Radio Network Aggregation for 5G Mobile Terminals in Heterogeneous Wireless and Mobile Networks

Shuminoski, Tomislav; Janevski, Toni

doi:10.1007/s11277-014-1813-0

Cited by 17 publications

(13 citation statements)

References 16 publications

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“…The 5G nodes (5G mobile node in the access part of the network and 5G Cloud-RAN node in the core part of the network) are multi-RAT interface, equipped with several (M) interfaces: each for different RAT. Despite our previous works [35,49,50] and using AQUAplus (advanced QoS-based user-centric aggregation plus Lyapunov optimization) algorithm from [35], the novelty in this framework is in Wireless Communications and Mobile Computing 5 (i) implementing additional intelligent battery saving module (for optimal use of energy) in the 5G mobile nodes; implementing advanced mobile cloud and fog computing features within the proposed 5G system architecture for rich computational resources (used by AQUAplus for faster and better QoS provisioning),…”

Section: Related Workmentioning

confidence: 90%

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Advanced QoS Provisioning and Mobile Fog Computing for 5G

Shuminoski

Kitanov

Janevski

2018

Wireless Communications and Mobile Computing

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This paper presents a novel QoS and mobile cloud and fog computing framework for future fifth generation (5G) of mobile and fixed nodes with radio network aggregation capability. The proposed 5G framework is leading to high QoS provisioning for any given multimedia service, higher bandwidth utilization, traffic load sharing, mobile cloud plus fog computing features, and multi-radio interface capabilities. The framework is user-centric, targeted at always-on connectivity with using radio network aggregation for available mobile broadband connections, and empowered with mobile cloud and fog computing advantages. Moreover, our proposed framework is using Lyapunov drift-plus-penalty theorem that provides a methodology for designing algorithm to maximize the average throughput and stabilize the queuing. Also, we are showing the upper bound of the consumed power and the lower bound of the battery lifetime for the proposed 5G terminal. The advanced performance of our 5G QoS plus MCC framework is evaluated using simulations and analysis with multimedia traffic in heterogeneous mobile and wireless environment. The simulation results are showing that the maximal network utilization, maximal throughput, minimal end-to-end delay, efficient energy consumption, and other performance improvements are achieved.

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Section: Related Workmentioning

confidence: 90%

“…To emphasize that the used AQUAplus algorithm is explained in detail in [35] (named plus, because it is extension of our AQUA algorithm placed within 5G mobile nodes and proxy servers, presented in related previous papers [49,50]). …”

Section: G Qos Algorithm and Mobilementioning

confidence: 99%

Advanced QoS Provisioning and Mobile Fog Computing for 5G

Shuminoski

Kitanov

Janevski

2018

Wireless Communications and Mobile Computing

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“…Furthermore, a Wi-Fi Access Point (AP) based on IEEE802.11ac is placed at the centre of each LTE cell site to boost capacity. These assumptions of cell layout are just practical examples that are common among the studies that discuss the problem of resource management in 5G or future implementations from a flow level perspective [39,40]. In this regard, the aim of considering a scenario with coexisting different RATs is to evaluate the performance of CRRM in terms of mapping service demands onto RATs according to the suitability and loading factor of these RATs.…”

Section: Case Study Scenariomentioning

confidence: 99%

A Service‐Oriented Approach for Radio Resource Management in Virtual RANs

Rouzbehani

Correia

Caeiro

2018

Wireless Communications and Mobile Computing

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Virtualisation, as a key role player of future mobile communications, promotes the idea of service-oriented architectures. This paper proposes a model of Radio Resource Management (RRM) for emerging Virtual Radio Access Networks, based on the interaction between two separated management entities: Common-RRM (CRRM) to coordinate the radio resources among the Radio Access Technologies (RATs) and a centralised virtualisation platform on top of it, called Virtual-RRM (VRRM), which is responsible for service orchestration among Virtual Network Operators, enabling the definition of various services and policies, separately from vendors and underlying RATs. The main objective of VRRM is to satisfy the Service Level Agreements associated with different service classes to the highest possible level, within the framework of proportional fairness. On the other hand, CRRM is in charge of mapping the demanded capacity of each service onto the most suitable RATs. The model is further extended to deal with extreme situations of resource shortage, resulting from high traffic loads, by introducing delay to lower priority services. The performance of the proposed model is evaluated in a practical multi-RAT scenario. Results confirm that the isolation of service classes is consistent with the introduced serving weights, while all the demanded capacities from different services are responded by the most suitable RATs. Finally, independent of the variation of traffic load, 100% of the aggregated capacity is used.

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“…Despite all, the AQUAplus algorithm has no mechanisms for dropping packets in the real queues, which leading to higher queue delays in comparison with the furthermore proposed MassiveAQUA. Plus, the MassiveAQUA algorithm is another extension of the AQUA algorithm (presented in related papers: [31] and [32]) placed within 5GMTs and proxy servers, because the AQUA algorithm is doing only multihoming (selecting the best RAT for given service) with optimization problem solved by linear programming or genetic algorithms, without considering the network delay, the network stability and the queue backlogs. The 5GT system model ( Fig.…”

Section: Related Workmentioning

confidence: 99%