Fairness Aware Downlink Scheduling Algorithm For Lte Networks

Bahreyni, Mahnaz Sotoudeh; Sattari-Naeini, Vahid

doi:10.22436/jmcs.011.01.06

Cited by 6 publications

(5 citation statements)

References 11 publications

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“…(4) when determining the priority of each user on each RB, on each CC and at each time. Besides the extended versions of the M-LWDF, other studies that investigated the packet scheduling algorithms in the literature include [6][7][8][9][10][11][12][13][14]. Based on our study, it was observed that the majority of these conventional multi-user packet scheduling algorithms offer to meet the desired QoS of either for RT packets or NRT packets.…”

Section: Introductionmentioning

confidence: 74%

An Enhanced Packet Scheduling Algorithm for the Downlink Cognitive Long Term Evolution-Advanced

Ramli¹,

Mansor²

2020

IIUMEJ

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A packet scheduling algorithm known as Enhanced Maximum-Largest Weighted Delay First (EM-LWDF) is proposed in this paper. It aims to maximize the number of Real Time (RT) and Non Real Time (NRT) users that meet their desired Quality of Service (QoS) in the cognitive Long Term Evolution-Advanced (LTE-A). This cognitive LTE-A allows its Component Carriers (CCs) to be combined with licensed CCs from another system. This enables the LTE-A to further expand the available bandwidth for packet transmission. The EM-LWDF algorithm decides the priority of each user on the basis of the desired QoS of each packet, the urgency of each packet, channel quality and the average throughput. The potential of this algorithm is validated via computer simulation where it is able to simultaneously maximize more users that meet their desired QoS in realistic RT and NRT multimedia of the downlink cognitive LTE-A. ABSTRAK: Algoritma penjadualan paket yang dikenali sebagai Enhanced Maximum-Largest Weighted Delay First (EM-LWDF) dicadangkan dalam kertas ini. Ia bertujuan untuk memaksimumkan bilangan pengguna Real Time (RT) dan Non Real Time (NRT) yang memenuhi Kualiti Perkhidmatan yang dikehendaki (QoS) dalam system kognitif Long Term Evolution-Advanced (LTE-A). Kognitif LTE-A ini membolehkan Carrier Component (CC) untuk digabungkan dengan CC berlesen dari sistem lain. Ini membolehkan LTE-A untuk memperluaskan jalur lebar yang tersedia bagi penghantaran paket. Algoritma EM-LWDF menentukan keutamaan setiap pengguna berdasarkan QoS yang dikehendaki dari setiap paket, kecemasan setiap paket, kualiti saluran dan purata penghantaran. Potensi algoritma ini disahkan melalui simulasi komputer di mana ia dapat memaksimumkan lebih banyak pengguna yang memenuhi QoS yang dikehendaki mereka dalam realistik RT dan NRT multimedia dari downlink sistem kognitif LTE-A.

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Section: Introductionmentioning

confidence: 74%

An Enhanced Packet Scheduling Algorithm for the Downlink Cognitive Long Term Evolution-Advanced

Ramli¹,

Mansor²

2020

IIUMEJ

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“…In the air interface, Bahreyni and Sattari-naeini in 2014 [4] proposed a new scheduling algorithm for LTE networks that supports rapid channel variations and aims to increase system capacity while maintaining fairness when the number of active users is greater than the number of existing RBs. This algorithm improved the performance of cell edge users based on the preferences of users with less bandwidth.…”

Section: Existing Methodsmentioning

confidence: 99%

Dynamic Resource Allocation in LTE Radio Access Network Using Machine Learning Techniques

Djomadji,

Kabiena,

Nkemeni

et al. 2023

JCC

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Current LTE networks are experiencing significant growth in the number of users worldwide. The use of data services for online browsing, e-learning, online meetings and initiatives such as smart cities means that subscribers stay connected for long periods, thereby saturating a number of signalling resources. One of such resources is the Radio Resource Connected (RRC) parameter, which is allocated to eNodeBs with the aim of limiting the number of connected simultaneously in the network. The fixed allocation of this parameter means that, depending on the traffic at different times of the day and the geographical position, some eNodeBs are saturated with RRC resources (overused) while others have unused RRC resources. However, as these resources are limited, there is the problem of their underutilization (non-optimal utilization of resources at the eNodeB level) due to static allocation (manual configuration of resources). The objective of this paper is to design an efficient machine learning model that will take as input some key performance indices (KPIs) like traffic data, RRC, simultaneous users, etc., for each eNodeB per hour and per day and accurately predict the number of needed RRC resources that will be dynamically allocated to them in order to avoid traffic and financial losses to the mobile network operator. To reach this target, three machine learning algorithms have been studied namely: linear regression, convolutional neural networks and long short-term memory (LSTM) to train three models and evaluate them. The model trained with the LSTM algorithm gave the best performance with 97% accuracy and was therefore implemented in the proposed solution for RRC resource allocation.

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“…The proposed algorithm tries to balance between fairness and spectral efficiency, as it enhances fairness in terms of throughput among the UEs while retaining the spectral efficiency. Moreover, another fairness aware scheme is proposed in [24] to adopt fast variations in channel conditions. The proposed technique fairly allocates resources between UEs to enhance the performance of the cell edge users [24].…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, another fairness aware scheme is proposed in [24] to adopt fast variations in channel conditions. The proposed technique fairly allocates resources between UEs to enhance the performance of the cell edge users [24]. Nevertheless, these algorithms do not account for the QoS requirements for different UEs, which leads to performance degradation [25].…”

Section: Related Workmentioning

confidence: 99%

Dragonfly-Based Joint Delay/Energy LTE Downlink Scheduling Algorithm

et al. 2020

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Managing radio resources in Long Term Evolution (LTE) networks is considered as one of the essential design factors for enhancing the overall system performance. Common approaches are introduced to either achieve fairness between network users or attain maximum spectral efficiency. However, these approaches do not consider optimizing energy consumption. Therefore, in this paper, a novel resource allocation algorithm based on the Dragonfly metaheuristic technique is proposed to allocate bandwidth to users. The new algorithm is called Dragonfly-based Joint Delay/Energy (DJDE) and considers the Quality of Services (QoS) requirements of the users while achieving a high level of energy efficiency. The proposed solution utilizes the Dragonfly algorithm to optimize the integration process of different scheduling policies. To evaluate the proposed algorithm, an extensive set of experiments are conducted to compare the proposed solution to the state-of-the-art techniques. Also, to assess the energy efficiency of the proposed method, another set of experiments are simulated to compare it with various algorithms that optimize energy consumption. The obtained results prove that the DJDE algorithm can satisfy the QoS requirements of the users while improving the overall system performance.

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Fairness Aware Downlink Scheduling Algorithm For Lte Networks

Cited by 6 publications

References 11 publications

An Enhanced Packet Scheduling Algorithm for the Downlink Cognitive Long Term Evolution-Advanced

An Enhanced Packet Scheduling Algorithm for the Downlink Cognitive Long Term Evolution-Advanced

Dynamic Resource Allocation in LTE Radio Access Network Using Machine Learning Techniques

Dragonfly-Based Joint Delay/Energy LTE Downlink Scheduling Algorithm

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