Greedy–knapsack algorithm for optimal downlink resource allocation in LTE networks

Ferdosian, Nasim; Othman, Мohamed; Ali, Borhanuddin Mohd; Lun, Kweh Yeah

doi:10.1007/s11276-015-1042-9

Cited by 19 publications

(13 citation statements)

References 30 publications

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“…The difference between the weighted and current transmission rate is considered as the gain in (13). If the current rate Ω (j, k) is higher than the weighted rate W i,j,k,l , there will be no gain for that particular user.…”

Section: Proposed Greedy-based Modelmentioning

confidence: 99%

“…Channel dependent packet schedulers are more accurate in real life scenarios as they consider more parameters than channel independent schedulers. Most of the previous researchers have used proportional fairness (PF) [5][6][7], Round Robin (RR) [8,9] and other algorithms [10][11][12][13][14][15][16][17] for resource allocation with CA in an LTE-A system. However, some important parameters, such as packet delay or error rate are not considered.…”

Section: Introductionmentioning

confidence: 99%

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An Improved Radio Resource Management with Carrier Aggregation in LTE Advanced

et al. 2017

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Long term evolution-advanced (LTE-A) system introduces carrier aggregation (CA) technique to improve the user throughput by aggregating multiple component carriers (CCs). Previous research works related to downlink radio resource allocation with carrier aggregation have not considered the delay factor and the error probability. Therefore, the previous methods are unable to provide better quality of service (QoS) compared to the LTE-A standard. This paper considers the radio resource management problem by zooming into the head of line delay, probability of packet loss, and the delay threshold for different types of data. In doing this, several constraints are imposed following the specifications of LTE-A system. Hence, an improved method is developed in this study to enhance the system throughput and to maintain the computational complexity. Extensive simulations were carried out with other well-known methods to verify the overall performance of the proposed method. The result obtained indicates that the proposed method outperforms the previous methods in the measurement of average user throughput, average cell throughput, fairness index, and spectral efficiency.

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Section: Proposed Greedy-based Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Improved Radio Resource Management with Carrier Aggregation in LTE Advanced

et al. 2017

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“…One can see in Table 4 that Voice (Vo) and Video streaming (Vi) are categorised as GB services, since they are delay sensitive and demanding almost constant data rates. Therefore, allocating capacities higher than the contracted ones will not improve their QoS [41]. On the other hand, an increase in the assigned data rate of Web browsing (We) and Email (Em) can indeed improve the users' quality of experience.…”

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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“…Regarding resource allocation, we can find this problem presented in [17] where a greedy-Knapsack algorithm for downlink resource allocation in LTE networks have been discussed. Also in [18], greedy algorithm was proposed for physical resource block allocation in multi-carrier wireless communications systems.…”

Section: Related Workmentioning

confidence: 99%

A greedy approach for resource allocation in Virtual Sensor Networks

et al. 2017

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Abstract-Virtual Sensor Networks (VSNs) envision the creation of general purpose wireless sensor networks which can be easily adapted and configured to support multifold applications with heterogeneous requirements, in contrast with the classical approach of wireless sensor networks vertically optimized on one specific task/service. The very heart of VSNs' vision is the capability to dynamically allocate shared physical resources (processing power, bandwidth, storage) to multiple incoming applications. In this context, we tackle the problem of optimally allocating shared resources in VSNs by proposing an efficient greedy heuristic that aims to maximize the total revenue out of the deployment of multiple concurrent applications while considering the inherent limitations of the shared physical resources. The proposed heuristic is tested on realistic network instances with notable performances in terms of execution time while keeping the gap with respect to the optimal solution limited (below 5%in the tested environments).

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Greedy–knapsack algorithm for optimal downlink resource allocation in LTE networks

Cited by 19 publications

References 30 publications

An Improved Radio Resource Management with Carrier Aggregation in LTE Advanced

An Improved Radio Resource Management with Carrier Aggregation in LTE Advanced

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

A greedy approach for resource allocation in Virtual Sensor Networks

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