Cluster-Based Resource Management in OFDMA Femtocell Networks With &lt;roman&gt;QoS&lt;/roman&gt; Guarantees

Hatoum, Abbas; Langar, Rami; Aitsaadi, Nadjib; Boutaba, Raouf; Pujolle, Guy

doi:10.1109/tvt.2013.2290125

Cited by 87 publications

(85 citation statements)

References 27 publications

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“…The system overhead and transmission delay caused by PRB switching should be minimized as much as possible. Here, we give a scheduling case comparison of the clusteringbased interference coordination algorithm [16] and the graph coloring algorithm [27] to our algorithm based on the allocation in Fig. 5 to analyze and contrast transmission delay and signaling overheads.…”

Section: Resource Scheduling Examplementioning

confidence: 99%

“…To minimize ISI and ensure users' quality of service, some academics have used small cell clustering to coordinate cluster members' resource allocation. Considering graph theory, a clustering-based interference coordination heuristic algorithm was proposed in [14][15][16], which forms clusters dynamically and divides resource allocation into three phases to minimize ISI: (1) cluster formation; (2) intracluster resource allocation and admission control; and (3) inter-cluster resource contention resolution, in order to minimize ISI. It is worth noting that in this model, the formation of a given cluster is updated only when the interference caused by the changes in the user number exceeds a given threshold.…”

Section: Introductionmentioning

confidence: 99%

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Radio resource coordination and scheduling scheme in ultra-dense cloud-based small cell networks

Chen

Zou

Tang

et al. 2018

J Wireless Com Network

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In a 5G ultra-dense network, dynamic network topology and traffic patterns lead to excessive system overhead and complex radio resource conflicts. The cloud radio access network and the fog computing have the advantages of high computation capabilities and low transmission delays. Therefore, by taking full advantage of these two characteristics, this study proposes a novel radio resource coordination and scheduling scheme in an ultra-dense cloud-based small cell network. Interference among small cells (or remote radio heads) can be avoided by implementing centralized cooperative processing in the base band unit pool in advance. Resource sharing in coordination and transfer depend on fog computing to relieve the overloaded cloud processing platform and reduce transmission delays, thereby maximizing resource utilization and minimizing system overhead when the network topology and number of users change dynamically. The simulation shows that the proposed scheme can increase the system throughput by 20% compared with the clustering-based algorithm; it can also increase system throughput by 33% compared with the graph coloring algorithm, decrease the signaling overhead by about 50%, and improve network's quality of service.

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Section: Resource Scheduling Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Radio resource coordination and scheduling scheme in ultra-dense cloud-based small cell networks

Chen

Zou

Tang

et al. 2018

J Wireless Com Network

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“…Note that problem (7) has been demonstrated to be NP-hard [5]. To solve it, we propose to divide it into three subproblems through clustering.…”

Section: Problem Formulationmentioning

confidence: 99%

“…Nevertheless, the computation complexity of clustering algorithms has typically proven to be too high [5]. To reduce the complexity of clustering, we propose here a suboptimal clustering mechanism called SDC.…”

Section: Partial Observation Markov Decision Process-based Semi-dynammentioning

confidence: 99%

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Clustering‐based low‐complexity resource allocation in two‐tier femtocell networks with QoS provisioning

Xie

et al. 2015

Int J Communication

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SUMMARYIn this paper, we study the resource allocation problem of the uplink transmission with delay quality-ofservice constraints in two-tier femtocell networks. Particularly, to provide statistical delay guarantees, the effective capacity is employed as the network performance measure instead of the conventional Shannon capacity. To make the problem computationally efficient and numerically tractable, we decompose the problem into three subproblems, namely, cluster configuration subproblem, intra-cluster subchannel allocation subproblem and inter-cluster power control subproblem. Firstly, we develop a low-complexity heuristic semi-dynamic clustering scheme, where the delay of the channel state information feedback via backhaul is considered. We model such system in the framework of networked partial observation Markov decision process and derive a strategy to reduce the search range for the best cluster configuration. Then, for a given cluster configuration, the cluster heads deal with subchannel allocation and power control within each cluster. We propose a subchannel allocation scheme with proportional fairness. Thereafter, the inter-cluster power control subproblem is modeled as a set of exact potential games, and a channel quality related pricing mechanism is presented to mitigate inter-cluster interference. The existence and uniqueness of Nash equilibriums for the proposed game are investigated, and an effective decentralized algorithm with guaranteed convergence is designed. Simulation results demonstrate that the proposed algorithms not only have much lower computational complexity but also perform close to the exhaustive search solutions and other existing schemes.

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Vehicular Networks

2022

Edge Networking

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Cluster-Based Resource Management in OFDMA Femtocell Networks With <roman>QoS</roman> Guarantees

Cited by 87 publications

References 27 publications

Radio resource coordination and scheduling scheme in ultra-dense cloud-based small cell networks

Radio resource coordination and scheduling scheme in ultra-dense cloud-based small cell networks

Clustering‐based low‐complexity resource allocation in two‐tier femtocell networks with QoS provisioning

Vehicular Networks

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