Joint Scheduling and Power Allocation for 6G Terahertz Mesh Networks

Yu, Mengxin; Tang, Aimin; Wang, Xudong; Han, Chong

doi:10.1109/icnc47757.2020.9049790

Cited by 32 publications

(23 citation statements)

References 14 publications

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“…In [155], for the sake of increasing the weighted sum of completed flows with QoS constraints appeared in THz wireless backhaul network, a heuristic algorithm that contains QoS-aware bandwidth allocation and concurrent scheduling (IHQB) is developed. The existing researches on power allocation at THz band have also been extended to NOMA [156] and THz backhaul networks [157]. However, the resource allocation problem related to IRS assisted THz wireless network is much more complicated than that of the THz wireless network without IRS.…”

Section: Resource Allocationmentioning

confidence: 99%

Towards intelligent reflecting surface empowered 6G terahertz communications: A survey

et al. 2021

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Terahertz (THz) communications have been widely envisioned as a promising enabler to provide adequate bandwidth and achieve ultra-high data rates for sixth generation (6G) wireless networks. In order to mitigate blockage vulnerability caused by serious propagation attenuation and poor diffraction of THz waves, an intelligent reflecting surface (IRS), which manipulates the propagation of incident electromagnetic waves in a programmable manner by adjusting the phase shifts of passive reflecting elements, is proposed to create smart radio environments, improve spectrum efficiency and enhance coverage capability. Firstly, some prospective application scenarios driven by the IRS empowered THz communications are introduced, including wireless mobile communications, secure communications, unmanned aerial vehicle (UAV) scenario, mobile edge computing (MEC) scenario and THz localization scenario. Then, we discuss the enabling technologies employed by the IRS empowered THz system, involving hardware design, channel estimation, capacity optimization, beam control, resource allocation and robustness design. Moreover, the arising challenges and open problems encountered in the future IRS empowered THz communications are also highlighted. Concretely, these emerging problems possibly originate from channel modeling, new material exploration, experimental IRS testbeds and intensive deployment. Ultimately, the combination of THz communications and IRS is capable of accelerating the

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Section: Resource Allocationmentioning

confidence: 99%

Towards intelligent reflecting surface empowered 6G terahertz communications: A survey

et al. 2021

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“…Regarding the resource allocation of 6G networks, Yu et al [10] used the Greedy Shrinking Algorithm (GSA) for the Joint Time-Slot and Sub-band scheduling and Power (JTSP) allocation problem. The proposed problem was expressed in the form of a Mixed-Integer Nonlinear Programming (MINLP) problem.…”

Section: A the Literature Of 6g Optimizationmentioning

confidence: 99%

Enhanced Computational Intelligence Algorithm for Coverage Optimization of 6G Non-Terrestrial Networks in 3D Space

et al. 2021

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The next generation 6G communication network is typically characterized by the full connectivity and coverage of Users Equipment (UEs). This leads to the need for moving beyond the traditional twodimensional (2D) coverage service to the three-dimensional (3D) full-service one. The 6G 3D architecture leverages different types of non-terrestrial or aerial nodes that can act as mobile Base Stations (BSs) such as Unmanned Aerial Vehicles (UAVs), Low Altitude Platforms (LAPs), High-Altitude Platform Stations (HAPSs), or even Low Earth Orbit (LEO) satellites. Moreover, aided technologies have been added to the 6G architecture to dynamically increase its coverage efficiency such as the Reconfigurable Intelligent Surfaces (RIS). In this paper, an enhanced Computational Intelligence (CI) algorithm is introduced for optimizing the coverage of UAV-BSs with respect to their location from RIS in the 3D space of 6G architecture. The regarded problem is formulated as a constrained 3D coverage optimization problem. In order to increase the convergence of the proposed algorithm, it is hybridized with a crossover operator. For the validation of the proposed method, it is tested on different scenarios with large-scale coordinates and compared with many recent and hybrid CI algorithms, as Slime Mould Algorithm (SMA), Lévy Flight Distribution (LFD), hybrid Particle Swarm Optimization and Gravitational Search Algorithm (PSOGSA), the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), and hybrid Grey Wolf Optimizer and Cuckoo Search (GWOCS). The experiment and the statistical analysis show the significant efficiency of the proposed algorithm in achieving complete coverage with a lower number of UAV-BSs and without constraints violation.INDEX TERMS 6G technology, computational intelligence, non-terrestrial base stations, reconfigurable intelligent surfaces, three-dimensional coverage optimization problem.

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“…To evaluate the performance of the proposed scheme in 5G and B5G systems, a scenario in the ultra-dense CRAN is considered, where in a square area of 400m × 400m, both users and BSs are uniformly distributed. To satisfy seamless coverage, the density of BSs is anticipated to come up to 40−50 BSs/km 2 [7,54], therefore the number of BSs and users are set to 8 and 14 to have densities of 50 BSs/km 2 and 87 Users/km 2 . Because of limitation in the maximum number of users that can be supported by each backhaul link, each user is assumed to be served with its nearest 3 BSs [7].…”

Section: Performance Of the Proposed Scheme In The Cran Networkmentioning

confidence: 99%

MSE Minimized Joint Transmission in Coordinated MultiPoint Systems with Sparse Feedback and Constrained Backhaul Requirements

Nezafati

Taki

Svensson

2021

Preprint

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In a joint transmission coordinated multipoint (JT-CoMP) system, a shared spectrum is utilized by all neighbor cells. In the downlink, a group of base stations (BSs) coordinately transmit the users’ data to avoid serious interference at the users in the boundary of the cells, thus substantially improving area fairness. However, this comes at the cost of high feedback and backhaul load; In a frequency division duplex (FDD) system, all users at the cell boundaries have to collect and send feedback of the downlink channel state information (CSI). In centralized JT-CoMP, although with capabilities for perfect coordination, a central coordination node (CCN) have to send the computed precoding weights and corresponding data to all cells which can overwhelm the backhaul resources. In this paper, we design a JT-CoMP scheme, by which the sum of the mean square error (MSE) at the boundary users is minimized, while feedback and backhaul loads are constrained and the load is balanced between BSs. Our design is based on the singular value decomposition (SVD) of CSI matrix and optimization of a binary link selection matrix to provide sparse feedback -constrained backhaul link. For comparison, we adopt the previously presented schemes for feedback and backhaul reduction in the physical layer. Extensive numerical evaluations show that the proposed scheme can reduce the MSE with at least 25%, compared to the adopted and existing schemes.

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Joint Scheduling and Power Allocation for 6G Terahertz Mesh Networks

Cited by 32 publications

References 14 publications

Towards intelligent reflecting surface empowered 6G terahertz communications: A survey

Towards intelligent reflecting surface empowered 6G terahertz communications: A survey

Enhanced Computational Intelligence Algorithm for Coverage Optimization of 6G Non-Terrestrial Networks in 3D Space

MSE Minimized Joint Transmission in Coordinated MultiPoint Systems with Sparse Feedback and Constrained Backhaul Requirements

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