Performance analysis of cognitive‐based radio resource allocation in multi‐channel LTE‐A networks with M2M/H2H coexistence

AlQahtani, Salman A.

doi:10.1049/iet-com.2016.0469

Cited by 13 publications

(6 citation statements)

References 15 publications

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“…We use two performance metrics to validate our model: SCR : It gives the number of completed requests per time interval and it is based on the service rate

μ

and the number of ongoing requests for a certain traffic (e.g.

SC {normalR}_{HP}

and

SC {normalR}_{LP}

which represent the SCR for HP/LP traffics [11]):

SCR = \sum_{i j} i μ π_{false(i, j false)}

As we consider the service rate (

μ

) as constant in our model, (13) can be written as

SCR = μ \sum_{i} i \sum_{j} π_{false(i, j false)}

As the sum of conjoint probability leads to the marginal probability, the previous equations can be re‐written as follows:

\begin{matrix} right leftthickmathspace.5em \\ normalSCR & = μ \underset{i}{false\sum} i π_{(i)} \\ = μ E (i) \end{matrix}

where E ( i ) represents the expected value of an ongoing service denoted by ( i ). Resource utilisation (

R_{normalu}

): This metric gives the probability of the system to be busy serving the arrivals in terms of the number of utilised RBs in each state:

\begin{matrix} right leftthickmathspace.5em \\ R_{u} & = \underset{i j}{false\sum} \frac{i + j}{c} π_{false(i, j false)} \\ = {false\sum}_{i j} i π_{false(i, j false)} ... \end{matrix}

…”

Section: Ctmc Analytical Methodologymentioning

confidence: 99%

“…SCR : It gives the number of completed requests per time interval and it is based on the service rate

μ

and the number of ongoing requests for a certain traffic (e.g.

SC {normalR}_{HP}

and

SC {normalR}_{LP}

which represent the SCR for HP/LP traffics [11]):

SCR = \sum_{i j} i μ π_{false(i, j false)}

…”

Section: Ctmc Analytical Methodologymentioning

confidence: 99%

“…In [10], a shared channel resource allocation in an H2H/M2M coexistence scenario was considered in order to formulate the resource sharing problem among M2M and H2H communications. In [11], the author proposes a cognitive‐based radio access model with a priority queuing scheme. The model is applied for a LTE‐A networks with M2M/H2H coexistence distinguishing M2M devices based on their traffic quality of service (QoS) requirements, in which M2M communications have real‐time (M2M‐RT) and non‐real‐time (M2M‐NRT) traffic.…”

Section: State Of the Art And Motivationsmentioning

confidence: 99%

See 2 more Smart Citations

CTMC modelling for H2H/M2M coexistence in LTE‐A/LTE‐M networks

Fawal

Najem

Mansour

et al. 2018

J. eng.

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“…We use two performance metrics to validate our model: SCR : It gives the number of completed requests per time interval and it is based on the service rate

μ

and the number of ongoing requests for a certain traffic (e.g.

SC {normalR}_{HP}

and

SC {normalR}_{LP}

which represent the SCR for HP/LP traffics [11]):

SCR = \sum_{i j} i μ π_{false(i, j false)}

As we consider the service rate (

μ

) as constant in our model, (13) can be written as

SCR = μ \sum_{i} i \sum_{j} π_{false(i, j false)}

As the sum of conjoint probability leads to the marginal probability, the previous equations can be re‐written as follows:

\begin{matrix} right leftthickmathspace.5em \\ normalSCR & = μ \underset{i}{false\sum} i π_{(i)} \\ = μ E (i) \end{matrix}

where E ( i ) represents the expected value of an ongoing service denoted by ( i ). Resource utilisation (

R_{normalu}

): This metric gives the probability of the system to be busy serving the arrivals in terms of the number of utilised RBs in each state:

\begin{matrix} right leftthickmathspace.5em \\ R_{u} & = \underset{i j}{false\sum} \frac{i + j}{c} π_{false(i, j false)} \\ = {false\sum}_{i j} i π_{false(i, j false)} ... \end{matrix}

…”

Section: Ctmc Analytical Methodologymentioning

confidence: 99%

“…SCR : It gives the number of completed requests per time interval and it is based on the service rate

μ

and the number of ongoing requests for a certain traffic (e.g.

SC {normalR}_{HP}

and

SC {normalR}_{LP}

which represent the SCR for HP/LP traffics [11]):

SCR = \sum_{i j} i μ π_{false(i, j false)}

…”

Section: Ctmc Analytical Methodologymentioning

confidence: 99%

Section: State Of the Art And Motivationsmentioning

confidence: 99%

See 1 more Smart Citation

CTMC modelling for H2H/M2M coexistence in LTE‐A/LTE‐M networks

Fawal

Najem

Mansour

et al. 2018

J. eng.

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“…Results by testing against RR and B-CQI indicated that the technique provides a balance between fairness and throughput. AlQahtani et al [60] proposed a queuing-model based access strategy in the case of H2H and M2M coexistence. The authors evaluated the system performance using a continuous-time Markov-Chain model.…”

Section: Hybrid / Multi-objective Techniquesmentioning

confidence: 99%

A Survey on LTE/LTE-A Radio Resource Allocation Techniques for Machine-to-Machine Communication for B5G Networks

et al. 2021

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The Machine-to-Machine (M2M) communication refers to an autonomous communication among devices that aims for a massive number of connected devices. M2M communication can support ubiquitous communication and full mechanical automation, and it will change everything from industry to ourselves. Recent developments in communication technology make Long Term Evolution (LTE)/Long Term Evolution-Advance (LTE-A) a promising technology for supporting M2M communication. LTE can support the diverse characteristic of M2M communication due to its IP connectivity, coverage area, and scalability. Therefore, the LTE schedulers should satisfy the need for M2M communication.Motivated from these facts, in this paper, we present a survey on classification of LTE / LTE-A scheduling methodologies from the perspective of M2M communication. We classify the schedulers based on their objectives, such as energy efficiency, spectrum efficiency, group-based, and Quality-of-Service (QoS) support for Machine Type Communication Devices (MTCDs). We also highlight the scope of future research direction for the scheduling work.

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“…In [7], despite the mathematical model for LTE downlink bandwidth allocation that was proposed with the aim of providing a good QoS for each UE, the coexistence between LTE-M and LTE-A systems and the bandwidth adaptation are not spotted. In [8], a proposed cognitive-based radio access strategy with a priority queuing scheme is applied in LTE-A networks with M2M/H2H coexistence distinguishing M2M devices based on their traffic QoS requirements. Although an analytical model is developed in normal scenarios, but the expected surge number of M2M devices which might have higher priority during disaster scenarios and how to deal with this sticky situation hadn't been discussed.…”

Section: State Of the Artmentioning

confidence: 99%

LTE-M adaptive eNodeB for emergency scenarios

Fawal¹,

Mansour²,

Najem³

et al. 2017

2017 International Conference on Information and Communication Technology Convergence (ICTC)

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Abstract-The next generation of mobile systems must provide a balancing strategy towards M2M (Machine-to-Machine) traffic, while maintaining a sustainable Quality of Service (QoS) for H2H (Human-to-Human) traffic, especially with the expected exponential growth of the number of M2M devices in the coming years carried by the advance of the IoT (Internet of Things) technology. In normal situations, it is obvious that using a 1.4 MHz bandwidth in Long Term Evolution for Machines (LTE-M), helps in improving M2M device complexity, cost and battery life effectively. However, in emergency scenarios, an expected M2M storm will lead inevitably to a fast resource depletion accompanied by an eNodeB congestion in a split second. In this manuscript, we propose a novel scheme "LTE-M Adaptive eNodeB", which gradually solves the eNodeB overload problem, while keeping the H2H traffic QoS not to be affected badly. Moreover, We adaptively manage network resources to allow both traffic to efficiently access the LTE network via SimuLTE opensource modeler. Eventually, an evaluation of the mutual impact of M2M and H2H coexistence is also presented.

show abstract

Performance analysis of cognitive‐based radio resource allocation in multi‐channel LTE‐A networks with M2M/H2H coexistence

Cited by 13 publications

References 15 publications

CTMC modelling for H2H/M2M coexistence in LTE‐A/LTE‐M networks

CTMC modelling for H2H/M2M coexistence in LTE‐A/LTE‐M networks

A Survey on LTE/LTE-A Radio Resource Allocation Techniques for Machine-to-Machine Communication for B5G Networks

LTE-M adaptive eNodeB for emergency scenarios

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