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

Chukhno, Nadezhda; Chukhno, Olga; Moltchanov, Dmitri; Molinaro, Antonella; Gaidamaka, Yuliya; Samouylov, Konstantin; Koucheryavy, Yevgeni; Araniti, Giuseppe

doi:10.1109/tmc.2021.3136298

Cited by 11 publications

(13 citation statements)

References 28 publications

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“…To summarize, optimal multicasting is a complex problem for both single-and multi-beam mmWave systems that can be solved exactly for a limited set of UEs only. Specifically, in [24], authors demonstrate the results considering only 8 UEs in the system, whereas only 12 UEs are considered in [29] due to the solution time complexity as well as memory challenges.…”

Section: Discussionmentioning

confidence: 99%

“…In contrast to existing works, we also consider adaptive multi-lobe beamforming antennas, which further complicates the problem. To this aim, in order to determine a computationally efficient and accurate solution of the multicasting problem, in what follows, we first provide the exact solution [29] and then utilize it to train candidate ML alternatives.…”

Section: Discussionmentioning

confidence: 99%

“…), where K is a number of multicast UEs in the multicast group. In [29], an optimal multicast grouping and resource allocation problem is formulated based on a variable-sized bin packing problem, which is known to be NP-hard. To this end, several heuristics with different complexities and approximation accuracy are developed to provide practical algorithms with reduced computational requirements.…”

Section: B Multi-beam Antennasmentioning

confidence: 99%

“…To optimize the mmWave NR resource utilization with multi-beam directional antennas, we consider the ratio of occupied resources to the overall set of available resources, ρ, as the optimization criterion. By solving the optimization problem, we also determine the inter-site distance D and, thus, η -the minimum NR BS deployment density required for multicast service provisioning [29].…”

Section: Metrics Of Interestmentioning

confidence: 99%

“…This section introduces our framework for optimal resource allocation of UEs in a multi-beam environment [29]. We first formulate the problem as a bin packing formalism.…”

Section: Problem Formalizationmentioning

confidence: 99%

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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: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: B Multi-beam Antennasmentioning

confidence: 99%

Section: Metrics Of Interestmentioning

confidence: 99%

“…This section introduces our framework for optimal resource allocation of UEs in a multi-beam environment [29]. We first formulate the problem as a bin packing formalism.…”

Section: Problem Formalizationmentioning

confidence: 99%

See 3 more Smart Citations

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

Chukhno

Moltchanov

et al. 2023

IEEE Trans. on Broadcast.

Self Cite

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Capacity Analysis of Full-Duplex Directional Wireless Mobile Networks

Abusabah,

Oliveira

2023

IEEE Access

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This paper presents a comprehensive study of capacity characterization in mobile wireless networks employing directive communications and full-duplex communication technology. We address the impact of nodes' mobility, directive beams, and full-duplex capabilities on the network capacity. Firstly, we derive the signal-to-interference-plus-noise ratio (SINR) for a reference receiver, considering the combined effects of mobility, directive beams, and full-duplex communications. The derived SINR is then utilized to calculate the network's capacity. To validate the proposed model, extensive simulations are performed, incorporating various scenarios of mobility levels, self-interference suppression levels in the full-duplex wireless system, and different receiving thresholds. The simulation results confirm the accuracy and effectiveness of the derived SINR-based capacity characterization model under real-world conditions. Furthermore, we study the probability of a receiver simultaneously decoding multiple packets as a function of the receiver's threshold and full-duplex self-interference suppression. The probability is crucial for assessing the network performance and facilitating future optimization schemes aimed at regulating the number of competing nodes in a given spatial region. The validated model and probability characterization offer valuable insights into network performance evaluation and can be instrumental in devising future efficient optimization strategies for managing network contention at the medium access control and enhancing the network performance.

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