NOn-parametric Bayesian channEls cLustering (NOBEL) Scheme for Wireless Multimedia Cognitive Radio Networks

Ali, Amjad; Ahmed, Muhammad Ejaz; Ali, Farman; Tran, Nguyen H.; Niyato, Dusit; Pack, Sangheon

doi:10.1109/jsac.2019.2933943

Cited by 8 publications

(4 citation statements)

References 42 publications

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“…High order correlations rotate irotate Kernel affinity graphs Since S (k) 2 F = c is hard to solve, we relax this equality constraint according to Lagrangian relaxation, which approximates a difficult problem of constrained optimization by a simpler problem. If α is large enough, we can append α( S (k) 2 F − c) into (11). Therefore, the final objective function can be upgraded to min…”

Section: Inverse Rotate Operatormentioning

confidence: 99%

“…Clustering is essential to handle unlabeled data and plays a central role in data mining, whose goal is to partition unlabeled data points into clusters [8], [9]. Along with the remarkable growth in data traffic over the past years, how to effectively handle non-linear data is a daunting task [10], [11]. Traditional single kernel methods can solve this problem to some extent, nevertheless, they have a weak ability to exploit the underlying relationships of non-linear data, such as those generated by Internet of Things (IoT) sensor and surveillance video data.…”

Section: Introductionmentioning

confidence: 99%

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Multikernel Clustering via Non-Negative Matrix Factorization Tailored Graph Tensor Over Distributed Networks

Ren

Mukherjee

Bennis

et al. 2021

IEEE J. Select. Areas Commun.

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Next-generation wireless networks are witnessing an increasing number of clustering applications, and produce a large amount of non-linear and unlabeled data. In some degree, single kernel methods face the challenging problem of kernel choice. To overcome this problem for non-linear data clustering, multiple kernel graph-based clustering (MKGC) has attracted intense attention in recent years. However, existing MKGC methods suffer from two common problems: (1) they mainly aim to learn a consensus kernel from multiple candidate kernels, slight affinity graph learning, such that cannot fully exploit the underlying graph structure of non-linear data; (2) they disregard the highorder correlations between all base kernels, which cannot fully capture the consistent and complementary information of all kernels. In this paper, we propose a novel non-negative matrix factorization (NMF) tailored graph tensor MKGC method for nonlinear data clustering, namely TMKGC. Specifically, TMKGC integrates NMF and graph learning together in kernel space so as to learn multiple candidate affinity graphs. Afterwards, the highorder structure information of all candidate graphs is captured in a 3-order tensor kernel space by introducing tensor singular value decomposition based tensor nuclear norm, such that an optimal affinity graph can be obtained subsequently. Based on the alternating direction method of multipliers, the effective local and distributed solvers are elaborated to solve the proposed objective function. Extensive experiments have demonstrated the superiority of TMKGC compared to the state-of-the-art MKGC methods.

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Section: Inverse Rotate Operatormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multikernel Clustering via Non-Negative Matrix Factorization Tailored Graph Tensor Over Distributed Networks

Ren

Mukherjee

Bennis

et al. 2021

IEEE J. Select. Areas Commun.

View full text Add to dashboard Cite

show abstract

“…The authors used MIN-MAX dynamic programming to jointly optimize the cognitive MAC scheduling, encoder behavior, transmission, modulation, and coding under distortion-delay framework. In [37][38], Ali et al introduced a channel clustering scheme for MCRNs. In the proposed scheme the authors quantified the PU licensed channels into multiple clusters according to the supported bitrate, packet delay variation, and packet delivery ratio to facilitate the MSU for [39], the authors proposed a RaptorQ-based efficient multimedia transmission scheme for underlay cellular CRN.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Bitrate Video Transmission Over Cognitive Radio Networks Using Cross Layer Routing Approach

Ali

Tariq

Iqbal

et al. 2020

IEEE Trans. Cogn. Commun. Netw.

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Due to the recent developments in the wireless mesh and ad-hoc networks, multi-hop cognitive radio networks (MCRNs) have attained the significant attention towards providing the reliable multimedia communications. However, in reliable multimedia communications each multimedia application observed a very stringent quality-of-service (QoS) requirements. Moreover, in MCRNs, channel allocated to the multimedia secondary users (MSUs) can be re-occupied by the primary users (PUs) at any time which causes the end-to-end-path discontinuity that severely affect the performance of the MCRNs. Therefore, under the dynamic channel availability, selecting an end-to-end path that is not only stable but also fulfills the QoS requirements of the real time and multimedia (RM) applications is a challenging task and still an open research problem. Hence, in this paper, we propose a cross-layer routing scheme that supports adaptive bit-rate multimedia (ABM) transmissions over MCRNs. Moreover, our path selection is based on the QoSaware end-to-end path delay, and PU-activity aware endto-end path stability metrices. Furthermore, to avoid the PU interference, continuity in transmission, efficient channel utilization, and supporting error resilience over time varying wireless channels our selected end-to-end path is periodically updated. Simulation study shows that the proposed scheme is more robust and suitable for supporting ABM over MCRNs.

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“…In [35], authors introduced a novel channel clustering scheme called ''NOBEL'' for transmitting real time and multimedia contents in MCRNs. The proposed scheme, quantifies the licensed channels based on their available QoS parameters and make them available for SUs to select the licensed channel as per their desired QoS requirements.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Priority-Based Efficient Resource Allocation and Computing Framework for Vehicular Multimedia Cloud Computing

et al. 2020

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In intelligent transportation system, smart vehicles are equipped with a variety of sensing devices those offer various multimedia applications and services related to smart driving assistance, weather forecasting, traffic congestion information, road safety alarms, and many entertainment and comfort-related applications. These smart vehicles produce a massive amount of multimedia related data that required fast and real-time processing which cannot be fully handled by the standalone onboard computing devices due to their limited computational power and storage capacities. Therefore, handling such multimedia applications and services demanded changes in the underlaying networking and computing models. Recently, the integration of vehicles with cloud computing is emerged as a challenging computing paradigm. However, there are certain challenges related to multimedia contents processing, (i.e., resource cost, fast service response time, and quality of experience) that severely affect the performance of vehicular communication. Thus, in this paper, we propose an efficient resource allocation and computation framework for vehicular multimedia cloud computing to overcome the aforementioned challenges. The performance of the proposed scheme is evaluated in terms of quality of experience, service response time, and resource cost by using the Cloudsim simulator. INDEX TERMS Intelligent transportation system, multimedia cloud computing, vehicular networks, quality of experience, multimedia contents processing, response time, smart vehicle.

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NOn-parametric Bayesian channEls cLustering (NOBEL) Scheme for Wireless Multimedia Cognitive Radio Networks

Cited by 8 publications

References 42 publications

Multikernel Clustering via Non-Negative Matrix Factorization Tailored Graph Tensor Over Distributed Networks

Multikernel Clustering via Non-Negative Matrix Factorization Tailored Graph Tensor Over Distributed Networks

Adaptive Bitrate Video Transmission Over Cognitive Radio Networks Using Cross Layer Routing Approach

Dynamic Priority-Based Efficient Resource Allocation and Computing Framework for Vehicular Multimedia Cloud Computing

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