TY - JOUR
T1 - Throughput oriented lightweight near-optimal rendezvous algorithm for cognitive radio networks
AU - Xin, Chun Sheng
AU - Ullah, Sharif
AU - Song, Min
AU - Wu, Zhao
AU - Gu, Qiong
AU - Cui, Huanqing
N1 - Publisher Copyright:
© 2018
PY - 2018/6/4
Y1 - 2018/6/4
N2 - In cognitive radio networks, secondary users have to dynamically search and access spectrum unused by primary users. Due to this dynamic spectrum access nature, the rendezvous between secondary users is a great challenge for cognitive radio networks. In this paper, we propose a Throughput oriEnted lightweight Near-Optimal Rendezvous (TENOR) algorithm that does not need a common control channel. TENOR has very lightweight overhead and accomplishes near-optimal performance with regard to both throughput and rendezvous time. With TENOR, secondary users are grouped into node pairs that are spread onto different channels in a decentralized manner. The co-channel interference is minimized and the throughput is near optimal. We develop a mathematical model to analyze the performance of TENOR. Both analytical and simulation results indicate that TENOR achieves near-optimal throughput and rendezvous time, and significantly outperforms the state-of-the-art rendezvous algorithms in the literature.
AB - In cognitive radio networks, secondary users have to dynamically search and access spectrum unused by primary users. Due to this dynamic spectrum access nature, the rendezvous between secondary users is a great challenge for cognitive radio networks. In this paper, we propose a Throughput oriEnted lightweight Near-Optimal Rendezvous (TENOR) algorithm that does not need a common control channel. TENOR has very lightweight overhead and accomplishes near-optimal performance with regard to both throughput and rendezvous time. With TENOR, secondary users are grouped into node pairs that are spread onto different channels in a decentralized manner. The co-channel interference is minimized and the throughput is near optimal. We develop a mathematical model to analyze the performance of TENOR. Both analytical and simulation results indicate that TENOR achieves near-optimal throughput and rendezvous time, and significantly outperforms the state-of-the-art rendezvous algorithms in the literature.
KW - Cognitive radio network
KW - Lightweight rendezvous
KW - Near-optimal rendezvous
KW - Rendezvous
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U2 - 10.1016/j.comnet.2018.03.009
DO - 10.1016/j.comnet.2018.03.009
M3 - Article
AN - SCOPUS:85044150032
SN - 1389-1286
VL - 137
SP - 49
EP - 60
JO - Computer Networks
JF - Computer Networks
ER -