TY - JOUR
T1 - NOn-parametric Bayesian channEls cLustering (NOBEL) Scheme for Wireless Multimedia Cognitive Radio Networks
AU - Ali, Amjad
AU - Ahmed, Muhammad Ejaz
AU - Ali, Farman
AU - Tran, Nguyen H.
AU - Niyato, Dusit
AU - Pack, Sangheon
N1 - Funding Information:
Manuscript received December 15, 2018; revised April 5, 2019; accepted May 20, 2019. Date of publication August 8, 2019; date of current version September 16, 2019. This work was supported in part by the MSIT (Ministry of Science and ICT), South Korea, under the Information Technology Research Center (ITRC) Support Program (IITP-2019-2017-0-01633), supervised by the Institute for Information & communications Technology Planning & Evaluation (IITP), and in part by the National Research Foundation under Grant 2017R1E1A1A01073742. (Corresponding author: Sangheon Pack.) A. Ali is with the School of Electrical Engineering, Korea University, Seoul 02841, South Korea, and also with the Department of Computer Science, COMSATS University Islamabad, Lahore Campus, Lahore 54000, Pakistan (e-mail: amjadali@korea.ac.kr).
Publisher Copyright:
© 1983-2012 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - In wireless multimedia cognitive radio networks (WMCRNs), to optimize multimedia transmissions and scarce wireless spectrum utilization, a multimedia secondary user (MSU) needs to estimate and/or identify the achievable quality of service (QoS)-levels over the available licensed channels. However, due to the lack of signaling information among MSUs and the primary users (PUs) in uncoordinated environments, identification of the achievable QoS-levels on the available licensed channels is a challenging problem and has not yet been fully explored. To address this challenge, we propose a novel NOn-parametric Bayesian channEls cLustering (NOBEL) scheme. In NOBEL, an infinite Gaussian mixture model-based collapsed Gibbs sampler is adopted to identify the achievable QoS-levels over the feature space, i.e., bitrate, packet delay variation, and packet delivery ratio on the PUs' licensed channels. Real trace-driven evaluation results demonstrate that NOBEL outperforms other baseline clustering techniques and guarantee high accuracy from 98% to 99.5%.
AB - In wireless multimedia cognitive radio networks (WMCRNs), to optimize multimedia transmissions and scarce wireless spectrum utilization, a multimedia secondary user (MSU) needs to estimate and/or identify the achievable quality of service (QoS)-levels over the available licensed channels. However, due to the lack of signaling information among MSUs and the primary users (PUs) in uncoordinated environments, identification of the achievable QoS-levels on the available licensed channels is a challenging problem and has not yet been fully explored. To address this challenge, we propose a novel NOn-parametric Bayesian channEls cLustering (NOBEL) scheme. In NOBEL, an infinite Gaussian mixture model-based collapsed Gibbs sampler is adopted to identify the achievable QoS-levels over the feature space, i.e., bitrate, packet delay variation, and packet delivery ratio on the PUs' licensed channels. Real trace-driven evaluation results demonstrate that NOBEL outperforms other baseline clustering techniques and guarantee high accuracy from 98% to 99.5%.
KW - QoS-level quantification
KW - Wireless multimedia applications
KW - channel clustering
KW - multi-channel
KW - multimedia CRNs
UR - http://www.scopus.com/inward/record.url?scp=85070695147&partnerID=8YFLogxK
U2 - 10.1109/JSAC.2019.2933943
DO - 10.1109/JSAC.2019.2933943
M3 - Article
AN - SCOPUS:85070695147
SN - 0733-8716
VL - 37
SP - 2293
EP - 2305
JO - IEEE Journal on Selected Areas in Communications
JF - IEEE Journal on Selected Areas in Communications
IS - 10
M1 - 8792138
ER -