems, physical testbeds and demonstration systems. The key aspects presented in this paper will enable THz communications as a pillar of 6G and Beyond wireless systems in the next decade.

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Advanced Wireless Technologies for Industrial Automation

Lyu

Guan²,

Cheng³

et al. 2023

Wireless Networks

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Spectrum Allocation With Adaptive Sub-Band Bandwidth for Terahertz Communication Systems

Cited by 24 publications

References 42 publications

An Unsupervised Learning Approach for Spectrum Allocation in Terahertz Communication Systems

An Unsupervised Learning Approach for Spectrum Allocation in Terahertz Communication Systems

TeraHertz Band Communication: An Old Problem Revisited and Research Directions for the Next Decade

Advanced Wireless Technologies for Industrial Automation

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