Optimized channel allocation in emerging mobile cellular networks

Asuquo, Daniel E.; Ekpenyong, Moses E.; Udoh, Samuel S.; Robinson, Samuel; Attai, Kingsley

doi:10.1007/s00500-020-04947-z

Cited by 11 publications

(10 citation statements)

References 33 publications

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“…Refs. [17][18][19][20][21][22][23][24][25][26][27][28][29]). The classification of these models can be done in various different ways, for example, according to the adopted CAC mechanism, the call arrival process, the amount of b.u.…”

Section: Related Workmentioning

confidence: 99%

“…requested by calls or even according to whether call queueing is allowed or not. In this section, we classify them into loss models [17][18][19][20][21][22][23][24] and loss/ queueing models [25][26][27][28][29].…”

Section: Related Workmentioning

confidence: 99%

“…In Refs [17][18][19][20], new or handover calls request only a single b.u. so as to be accepted in the system.…”

Section: Related Workmentioning

confidence: 99%

“…In Ref. [20], a modified Erlang loss model is considered where handover calls have access to a certain amount of b.u. based on the BR policy.…”

Section: Related Workmentioning

confidence: 99%

“…based on the BR policy. In Refs [17][18][19][20], the fact that variations of the BR policy are adopted may lead to a substantial CBP increase of new calls, which is not desirable. In Refs [21][22][23][24], handover traffic can simply be a component of the corresponding offered traffic load.…”

Section: Related Workmentioning

confidence: 99%

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Performance evaluation of a mobile hotspot supporting quasi‐random traffic

et al. 2023

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The authors study and evaluate a mobility‐aware call admission control algorithm in a mobile hotspot. More specifically, a vehicle which has an access point of a fixed capacity and may alternate between stop and moving phases is considered. In the stop phase, the vehicle services new and handover calls. To prioritise handover calls a probabilistic bandwidth reservation policy is considered where a fraction of the capacity is reserved for handover calls. Based on this policy, new calls may enter the reservation space with a predefined probability. In addition, handover calls have the option to wait in a queue of finite size if there are no available resources at the time of their arrival. In the moving phase, the vehicle services only new calls under the classical complete sharing policy. In both phases, calls arrive in the system according to a quasi‐random process, require a single bandwidth unit for their acceptance in the system and have an exponentially distributed service time. To analytically determine the various performance measures, such as time congestion probabilities, call blocking probabilities and link utilisation, an accurate analytical method is presented based on three‐dimensional Markov chains.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…In Refs [17][18][19][20], new or handover calls request only a single b.u. so as to be accepted in the system.…”

Section: Related Workmentioning

confidence: 99%

“…In Ref. [20], a modified Erlang loss model is considered where handover calls have access to a certain amount of b.u. based on the BR policy.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Performance evaluation of a mobile hotspot supporting quasi‐random traffic

et al. 2023

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Efficient machine learning‐based hybrid resource allocation for device‐to‐device underlay communication in 5G wireless networks

Gopal,

2024

Int J Communication

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SummaryDevices in close proximity can directly communicate with each other through device‐to‐device (D2D) communication, bypassing the need for base stations. To minimize the delay for D2D active clients, we use a sequential approach to reuse cellular‐type resources. Unlike conventional methods that focus solely on uplink or downlink resource distribution, our study introduces a novel optimization technique to maximize network throughput. Our proposal incorporates a hybrid approach that combines both downlink and uplink techniques for resource allocation. We utilize random forest and game theory algorithms to ensure smooth D2D communication while reducing interference between cellular and D2D pairings. Our hybrid structure effectively addresses challenges arising from intra‐ and inter‐cell interferences resulting from spectrum reusability and deployment. This approach also improves power control and quality of service. Traditionally, NP‐hard optimization solutions are proposed for mixed‐integer nonlinear problems. In our study, the critical stages include channel assignment and energy allocation. The objective problem in resource allocation considers parameters such as the transmission power of D2D active clients, cellular users, connection distance, base stations, and quality of service constraints. Our proposed hybrid method aims to enhance overall spectrum efficiency and network throughput. Simulated results demonstrate the superiority of our modified hybrid technique compared to existing joint resource allocation methods. This comprehensive approach represents a significant advancement in addressing the complexities associated with D2D communication, offering improved efficiency and performance in contemporary network environments.

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