Double Hopping: A new approach for Dynamic Frequency Hopping in Cognitive Radio networks

Abstract

Abstract-one of the major challenges in designing cellular Cognitive Radio (CR) networks is the avoidance of Secondary User (SU) interference to so called Primary Users (PUs) operating in the licensed bands. Usually, SU operation has to be interrupted periodically in order to detect PU activity and avoid the respective frequencies. Recently, Dynamic Frequency Hopping (DFH) mechanisms have been suggested to enable reliable PU detection and continuous SU operation at the same time. Applying DFH in a multi-cell environment adds the challenge of mitigating Co-Channel Interference (CCI). In this paper, we introduce a new DFH approach for cellular CR networks to allow reliable PU detection and continuous SU operation while avoiding CCI: Double Hopping (DH). We present a distributed frequency assignment heuristic for DH and compare it to the optimal assignment. We show that the performance of the suboptimal distributed assignment is only slightly worse than the optimal performance, and, thus, outperforms existing distributed approaches by far.

A survey on MAC protocols for complex self-organizing cognitive radio networks

“…To overcome the limitation of complexity in DSA-MAC protocols, several approaches have been considered to model network interactions e.g. the localized variation of the island genetic algorithm (El Nainay et al 2008), graph colouring theory (Zheng and Peng 2005;Willkomm et al 2008), game theory (Younis and Krunz 2006;Zou and Chigan 2008), stochastic theory Swami et al (2005), genetic algorithms (Rondeau et al 2004), and swarm intelligence algorithms (Atakan and Akan 2007).…”

Robust Clustering of Ad-Hoc Cognitive Radio Networks under Opportunistic Spectrum Access

“…It is known that player-specific matroid congestion game admit pure equilibrium, and the number of steps towards Nash Equilibrium is upper-bounded 3 by n 2 · m. In our context, n is the number of debatable nodes, m is number of clusters in CRN, so the total time complexity to achieve the Nash Equilibrium using greedy approach is O(n 2 m). This is upper-bounded (in the worst case) by O(|I| 3 ).…”

“…This typically requires all CR users 1 within some cluster to stop payload transmission on the operating channel and initiate the sensing process. Furthermore, by clustering (and shifting the sensing process) the potential for collisions (when vacating the channel due to primary node appearance) among neighboring clusters is reduced [3]. Finally, routing becomes simplified if clusters are formed in cognitive ad-hoc 0 Di Li and James Gross are with the Mobile Network Performance Group, UMIC Research Center, RWTH Aachen University, Germany, {li|gross}@umic.rwth-aachen.de; This work was supported in part by NRW State, Germany, within the B-IT Research School and by the DFG Cluster of Excellence on Ultra High-Speed Mobile Information and Communication (UMIC), German Research Foundation grant DFG EXC 89.…”

A MAC level strategy for dynamic resource allocation in Cognitive Radio tactical networks

“…In DSA-driven MAC protocols each cognitive radio node can adapt its transmission parameters, such as modulation, coding scheme and power transmission, in order to find the optimum configuration for the experimented radio environment. In this context, in the last decade several way to realize DSA has been proposed, such as trial and error solutions [10], algorithms derived from graph theory [11], [12], stochastic theory [13], game theory [14]. Regarding algorithms derived from heuristic [15], [16], in this context they appear to be not suitable due to their prohibitively computational cost and the necessity of a complete knowledge of the network.…”