Interference Hypergraph-Based Resource Allocation (IHG-RA) for NOMA-Integrated V2X Networks

Chen, Chen; Wang, Baoji; Zhang, Rongqing

doi:10.1109/jiot.2018.2875670

Cited by 74 publications

(47 citation statements)

References 32 publications

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“…The authors then derived a closed-form expression for the outage probability, where the NOMA-enabled V2X system outperformed the conventional OMA-enabled system. References [13] and [14] applied a centralized resource allocation scheme to improve the sum rate of D2D-enhanced V2X NOMA networks. The authors first constructed an interference hypergraph to model interference environment and, based on this constructed hypergraph model, a cluster coloring algorithm for interference hyper-graph resource management scheme was investigated.…”

Section: A Recent Workmentioning

confidence: 99%

Multiobjective Optimization of Uplink NOMA-Enabled Vehicle-to-Infrastructure Communication

et al. 2020

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Vehicular communication is gradually evolving from the rudimentary data exchange techniques to innovative, efficient, and large-scale communication platform. The future vehicular networks aim to offer new services and achieve massive connectivity with the aid of advanced wireless communication techniques. In this regard, one of the emerging paradigms is non-orthogonal multiple access (NOMA) which holds the promise to meet the resource-intensive demands of future networks. Due to this reason, the applications of NOMA are being investigated by the researcher of academia and industry alike. This article aims to build on the state-of-the-art by providing a novel multiobjective optimization framework for vehicle-to-infrastructure (V2I) communications. Specifically, this work aims to improve upon the sum-rate and energy efficiency of uplink NOMA-enabled V2I communications. The formulated problem is based on the tradeoff between sum-rate and total transmit power while maintaining the quality of service (QoS) requirements of the network. To provide a reliable solution, a multiobjective optimization framework has been proposed by applying the weighted-sum method and handle joint metrics of sum-rate maximization and total transmit power minimization. Extensive simulations have been performed to validate the superiority of the proposed framework against conventional benchmark techniques. The results clearly indicate that the proposed framework is feasible for NOMA-enabled V2I communication and can be scaled up for practical implementation. INDEX TERMS Non-orthogonal multiple access, multiobjective optimization, QoS, vehicular communication.

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Section: A Recent Workmentioning

confidence: 99%

Multiobjective Optimization of Uplink NOMA-Enabled Vehicle-to-Infrastructure Communication

et al. 2020

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“…In a graph or network, clusters are typically groups of vertices with a higher probability of connecting to each other than to members of other groups, although other patterns are possible [2]. Clusters have many application scenarios in the Internet of Things [3], including sensor networks [4], vehicular ad hoc networks [5]- [7], and in-vehicle networks [8]- [10]. Community detection can be viewed as a problem of graph clustering in which each community corresponds to a cluster in the graph [11], [12].…”

Section: A Backgroundmentioning

confidence: 99%

“…These interactions correspond to induced subgraphs of networks that contain multiple vertices and edges and represent the information from different interactions among multiple vertices, and this kind of subgraph is also refered to as a motif [18]. The motif of a network is crucial to organization of complex networks [19], [20] and has a wide range of application scenarios in many fields, such as carbon exchange models in food chains, resource allocation in the Internet of Things [7], and analysis of small structures in social networks [21]. The use of motifs as atomic units in graph clustering is known as higher-order graph clustering.…”

Section: A Backgroundmentioning

confidence: 99%

Local Expansion and Optimization for Higher-Order Graph Clustering

Cai

et al. 2019

IEEE Internet Things J.

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Graph clustering aims to identify clusters that feature tighter connections between internal nodes than external nodes. We noted that conventional clustering approaches based on a single vertex or edge cannot meet the requirements of clustering in a higher-order mixed structure formed by multiple nodes in a complex network. Considering the above limitation, we are aware of the fact that a clustering coefficient can measure the degree to which nodes in a graph tend to cluster, even if only a small area of the graph is given. In this study, we introduce a new cluster quality score, i.e., the local motif rate, which can effectively respond to the density of clusters in a higherorder graph. We also propose a motif-based local expansion and optimization algorithm (MLEO) to improve local higher-order graph clustering. This algorithm is a purely local algorithm and can be applied directly to higher-order graphs without conversion to a weighted graph, thus avoiding distortion of the transform. In addition, we propose a new seed-processing strategy in a higher-order graph. The experimental results show that our proposed strategy can achieve better performance than the existing approaches when using a quadrangle as the motif in the LFR network and the value of the mixing parameter µ exceeds 0.6.

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“…Sharing of bandwidth improves the spectral efficiency and more users can be accommodated at once. The capacity of the system can be enhanced by implementing NOMA by means of reducing the interference caused by the sharing user(s) in the same group via successive interference cancellation (SIC) [12][13][14][15][16][17]. Interference, without SIC, tend to grow worse when many users access the system at the same time.…”

Section: Introductionmentioning

confidence: 99%

Ant-colony and nature-inspired heuristic models for NOMA systems: a review

Liyn

Ghani²,

Roslim³

et al. 2020

TELKOMNIKA

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The increasing computational complexity in scheduling the large number of users for non-orthogonal multiple access (NOMA) system and future cellular networks lead to the need for scheduling models with relatively lower computational complexity such as heuristic models. The main objective of this paper is to conduct a concise study on ant-colony optimization (ACO) methods and potential nature-inspired heuristic models for NOMA implementation in future high-speed networks. The issues, challenges and future work of ACO and other related heuristic models in NOMA are concisely reviewed. The throughput result of the proposed ACO method is observed to be close to the maximum theoretical value and stands 44% higher than that of the existing method. This result demonstrates the effectiveness of ACO implementation for NOMA user scheduling and grouping.

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Interference Hypergraph-Based Resource Allocation (IHG-RA) for NOMA-Integrated V2X Networks

Cited by 74 publications

References 32 publications

Multiobjective Optimization of Uplink NOMA-Enabled Vehicle-to-Infrastructure Communication

Multiobjective Optimization of Uplink NOMA-Enabled Vehicle-to-Infrastructure Communication

Local Expansion and Optimization for Higher-Order Graph Clustering

Ant-colony and nature-inspired heuristic models for NOMA systems: a review

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