Scheduling for VoLTE: Resource Allocation Optimization and Low-Complexity Algorithms

Mohseni, Maryam; Banani, S. Alireza; Eckford, Andrew W.; Adve, Raviraj

doi:10.1109/twc.2019.2892128

Cited by 19 publications

(12 citation statements)

References 30 publications

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“…Since the optimized scheduling process is multifaceted and time-consuming, the research [27] treated the overall downlink scheduling as an NP-hard problem. Similarly, authors in [8] solved the optimization problem for throughput maximization and proposed a heuristic scheme with low complexity. However, the proposed solution resulted in an issue of fairness as the number of resources is not equally distributed among users.…”

Section: Related Workmentioning

confidence: 99%

“…In this sense, channel-aware scheduling and resource allocation algorithms [6] have been adopted in OFDMA systems by assigning RBs to users based on channel quality indicator (CQI) reports, which are regularly sent between User Equipments (UEs) to eNB. Alternatively, there are other techniques in which the packet scheduler tends to provide fairness among users regardless of the channel status [7] or achieve QoS requirements specified by the user [8]. Moreover, with the drastic increase in the number of UEs, the power consumption of the base stations has increased considerably, which negatively affects the operational cost.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dragonfly-Based Joint Delay/Energy LTE Downlink Scheduling Algorithm

et al. 2020

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“…The basic idea of the fairness index is balancing between fairness and throughput. In an IoV system, the fairness index is related to the multiple key attributes of QoS requirements from CSI, including the packet loss rate, average transmission rate, instantaneous transmission rate, instant service delay, and maximum service delay [26]. According to the feedback from the end users, the control entity can calculate the fairness index for each end user.…”

Section: ) Calculation Of the Fairness Indexmentioning

confidence: 99%

“…According to the feedback from the end users, the control entity can calculate the fairness index for each end user. If we do not consider the difference between real-time service and non-real-time service, at time slice t, the fairness index for end user k requesting service i can be defined as [26]:…”

Section: ) Calculation Of the Fairness Indexmentioning

confidence: 99%

Dynamic Resource Scheduling Optimization With Network Coding for Multi-User Services in the Internet of Vehicles

et al. 2020

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For Internet of Vehicles (IoV) systems with multiple users, network coding can be introduced to provide efficient error control and throughput improvement services. However, if the heterogeneity characteristics and requirements of the end users (vehicles) are neglected, it will be difficult for an IoV system to provide each end user with fair system services, without which the advantages of network coding cannot be fully achieved and the performance of the multiuser diversity system will be degraded. In this paper, we propose a Dynamic Resource Scheduling Optimization (DRSO) algorithm, a dynamic fair scheduling algorithm combined with network coding for system resource allocation in a multiuser IoV system. We construct a general solution framework for service scheduling: first, we estimate the fairness index for each end user (vehicle) with the key information on Quality of Service (QoS). Second, we construct a service scheduling control model based on the service capability of control entities (multiaccess edge computing servers), and propose a new utility evaluation function. Third, based on the fairness index, we select end users into multiple network coding sets. Network coding sets are the basic units of service scheduling. The optimization objective of the scheduling service is to maximize the total utility of all the network coding sets (the utility of the control entity). Finally, we establish a coding cache queue in the control entity based on the scheduling decision. To obtain the global optimal solution for active queue control, we combine a Quantum Particle Swarm Optimization (QPSO) algorithm with a Proportional Integral (PI) model. Then, the optimal scheduling decision can be made. Extensive simulation results show that DRSO outperforms related scheduling algorithms in varying traffic loads, demonstrating that DRSO can effectively guide service resource allocation. INDEX TERMS multiuser , fairness control, network coding set, cache queue, Internet of Vehicles

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“…The optimization variables are binary, hence this problem is an Integer Linear Problem (ILP) [33] (and also c.f. Problem (P1) in [34]). However, ILPs are difficult to solve and known to be NP-hard and with exponential execution time, preventing solutions even for reasonable problem sizes.…”

Section: A Problem Formulationmentioning

confidence: 99%

Performance Comparison of QoS Deployment Strategies for Cellular Network Services

Zeydan¹,

Mangues‐Bafalluy²,

Dedeoglu³

et al. 2020

IEEE Access

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Differentiated quality-of-Service (QoS) techniques are widely used to distinguish between different service classes and prioritize service needs in mobile networks. Mobile Network Operators (MNOs) utilize QoS techniques to develop strategies that are supported by the mobile network infrastructure. However, QoS deployment strategy can ensure that the radio resources provided by the base station are easily consumed if it is not used correctly or when different techniques are used all together. In this paper, we propose a scheduling algorithm and compare two different QoS deployment strategies for prioritized User Equipment (UEs) (with higher scheduling rates and dedicated bandwidth) that MNOs can use in the current infrastructure, using a commercial real-time Long Term Evolution (LTE) network in different test scenarios. Moreover, we expose the real-time user experience in terms of uplink throughput and analyze results of the UE's real-time key performance indicators (KPIs) in detail. Experiment results are evaluated considering the implications of different QoS support types on network coverage and capacity planning optimizations. Our results demonstrate that even though preconfigured resource allocations can be given to prioritize UEs, the experience of all the UEs can be affected unexpectedly in the presence of many UEs who have received different QoS deployment support. Our experimental observations have revealed that location of the UEs with respect to Base Station (BS) and the availability of dedicated bandwidth UEs inside cell may have implications on the apriori defined resource allocation strategies of the other UEs. INDEX TERMS QoS, differentiated services, mobile networks, experiments.

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Scheduling for VoLTE: Resource Allocation Optimization and Low-Complexity Algorithms

Cited by 19 publications

References 30 publications

Dragonfly-Based Joint Delay/Energy LTE Downlink Scheduling Algorithm

Dragonfly-Based Joint Delay/Energy LTE Downlink Scheduling Algorithm

Dynamic Resource Scheduling Optimization With Network Coding for Multi-User Services in the Internet of Vehicles

Performance Comparison of QoS Deployment Strategies for Cellular Network Services

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