Enhanced exponential rule scheduling algorithm for real-time traffic in LTE network

Yaqoob, J; Pang, W. L.; Wong, Sew Kin; Chan, Kah‐Yoong

doi:10.11591/ijece.v10i2.pp1993-2002

Cited by 9 publications

(4 citation statements)

References 23 publications

(31 reference statements)

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“…Since they are not given in the M:tel database, the continuous delay values are obtained indirectly based on the research presented in [13]. First, the Real Time Video delay value is estimated depending on the number of active users for the exponential rule scheduling algorithm (from Figure 6(a) in [13]). As a result of the regression over the estimated values, a cubic function is obtained:…”

Section: Fig 2 Geographical Area Of Research [11]mentioning

confidence: 99%

PCA modeling of extraction and selection of variables influencing LTE network delay in urban mobility conditions

Stojčić,

K. Banjanin,

Vasiljević

et al. 2023

Second International Conference on Advances in Traffic and Communication Technologies (ATCT 2023)

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In addition to network throughput, which defines the number of bits per second that can be transmitted, another primary measure of network performance is latency, or delay, which defines the time spent sending one bit from one computer to another in the network. In this paper, the main goal of the research is to model the efficient management of delay performance in a 4G Long Term Evolution (LTE) network on a defined section of the trunk road with the assumption that key performance indicators (KPI) can be effectively analyzed with the created predictive classification models. These models also enable the assessment and prediction of the fulfillment of sustainable urban mobility plans. Using Principal Component Analysis (PCA), the space of 17 input variables is reduced to four extracted components. Several models based on different machine learning techniques are created using the automatic modeling method, and the final solution is selected according to the criterion of maximum classification accuracy, interpretability, and complexity. A classification model based on Logistic Regression was chosen as the final solution.

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Section: Fig 2 Geographical Area Of Research [11]mentioning

confidence: 99%

PCA modeling of extraction and selection of variables influencing LTE network delay in urban mobility conditions

Stojčić,

K. Banjanin,

Vasiljević

et al. 2023

Second International Conference on Advances in Traffic and Communication Technologies (ATCT 2023)

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“…The simulation shows an increase in the QoS-fairness and overall throughput of the edge users without producing a substantial degradation in the system throughput when compared with best CQI, PF, RR and Coordinated Multi Point (CoMP) structure with RR. Yaqoob et al [20] presented an enhanced EXPRULE (eEXPRULE) scheduling algorithm for real-time (RT) traffic in LTE network. The scheduler shows improvement on all the metrics used by most scheduler reviewed.…”

Section: Related Workmentioning

confidence: 99%

“…Scheduling algorithm is not defined in LTE and various approaches have been presented to address this issue of scheduling algorithms [18,20] . Some results have shown some significant improvements in literature.…”

Section: Scheduling Algorithms Usedmentioning

confidence: 99%

Comparative Analysis of Scheduling Algorithms Performance in a Long Term Evolution Network

Kuboye¹

2021

j. of Comput. sci. res.

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The advancement in cellular communications has enhanced the special attention given to the study of resource allocation schemes. This study is to enhance communications to attain efficiency and thereby offers fairness to all users in the face of congestion experienced anytime a new product is rolled out. The comparative analysis was done on the performance of Enhanced Proportional Fair, Qos-Aware Proportional Fair and Logarithmic rule scheduling algorithms in Long Term Evolution in this work. These algorithms were simulated using LTE system toolbox in MATLAB and their performances were compared using Throughput, Packet delay and Packet Loss Ratio. The results showed Qos-Aware Proportional Fair has a better performance in all the metrics used for the evaluation.

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“…Yaqoob, 2020) studied the performance of existing schedulers in LTE network which include Proportional fair (PF), Modified largest weighted delay first (MLWDF), Exponential PF (EXP/PF), Logarithm rule (LOGRULE) and Exponential rule (EXPRULE) scheduler. The authors then enhanced the existing exponential rule scheduling whose limitations include high packet rate, low fairness and high delay.…”

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confidence: 99%

An Improved Proportional Fair Scheduling Algorithm for Downlink LTE Cellular Network

Ezeribe¹

2021

IJRASET

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Network providers of LTE networks can achieve maximum gain and Quality of Service (QoS) requirement of their users by employing a radio resource management technique that has the ability to allocate resource blocks to users in a fair manner without compromising the capacity of the network. This implies that for a better performing LTE network, a fair scheduling and balanced QoS delivery for various forms of traffic are needed. In this paper an improved proportional fair scheduling algorithm for downlink LTE cellular network has been developed. This algorithm was implemented using a MATLAB-based System Level simulator by Vienna University. The developed algorithm was compared to other scheduling algorithms such as the Proportional Fair (PF) algorithm, Best Channel Quality Indicator (CQI), and Round Robin (RR) scheduling methods. The system performance was also analyzed under different scenarios using different performance metrics. The achieved results showed that the developed algorithm had a better throughput performance than the Round Robin and Proportional fair scheduling. The developed algorithm shows improved cell edge throughputs of about 19.2% (as at 20 users) and 9.1% higher for cell edge users without and with mobility impact respectively. The Best CQI algorithm had higher peak throughput values but the fairness was highly compromised. The developed algorithm outperforms the Best CQI by 136.6% without the impact of mobility. Finally, in dense conditions, the developed algorithm still outperforms the other algorithms with a QoS metric of 4.6% increment when compared to the PF algorithm which was the closest competitor. Keywords: UE, eNodeB, Scheduling, Proportional Fair, LTE,

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Enhanced exponential rule scheduling algorithm for real-time traffic in LTE network

Cited by 9 publications

References 23 publications

PCA modeling of extraction and selection of variables influencing LTE network delay in urban mobility conditions

PCA modeling of extraction and selection of variables influencing LTE network delay in urban mobility conditions

Comparative Analysis of Scheduling Algorithms Performance in a Long Term Evolution Network

An Improved Proportional Fair Scheduling Algorithm for Downlink LTE Cellular Network

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