Resource allocation via hierarchical clustering in dense small cell networks: A correlated equilibrium approach

Xiao, Zhu; Li, Tong; Xiang, Zhiyang; Wang, Dong; Chen, Wenjie

doi:10.1109/pimrc.2016.7794900

Cited by 14 publications

(5 citation statements)

References 13 publications

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“…Clustering as part of a resource allocation strategy in wireless networks is also investigated in the ultra-dense networks literature, see for example [14], [25]- [30]. These works can be categorized into two groups: cell clustering (see [25]- [27]), in which the existing cells of a cellular networks are merged, and user-centric clustering (see [14], [28], [29]), in which each user chooses a subset of BSs to communicate with. The work presented in this manuscript differs from these works in several key aspects.…”

Section: Bs and User Clustering As Part Of A Resource Allocation Stramentioning

confidence: 99%

“…The work presented in this manuscript differs from these works in several key aspects. First, our channel state information model differs from that of the aforementioned works which either assume that the inter-cluster interference is perfectly known for all the channels in the network [25]- [27], [30], or strictly statistical for all the channels in the network [14], [28], [29]. In our setup we assume perfect channel state information inside each virtual cell but no channel information regarding users in different virtual cells.…”

Section: Bs and User Clustering As Part Of A Resource Allocation Stra...mentioning

confidence: 99%

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Virtual Cell Clustering with Optimal Resource Allocation to Maximize Cellular System Capacity

Yemini

Goldsmith

2019

2019 IEEE Global Communications Conference (GLOBECOM)

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This work proposes a new resource allocation optimization and network management framework for wireless networks using neighborhood-based optimization rather than fully centralized or fully decentralized methods. We propose hierarchical clustering with a minimax linkage criterion for the formation of the virtual cells. Once the virtual cells are formed, we consider two cooperation models: the interference coordination model and the coordinated multi-point decoding model. In the first model base stations in a virtual cell decode their signals independently, but allocate the communication resources cooperatively. In the second model base stations in the same virtual cell allocate the communication resources and decode their signals cooperatively. We address the resource allocation problem for each of these cooperation models. For the interference coordination model this problem is an NP-hard mixedinteger optimization problem whereas for the coordinated multi-point decoding model it is convex. Our numerical results indicate that proper design of the neighborhood-based optimization leads to significant gains in sum rate over fully decentralized optimization, yet may also have a significant sum rate penalty compared to fully centralized optimization. In particular, neighborhood-based optimization has a significant sum rate penalty compared to fully centralized optimization in the coordinated multi-point model, but not the interference coordination model.

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Section: Bs and User Clustering As Part Of A Resource Allocation Stramentioning

confidence: 99%

Section: Bs and User Clustering As Part Of A Resource Allocation Stra...mentioning

confidence: 99%

Virtual Cell Clustering with Optimal Resource Allocation to Maximize Cellular System Capacity

Yemini

Goldsmith

2019

2019 IEEE Global Communications Conference (GLOBECOM)

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“…Part of literature is based on the game theory. In general, via the aid of the game model, resource management decisions are regarded as actions of players, where resources such as power [12], sub-channels [13], spectrum [14] and transmission modes are allocated to maximize the utility or minimize the cost. Another method is based on the auction theory, which is a process of resource allocation and price discovery.…”

Section: Introductionmentioning

confidence: 99%

Resource Management of Maritime Edge Nodes for Collected Data Feedback

2020

IEEE Access

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With the development of marine economy, more marine applications emerge leading to more deployed sensors and more generated data. However, current marine Internet only provides low transmission rate constrained by limited communication resources and bad transmission environment. How to efficiently use these resources and guarantee quality-of-service (QoS) of applications need to be addressed. This paper proposes an online optimization resource management algorithm to improve the communication resource efficiency with guaranteed QoSs for different applications. This algorithm needs neither of the resource cost function nor QoS constraint function at the resource scheduling node. Furthermore, the gradient information, which is usually needed in learning strategies, is not required either. Instead, this algorithm performs resource management of both computation and communication resources in each time slot only based on the observation of the last time slot. With slot-by-slot resource allocation, the communication cost can be minimized to be suitable for maritime scenarios. In the meantime, a long-term delay constraint can be satisfied. Results show that the proposed algorithm achieves the goal of reducing communication cost while guaranteeing the delay-constraint of different applications. Although more computation resources will improve its performance, this algorithm still can obtain the minimized cost given a low computation resource. INDEX TERMS Online resource management, data feedback, edge computing, maritime communications.

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“…In [15], a factor graph-based distributed algorithm was presented to decompose the joint optimisation problem of resource allocation into a series of low complexity subproblems. To enhance the network throughput, the authors in [16] designed a non-cooperative game scheme based on regret-matching to allocate the frequency spectrum and mitigate the interference, but it did not consider the quality of service (QoS) of UEs. To ensure the QoS of UEs, the idea of Nash bargaining solution was applied in [17].…”

Section: Introductionmentioning

confidence: 99%

Cluster‐based resource allocation scheme with QoS guarantee in ultra‐dense networks

Zhang

2018

IET Communications

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Resource allocation via hierarchical clustering in dense small cell networks: A correlated equilibrium approach

Cited by 14 publications

References 13 publications

Virtual Cell Clustering with Optimal Resource Allocation to Maximize Cellular System Capacity

Virtual Cell Clustering with Optimal Resource Allocation to Maximize Cellular System Capacity

Resource Management of Maritime Edge Nodes for Collected Data Feedback

Cluster‐based resource allocation scheme with QoS guarantee in ultra‐dense networks

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