Characterizing Resource Allocation Trade-Offs in 5G NR Serving Multicast and Unicast Traffic

Samuylov, Andrey; Moltchanov, Dmitri; Kovalchukov, Roman; Pirmagomedov, Rustam; Gaidamaka, Yuliya; Andreev, Sergey; Koucheryavy, Yevgeni; Samouylov, Konstantin

doi:10.1109/twc.2020.2973375

Cited by 38 publications

(20 citation statements)

References 21 publications

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“…First, we would like to study the accuracy of the proposed heuristic algorithms without "external disturbances" that are always present in the system serving the mixture of traffic types and multiple competing multicast sessions. Secondly, to model the system with both unicast and multicast traffic, one needs to take additional assumptions as multicast traffic is known to occupy resources of the system more aggressively and eventually, under high multicast load conditions may fully occupy the system resources as we have shown in [8]. Nevertheless, the approach proposed in this paper can be extended to capture multiple multicast sessions and the presence of unicast sessions as well.…”

Section: Optimization Criterionmentioning

confidence: 99%

“…The algorithm's goal is to determine the groups, which will be served simultaneously, and the corresponding transmit power to minimize the ratio of occupied to Algorithm 2: Modified Incremental Multicast Grouping [17], L ≥ 1 available resources. Thus, the algorithm works until all groups are deleted from S M (lines [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. We also use D (m) to denote a set of groups to be served at a time slot m. The algorithm selects the worst (in the sense of the needed power) group from S M and adds this group to set D (m) (lines 7-9).…”

Section: Beam Assignment and Power Allocationmentioning

confidence: 99%

“…In the microwave spectrum with typically omnidirectional transmissions, multicast is a natural scheme to implement. However, in highly directional systems, i.e., the millimeter wave (mmWave) band communications considered for NR, the use of extremely directional radiation patterns at the BS's antennas poses some challenges to the multicast operation design, which still remain unsolved or even unaddressed [8], [9].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimal Multicasting in Millimeter Wave 5G NR With Multi-Beam Directional Antennas

Chukhno

Moltchanov

et al. 2023

IEEE Trans. on Mobile Comput.

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The support of multicast communications in the fifth-generation (5G) New Radio (NR) system poses unique challenges to system designers. Particularly, the highly directional antennas do not allow to serve all the user equipment devices (UEs) that belong to the same multicast session in a single transmission. However, the capability of modern antenna arrays to utilize multiple beams simultaneously, with potentially varying half-power beamwidth, adds a new degree of freedom to the UE scheduling. This work addresses the challenge of optimal multicasting in 5G millimeter wave (mmWave) systems by presenting a globally optimal solution for multi-beam antenna operation. The optimization problem is formulated as a special case of multiperiod variable cost and size bin packing problem that allows to not impose any constraints on the number of the beams and their configurations. We also propose heuristic solutions having polynomial time complexity. Our results show that for small cell radii of up to 100 meters, a single beam is always utilized. For higher cell coverage and practical ranges of the number of users (5-50), the optimal number of beams is upper bounded by 3.

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Section: Optimization Criterionmentioning

confidence: 99%

Section: Beam Assignment and Power Allocationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal Multicasting in Millimeter Wave 5G NR With Multi-Beam Directional Antennas

Chukhno

Moltchanov

et al. 2023

IEEE Trans. on Mobile Comput.

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“…Therefore, an explicit prioritization mechanism is needed at the NR BS to achieve a trade-off between unicast and multicast session drop probabilities. Later, in [32], a framework that applies stochastic geometry and queuing theory to estimate the NR AP parameters in the system where multicast and unicast traffic are simultaneously supported is proposed. In [33], 5G NR Mixed Mode (MM) to enable the use of multicast in the 5G NR Release 17 is proposed.…”

Section: Related Workmentioning

confidence: 99%

“…Non-orthogonal multiple access (NOMA) system is considered in [34], where a cooperation strategy for both unicast and multicast users by sharing the same time/space/frequency resource is designed. However, NOMA deployment in the 5G NR mmWave is still under discussion by 3GPP [32].…”

Section: Related Workmentioning

confidence: 99%

Efficient Management of Multicast Traffic in Directional mmWave Networks

Chukhno

Pizzi

et al. 2021

IEEE Trans. on Broadcast.

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Multicasting is becoming more and more important in the Internet of Things (IoT) and wearable applications (e.g., high definition video streaming, virtual reality gaming, public safety, among others) that require high bandwidth efficiency and low energy consumption. In this regard, millimeter wave (mmWave) communications can play a crucial role to efficiently disseminate large volumes of data as well as to enhance the throughput gain in fifth-generation (5G) and beyond networks. There are, however, challenges to face in view of providing multicast services with high data rates under the conditions of short propagation range caused by high path loss at mmWave frequencies. Indeed, the strong directionality required at extremely high frequency bands excludes the possibility of serving all multicast users via a single transmission. Therefore, multicasting in directional systems consists of a sequence of beamformed transmissions to serve all multicast group members, subgroup by subgroup. This paper focuses on multicast data transmission optimization in terms of throughput and, hence, of the energy efficiency of resource-constrained devices such as wearables, running their resource-hungry applications. In particular, we provide a means to perform the beam switching and propose a radio resource management (RRM) policy that can determine the number and width of the beams required to deliver the multicast content to all interested users. Achieved simulation results show that the proposed RRM policy significantly improves network throughput with respect to benchmark approaches. It also achieves a high gain in energy efficiency over unicast and multicast with fixed predefined beams.

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Quality of service‐aware adaptive radio resource management based on deep federated Q‐learning for multi‐access edge computing in beyond 5G cloud‐radio access network

Kumar

Ahmad

2023

Trans Emerging Tel Tech

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Due to the exponential increase of mobile data services, both industry and academia have turned their focus for improving the quality of service (QoS) provisioning for the beyond 5G (B5G) networks. In this paper, an adaptive QoS-aware resource allocation scheme is developed for multi-tenant and multi-service cloud-radio access networks (C-RAN) mobile edge computing scenario with the aim of maximizing network performance over time. First, a multi-user C-RAN resource allocation architecture for task computation and result collection is developed, where edge nodes can take on user equipments' (UEs) computation-intensive activities. Then deep reinforced learning (DRL) based multi-user resource allocation scheme, that is, deep federated Q-learning (DFQL) is developed for determining the best resource allocation policy. To maximize the validation accuracy of the global model in the FL while satisfying resource restrictions, this technique learns resource allocation immediately in contrast to the present allocation mechanisms. A realistic dataset is built for assessing the proposed method using 5G experimental prototype based on open air interface (OAI). At last, the effectiveness of the proposed scheme is demonstrated, where the performance of the scheme is compared with the existing resource allocation techniques.

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Characterizing Resource Allocation Trade-Offs in 5G NR Serving Multicast and Unicast Traffic

Cited by 38 publications

References 21 publications

Optimal Multicasting in Millimeter Wave 5G NR With Multi-Beam Directional Antennas

Optimal Multicasting in Millimeter Wave 5G NR With Multi-Beam Directional Antennas

Efficient Management of Multicast Traffic in Directional mmWave Networks

Quality of service‐aware adaptive radio resource management based on deep federated Q‐learning for multi‐access edge computing in beyond 5G cloud‐radio access network

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