An Accurate Approximation of Resource Request Distributions in Millimeter Wave 3GPP New Radio Systems

Kovalchukov, Roman; Moltchanov, Dmitri; Gaidamaka, Yuliya; Bobrikova, Ekaterina V.

doi:10.1007/978-3-030-30859-9_50

Cited by 21 publications

(8 citation statements)

References 24 publications

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“…The height of UEs, NR BS, and blockers are assumed to be constant and given by h U , h A , and h B , We assume the orthogonal frequency division multiple access (OFDMA) scheme as specified in NR [36]. Depending on UE locations, the number of PRBs required to serve multicast UEs might differ and can be computed using NR MCS according to the procedure outlined in, e.g., [37].…”

Section: A Deployment Traffic and Resource Modelsmentioning

confidence: 99%

The Use of Machine Learning Techniques for Optimal Multicasting in 5G NR Systems

Chukhno

Moltchanov

et al. 2023

IEEE Trans. on Broadcast.

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Multicasting is a key feature of cellular systems, which provides an efficient way to simultaneously disseminate a large amount of traffic to multiple subscribers. However, the efficient use of multicast services in fifth-generation (5G) New Radio (NR) is complicated by several factors, including inherent base station (BS) antenna directivity as well as the exploitation of antenna arrays capable of creating multiple beams concurrently. In this work, we first demonstrate that the problem of efficient multicasting in 5G NR systems can be formalized as a special case of multi-period variable cost and size bin packing problem (BPP). However, the problem is known to be NP-hard, and the solution time is practically unacceptable for large multicast group sizes. To this aim, we further develop and test several machine learning alternatives to address this issue. The numerical analysis shows that there is a trade-off between accuracy and computational complexity for multicast grouping when using decision tree-based algorithms. A higher number of splits offers better performance at the cost of an increased computational time. We also show that the nature of the cell coverage brings three possible solutions to the multicast grouping problem: (i) small-range radii are characterized by a single multicast subgroup with wide beamwidth, (ii) middlerange deployments have to be solved by employing the proposed algorithms, and (iii) BS at long-range radii sweeps narrow unicast beams to serve multicast users.

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Section: A Deployment Traffic and Resource Modelsmentioning

confidence: 99%

The Use of Machine Learning Techniques for Optimal Multicasting in 5G NR Systems

Chukhno

Moltchanov

et al. 2023

IEEE Trans. on Broadcast.

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“…While the rates and MCSs are standardized, the corresponding target SINRs are usually vendor-and equipment-specific. We consider the target SINRs in [40], which are shown in Table III (in linear scale) and approximately exhibit increments of twice the previous rate starting from R SBS 1 = 0.2344 bps/Hz. In order to assign R SBS j to the l-th SBS cluster, it is required…”

Section: Rnp-socp-1 Formulationmentioning

confidence: 99%

RadiOrchestra: Proactive Management of Millimeter-Wave Self-Backhauled Small Cells via Joint Optimization of Beamforming, User Association, Rate Selection, and Admission Control

Abanto-Leon

Asadi

García‐Saavedra³

et al. 2023

IEEE Trans. Wireless Commun.

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Millimeter-wave self-backhauled small cells are a key component of next-generation wireless networks. Their dense deployment will increase data rates, reduce latency, and enable efficient data transport between the access and backhaul networks, providing greater flexibility not previously possible with optical fiber. Despite their high potential, operating dense selfbackhauled networks optimally is an open challenge, particularly for radio resource management (RRM). This paper presents, RadiOrchestra, a holistic RRM framework that models and optimizes beamforming, rate selection as well as user association and admission control for self-backhauled networks. The framework is designed to account for practical challenges such as hardware limitations of base stations (e.g., computational capacity, discrete rates), the need for adaptability of backhaul links, and the presence of interference. Our framework is formulated as a nonconvex mixed-integer nonlinear program, which is challenging to solve. To approach this problem, we propose three algorithms that provide a trade-off between complexity and optimality. Furthermore, we derive upper and lower bounds to characterize the performance limits of the system. We evaluate the developed strategies in various scenarios, showing the feasibility of deploying practical self-backhauling in future networks.

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“…where p B (x ) is available from (17). The authors in [211] demonstrated that the resulting SINR CDF can be well approximated by Normal distribution.…”

Section: A Resource Request Characterizationmentioning

confidence: 99%

A Tutorial on Mathematical Modeling of 5G/6G Millimeter Wave and Terahertz Cellular Systems

Moltchanov

Sopin

Begishev

et al. 2022

IEEE Commun. Surv. Tutorials

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Millimeter wave (mmWave) and terahertz (THz) radio access technologies (RAT) are expected to become a critical part of the future cellular ecosystem providing an abundant amount of bandwidth in areas with high traffic demands. However, extremely directional antenna radiation patterns that need to be utilized at both transmit and receive sides of a link to overcome severe path losses, dynamic blockage of propagation paths by large static and small dynamic objects, macroand micromobility of user equipment (UE) makes provisioning of reliable service over THz/mmWave RATs an extremely complex task. This challenge is further complicated by the type of applications envisioned for these systems inherently requiring guaranteed bitrates at the air interface. This tutorial aims to introduce a versatile mathematical methodology for assessing performance reliability improvement algorithms for mmWave and THz systems. Our methodology accounts for both radio interface specifics as well as service process of sessions at mmWave/THz base stations (BS) and is capable of evaluating the performance of systems with multiconnectivity operation, resource reservation mechanisms, priorities between multiple traffic types having different service requirements. The framework is logically separated into two parts: (i) parameterization part that abstracts the specifics of deployment and radio mechanisms, and (ii) queuing part, accounting for details of the service process at mmWave/THz BSs. The modular decoupled structure of the framework allows for further extensions to advanced service mechanisms in prospective mmWave/THz cellular deployments while keeping the complexity manageable and thus making it attractive for system analysts.

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An Accurate Approximation of Resource Request Distributions in Millimeter Wave 3GPP New Radio Systems

Cited by 21 publications

References 24 publications

The Use of Machine Learning Techniques for Optimal Multicasting in 5G NR Systems

The Use of Machine Learning Techniques for Optimal Multicasting in 5G NR Systems

RadiOrchestra: Proactive Management of Millimeter-Wave Self-Backhauled Small Cells via Joint Optimization of Beamforming, User Association, Rate Selection, and Admission Control

A Tutorial on Mathematical Modeling of 5G/6G Millimeter Wave and Terahertz Cellular Systems

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