Throughput Analysis of Proportional Fair Scheduling for Sparse and Ultra-Dense Interference-Limited OFDMA/LTE Networks

Parruca, Donald; Gross, James

doi:10.1109/twc.2016.2592501

Cited by 21 publications

(6 citation statements)

References 26 publications

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“…To achieve throughput optimality with flow-level scheduling, Chen et al [17] propose a scheduling algorithm for fairly scheduling TCP flows in wireless networks with time varying channel conditions.Velkov et al [18] realize optimal proportional fair scheduling when each frame consists of either a downlink energy harvesting phase or an uplink information transmission phase. In a cellular system, Parruca et al [19] present a closed-form analytical model for the throughput expectation of proportional fair scheduling, and the model takes into account a precise Signal-to-Interference-and-Noise Ratio (SINR) distribution. Furthermore, for the effective reuse of radio resources in a device-to-device communication system, Gu et al [20] propose an optimal fair scheduling scheme that maximizes the logarithmic sum of the user data rates.…”

Section: Related Workmentioning

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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Section: Related Workmentioning

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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“…A mobile with a high SINR has a high priority, compared to users with a low SINR. Since PFS is based on variation in channel conditions mainly due to multipath fading, all mobiles get the same probability of access to resources across time [22]. As such, this algorithm offers a tradeoff between throughput and fairness [23].…”

Section: Pfs Algorithmmentioning

confidence: 99%

New strategy for resource allocation using PSO-PFS hybrid

Sylia

Gueguen

Brikh

et al. 2020

IJWMC

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In this article, we study the problem of resource allocation in a tri-sectoral cell in order to have an equal distribution of resources among users taking into account the OFDM transmission technique that has evolved in future wireless networks LTE and 5G. We propose a new strategy for resource allocation schemes able to help the users with a critical position thanks to hybridization PSO-PFS (Particle Swarm Optimization). In addition, PFS (Proportional Fair Scheduling) it take into consideration the channel state conditions of the users and the PSO algorithm provides an optimal solution to the allocation problem and improves the performances of users according to their stat. The simulation results show that the allocation of resources by the PSO-PFS hybrid algorithm guarantee a high throughput of the system by ensuring fairness between users.

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“…To dynamically multiplex the active eMBB user allocations across available resources, the proportional fair (PF) scheduling criterion [36] is applied as…”

Section: A Proposed Nsbps -At the Bs Sidementioning

confidence: 99%

Opportunistic Spatial Preemptive Scheduling for URLLC and eMBB Coexistence in Multi-User 5G Networks

Esswie

Pedersen

2018

IEEE Access

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The fifth generation (5G) of the mobile networks is envisioned to feature two major service classes: ultra-reliable low-latency communications (URLLC) and enhanced mobile broadband (eMBB). URLLC applications require a stringent one-way radio latency of 1 ms with 99.999% success probability while eMBB services demand extreme data rates. The coexistence of the URLLC and eMBB quality of service (QoS) on the same radio spectrum leads to a challenging scheduling optimization problem, that is vastly different from that of the current cellular technology. This calls for the novel scheduling solutions which cross-optimize the system performance on a user-centric, instead of network-centric basis. In this paper, a null-space-based spatial preemptive scheduler for joint URLLC and eMBB traffic is proposed for the densely populated 5G networks. Proposed scheduler framework seeks for cross-objective optimization, where the critical URLLC QoS is guaranteed while extracting the maximum possible eMBB ergodic capacity. It utilizes the system spatial degrees of freedom in order to instantly offer an interference-free subspace for the critical URLLC traffic. Thus, a sufficient URLLC decoding ability is always preserved, and with the minimal impact on the eMBB performance. Analytical analysis and extensive system level simulations are conducted to evaluate the performance of the proposed scheduler against the state-of-the-art scheduler proposals from industry and academia. Simulation results show that the proposed scheduler offers extremely robust URLLC latency performance with a significantly improved ergodic capacity.INDEX TERMS 5G, radio resource management, scheduling, ultra-reliable low-latency communications (URLLC), enhanced mobile broadband (eMBB), MU-MIMO, preemptive, null space.

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Throughput Analysis of Proportional Fair Scheduling for Sparse and Ultra-Dense Interference-Limited OFDMA/LTE Networks

Cited by 21 publications

References 26 publications

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

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

New strategy for resource allocation using PSO-PFS hybrid

Opportunistic Spatial Preemptive Scheduling for URLLC and eMBB Coexistence in Multi-User 5G Networks

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