A Transformed Conflict Graph-Based Resource-Allocation Scheme Combining Interference Alignment in OFDMA Femtocell Networks

Meng, Yun; Li, Jiandong; Li, Hongyan; Pan, Miao

doi:10.1109/tvt.2014.2366747

Cited by 20 publications

(14 citation statements)

References 19 publications

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“…In [22], subchannel and power allocation based on IA with clustering were performed to maximum the sum rate of all secondary users (SUs) after performing IA while guaranteeing the quality of service (QoS) requirement for each secondary, and the total interference caused by SUs performing IA at each PU was ensured to be below the given threshold. In [23], to maximize the number of users with satisfactory QoS requirements in femtocell networks, a subchannel allocation scheme based on IA with clustering was proposed, and a suboptimal solution to the corresponding optimization problem was obtained through the transformed conflict graph. In [24], aiming at maximizing the sum rate of all femtocell users, a scheme of orthogonal subchannel allocation based on IA with clustering was proposed for fully connected ultra-dense femtocell networks, then an efficient algorithm with low complexity consisting of two phases was proposed for clustering and subchannel allocation.…”

Section: B Resource Allocation Based On Ia With Clusteringmentioning

confidence: 99%

“…, K }. It is assumed that all SUEs have already been associated with their serving SBSs before clustering and subchannel allocation [23], [28], [31], and combining SUE association with resource allocation based on IA with clustering in dense SCNs will be our future work. Therefore, in dense SCNs, each SBS serves multiple SUEs associated with it, which is quite different from device-to-device (D2D) networks.…”

Section: System Model and Problem Formulation A System Modelmentioning

confidence: 99%

“…Therefore, in dense SCNs, each SBS serves multiple SUEs associated with it, which is quite different from device-to-device (D2D) networks. Furthermore, in this paper, each SBS provides data stream transmission for its multiple associated SUEs through different time slots, so we assume that each SBS only serves one SUE in a given time slot of subchannel allocation [23], [28]. How to select an exclusive SUE for each SBS in a given time slot is beyond the scope of this paper, accordingly, it is also assumed to be finished before clustering and subchannel allocation.…”

Section: System Model and Problem Formulation A System Modelmentioning

confidence: 99%

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Resource Allocation Based on Interference Alignment With Clustering for Data Stream Maximization in Dense Small Cell Networks

2019

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In this paper, by allocating the limited subchannel resources based on interference alignment (IA) with clustering to mitigate the severe co-tier interference in dense small cell networks (SCNs), we aim at maximizing the number of data streams achieved by all small cell user equipments (SUEs) while guaranteeing the data stream requirement for each SUE. As the corresponding optimization problem is NP-hard, we propose a two-phases algorithm based on graph theory to obtain a suboptimal but efficient solution, which requires much lower complexity and notably reduced feedback overhead. In the first phase, all SBS-SUE pairs are grouped into disjoint clusters by the recursive partition of the constructed graph, where the edge weight is only determined by the path losses from small cell base stations (SBSs) to SUEs, so the estimation of perfect global channel state information (CSI) is avoided. Furthermore, when guaranteeing that IA is feasible within each cluster, the number of formed clusters and the size of each cluster are determined by the distributions of dense SCNs instead of being specified. In the second phase, by further treating each cluster as a new vertex, graph coloring algorithm is proposed for subchannel allocation, which only requires to estimate the perfect CSI within each cluster. The analysis of computational complexity demonstrates that the proposed two-phases algorithm has a much lower complexity compared with the optimal solution obtained by the exhaustive search. Numerical results show that the proposed solution outperforms other related schemes and exhibits a performance close to the optimal solution. INDEX TERMS Clustering, data streams, dense small cell networks (SCNs), graph coloring, graph partitioning, interference alignment (IA), subchannel allocation.

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Section: B Resource Allocation Based On Ia With Clusteringmentioning

confidence: 99%

Section: System Model and Problem Formulation A System Modelmentioning

confidence: 99%

Section: System Model and Problem Formulation A System Modelmentioning

confidence: 99%

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Resource Allocation Based on Interference Alignment With Clustering for Data Stream Maximization in Dense Small Cell Networks

2019

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“…In Figure 3(b), the clustering configuration is given, in which the green dotted line denotes the cluster, and the small circles with different colours show that the SAPs belong to different groups, that is, the SAPs with red colour denote the CMs, while those of blue colours are SMs. From Figure 3(c), we can note that, based on the clustering, SAPs 6,7,15,17,19 and 20 are grouped into the SMs and the remaining SAPs are denoted by the CMs, constituting four clusters. Furthermore, SAPs 9, 13, 1 and 2 are made up of the cluster head for respective cluster.…”

Section: An Example Of the Interference-separation Clustering Schemementioning

confidence: 99%

Demand‐aware resource allocation for ultra‐dense small cell networks: an interference‐separation clustering‐based solution

Qiu

et al. 2016

Trans. Emerging Tel. Tech.

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In this paper, we present a novel clustering-based resource allocation framework for downlink transmission in ultra-dense small cell networks. Specifically, we first model a combinatorial optimisation problem that jointly considers subchannel and power allocation and user traffic demand in terms of a large-scale network scenario. Unfortunately, the huge communication overhead and computational complexity make it challenging for traditional centralised/distributed solutions. To address this issue, we propose an interference-separation clustering-based scheme to divide the massive small cells into smaller groups with different priorities, which reduces the network scale. Different from the existing cluster construction scheme, the proposed clustering method effectively avoids the inter-cluster interference through coordination. Then, for a given cluster configuration, we formulate the distributed resource allocation problem as a local interaction game where the utility of each player comprises not only its own profits but also the interests of neighbours. We prove the existence of Nash equilibrium for the formulated game and design a hierarchical learning algorithm to achieve the Nash equilibrium, which only needs local information exchange. Finally, simulation results validate that the proposed solution outperforms some other existing approaches and is more suitable for large-scale networks.

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“…For example, bipartite graph matching methods have been applied in wireless networks [11] to solve both resource allocation problems [12]- [15] and user association problems [16]. Additionally, the classic graph coloring approach has been widely employed for resource allocation [17]- [22]. However, to the best of our knowledge, the existing graph theory based algorithms cannot be directly used for solving the realistic user-centric overlapped clustering and resource allocation problems, since they neglect the practical traffic-loads and RB constraints.…”

Section: Introduction a Motivation And Scopesmentioning

confidence: 99%

Graph-Based Joint User-Centric Overlapped Clustering and Resource Allocation in Ultradense Networks

Lin

Zhang

et al. 2018

IEEE Trans. Veh. Technol.

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Abstract-With the increase of access point (AP) density and the exponential growth of mobile devices supported by ultra dense networks (UDNs), user-centric overlapped clustering relying on AP cooperation is becoming a promising design principle for guaranteeing the quality of service (QoS) required by each UE. This ambitious goal can be achieved by the joint design of user-centric overlapped clustering and resource allocation. In this context, both the traffic-load balancing and the limited availability of orthogonal resource blocks (RBs) have to be carefully considered in UDNs. To tackle these challenges, we formulate a joint user-centric overlapped clustering and resource allocation problem with the goal of maximizing the system's spectral efficiency (SE). To efficiently solve this problem, it is decoupled into two subproblems, which can be solved independently with the aid of our graph-theoretical framework. We first develop a novel distributed three-stage user-centric clustering solution, which is aware of the APs' traffic-loads. Then, based on the overlapped clusters constructed, we propose a novel two-stage graph-based user-centric resource allocation scheme for mitigating the resultant inter-cluster interference, despite the limited availability of orthogonal RBs. Our numerical results confirm the benefits of the proposed framework and show that it outperforms the benchmark solutions in terms of both its per area aggregated user rate (PAAR) and user rate.

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A Transformed Conflict Graph-Based Resource-Allocation Scheme Combining Interference Alignment in OFDMA Femtocell Networks

Cited by 20 publications

References 19 publications

Resource Allocation Based on Interference Alignment With Clustering for Data Stream Maximization in Dense Small Cell Networks

Resource Allocation Based on Interference Alignment With Clustering for Data Stream Maximization in Dense Small Cell Networks

Demand‐aware resource allocation for ultra‐dense small cell networks: an interference‐separation clustering‐based solution

Graph-Based Joint User-Centric Overlapped Clustering and Resource Allocation in Ultradense Networks

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