2019/02/19 by Cheng‐Shang Chang, Chang, Cheng-Shang, Yeh-Cheng Chang +3 · 2 citations
Computer Science · Engineering · #Cognitive Radio Networks and Spectrum Sensing #Cooperative Communication and Network Coding #FOS: Computer and information sciences #Networking and Internet Architecture (cs.NI) #Wireless Communication Security Techniques
paper · pdf · doi:10.48550/arxiv.1902.06933
openalex publication_date 2019/02/19 · openalex created_date 2019/03/02 · openalex updated_date 2026/07/28
The multichannel rendezvous problem that asks two secondary users to rendezvous on a common available channel in a cognitive radio network (CRN) has received a lot of attention lately. Most rendezvous algorithms in the literature focused on constructing channel hopping (CH) sequences that guarantee finite maximum time-to-rendezvous (MTTR). However, these algorithms perform rather poorly in terms of the expected time-to-rendezvous (ETTR) even when compared to the simple random algorithm. In this paper, we propose the quasi-random (QR) CH algorithm that has a comparable ETTR to the random algorithm and a comparable MTTR to the best bound in the literature. Our QR algorithm does not require the unique identifier (ID) assumption and it is very simple to implement in the symmetric, asynchronous, and heterogeneous setting with multiple radios. In a CRN with N commonly labelled channels, the MTTR of the QR algorithm is bounded above by 9 M \lceil n1/m1 \rceil ⋅ \lceil n2/m2 \rceil time slots, where n1 (resp. n2) is the number of available channels to user 1 (resp. 2), m1 (resp. m2) is the number of radios for user 1 (resp. 2), and M=\lceil \lceil log2 N \rceil /4 \rceil *5+6. Such a bound is only slightly larger than the best O((log log N) (n1 n2)/(m1 m2)) bound in the literature. When each SU has a single radio, the ETTR is bounded above by (n1 n2)/(G)+9Mn1n2 ⋅ (1-(G)/(n1 n2))M, where G is the number of common channels between these two users. By conducting extensive simulations, we show that for both the MTTR and the ETTR, our algorithm is comparable to the simple random algorithm and it outperforms several existing algorithms in the literature.