A Homogeneous Multi-Radio Rendezvous Algorithm for Cognitive Radio Networks

A Homogeneous Multi-Radio Rendezvous Algorithm for Cognitive Radio Networks

Abstract: In this letter, a homogeneous multi-radio channel hopping (CH) rendezvous algorithm that is backward compatible with any number of radios for cognitive radio networks is proposed. The algorithm is applicable to CH sequences that have uniform time-to-rendezvous (TTR) patterns and also achieves improved (exact) maximum-TTR, which is a linear function of a number of available channels instead of quadratic functions in heterogeneous multi-radio algorithms.

Existing system: The use of multiple radios in secondary users (SUs) of channel-hopping (CH) cognitive radio networks (CRNs) has received recent attention because it can quicken the rendezvous process and shorten the time-to-rendezvous (TTR).

Examples of “multi-radio� algorithms include the general construction for rendezvous (GCR) algorithm, multi-radio sunflower-set (MSS) algorithm , jumpstay independent (JS/I) and parallel (JS/P) algorithms, Chinese- RemainderTheorem multi-radio rendezvous (CMR) algorithm, and enhanced algorithm. As studied in, these algorithms, except JS/I and JS/P, were designed for a heterogeneous available-channel environment and their maximum-TTR (MTTR)

bounds were derived in “orders.” (In a general term, heterogeneous (resp. homogeneous) assumes that the sets of available channels of SUs are different (resp. identical)).

Proposed system: In this letter, a homogeneous multi-radio CH rendezvous algorithm that is backward compatible with any number of radios is proposed in Section II. While the homogeneous assumption is not as general as heterogeneous, the new algorithm is suitable for CRNs with SUs in close proximity with the same set of available channels. The algorithm begins with assigning each SU a CH sequence from the multi-MTTR asynchronous a symmetric prime sequences (MAAPSs)1.

The MAAPSs are created by a simple, fast algebraic algorithm and provide “asynchronous” rendezvous between SUs with full degree of rendezvous (i.e., channel diversity). To support multiple radios, each SU’s CH sequence is here time-rotated systematically so that the MTTR is reduced by a factor of m1m2 at the sender and m2 at the receiver. As a result, the algorithm achieves an improved (exact) MTTR, which is proportional to N/m1m2 a linear function of N. Advantages: It is also applicable to synchronous and/or symmetric CH sequences that have uniform TTR patterns, as explained in Section II. As these algebraic CH sequences are distinct with known cardinality, they provide unique IDs to SUs to support user identification and fast rendezvous.

Finally, the MTTR comparisons in Section III show that the new algorithm has shorter MTTR than the (homogeneous) JS/I and JS/P algorithms.

Disadvantages: In this letter, a homogeneous multi-radio CH rendezvous algorithm that is backward compatible with any number of radios is proposed in Section II. While the homogeneous assumption is not as general as heterogeneous, the new algorithm is suitable for CRNs with SUs in close proximity with the same set of available channels. The algorithm begins with assigning each SU a CH sequence from the multi-MTTR asynchronous a symmetric prime sequences (MAAPSs)1.

The MAAPSs are created by a simple, fast algebraic algorithm and provide “asynchronous” rendezvous between SUs with full degree of rendezvous (i.e., channel diversity).

Modules: Channel-hopping (CH): Cognitive radio networks (CRNs) has received recent attention because it can quicken the rendezvous process and shorten the time-to-rendezvous (TTR).

Examples of “multi-radio” algorithms include the general construction for rendezvous (GCR) algorithm, multi-radio sunflower-set (MSS) algorithm, jumpstay independent (JS/I) and parallel (JS/P) algorithms, Chinese- RemainderTheorem multi-radio rendezvous (CMR) algorithm, and enhanced algorithm. As studied in, these algorithms, except JS/I and JS/P, were designed for a heterogeneous available-channel environment and their maximum-TTR (MTTR) bounds were derived in “orders.” (In a general term, heterogeneous (resp. homogeneous) assumes that the sets of available channels of SUs are different (resp. identical)).

Multi- MTTR asynchronous a symmetric prime sequence: In this letter, a homogeneous multi-radio CH rendezvous algorithm that is backward compatible with any number of radios is proposed in Section II. While the homogeneous assumption is not as general as heterogeneous, the new algorithm is suitable for CRNs with SUs in close proximity with the same set of available channels. The algorithm begins with assigning each SU a CH sequence from the multi-MTTR asynchronous a symmetric prime sequences (MAAPSs).

The MAAPSs are created by a simple, fast algebraic algorithm and provide “asynchronous” rendezvous between SUs with full degree of rendezvous (i.e., channel diversity). To support multiple radios, each SU’s CH sequence is here time-rotated systematically so that the MTTR is reduced by a factor of m1m2 at the sender and m2 at the receiver. As a result, the algorithm achieves an improved (exact) MTTR, which is proportional to N/m1m a linear function of N. It is also applicable to synchronous and/or symmetric CH sequences that have uniform TTR patterns, as explained in Section II.

MTTRs: Compares the simulated MTTRs of the new, JS/I, and JS/P algorithms with the MAAPSs as functions of N, m1, and m2. The JS/I and JS/P algorithms are here simulated with the MAAPSs for a fair comparison. The JS/I algorithm relies on “random shifts” to construct the multi-radio sequences. Hence, the MTTR is computed by averaging the simulated MTTRs over 1,000 random shifts for each pair of m1 and m2 values. The random shifts make the MTTR unstable and there are cases that the MTTR can be as large as the single-radio MTTR. Thus, the “average” MTTR is worse than that of the new algorithm. In the JS/P algorithm, the original sequence of a SU is remapped to construct the multi-radio sequences by a method that requires m1 = m2, and the MTTR is reduced to MTTR1/m1. As shown in the new algorithm has the best (i.e., shortest) MTTR because it can always achieve.