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Fast Multichannel Topology Discovery in Cognitive Radio Networks

Yung-Li Wang, Yiwei Liu, Cheng-Shang Chang

TL;DR

This work tackles rapid topology discovery in cognitive radio networks by explicitly learning both the network structure and each node's available channels under primary-user interference. It introduces two methods—the pseudo-random sweep with forward replacement and a threshold-based stick-together strategy—and analyzes how reducing trial correlation lowers the expected discovery time ($ETTD$) while maintaining a bounded maximum time-to-discovery ($MTTD$). Theoretical insights connect rendezvous trial correlation to discovery speed and are supported by simulations showing substantial improvements over baseline sweep strategies and competitive performance relative to the Pi-algorithm. The results enable faster, more scalable topology-aware neighbor discovery in CRNs, with potential extensions to asynchronous operation and more realistic interference models.

Abstract

In Cognitive Radio Networks (CRNs), secondary users (SUs) must efficiently discover each other across multiple communication channels while avoiding interference from primary users (PUs). Traditional multichannel rendezvous algorithms primarily focus on enabling pairs of SUs to find common channels without explicitly considering the underlying network topology. In this paper, we extend the rendezvous framework to explicitly incorporate network topology, introducing the \emph{multichannel topology discovery problem}. We propose a novel \emph{pseudo-random sweep algorithm with forward replacement}, designed to minimize correlation between consecutive unsuccessful rendezvous attempts, thereby significantly reducing the expected time-to-discovery (ETTD). Additionally, we introduce a \emph{threshold-based stick-together strategy} that dynamically synchronizes user hopping sequences based on partially known information, further enhancing discovery efficiency. Extensive simulation results validate our theoretical analysis, demonstrating that the proposed algorithms substantially outperform conventional (sequential) sweep methods.

Fast Multichannel Topology Discovery in Cognitive Radio Networks

TL;DR

This work tackles rapid topology discovery in cognitive radio networks by explicitly learning both the network structure and each node's available channels under primary-user interference. It introduces two methods—the pseudo-random sweep with forward replacement and a threshold-based stick-together strategy—and analyzes how reducing trial correlation lowers the expected discovery time () while maintaining a bounded maximum time-to-discovery (). Theoretical insights connect rendezvous trial correlation to discovery speed and are supported by simulations showing substantial improvements over baseline sweep strategies and competitive performance relative to the Pi-algorithm. The results enable faster, more scalable topology-aware neighbor discovery in CRNs, with potential extensions to asynchronous operation and more realistic interference models.

Abstract

In Cognitive Radio Networks (CRNs), secondary users (SUs) must efficiently discover each other across multiple communication channels while avoiding interference from primary users (PUs). Traditional multichannel rendezvous algorithms primarily focus on enabling pairs of SUs to find common channels without explicitly considering the underlying network topology. In this paper, we extend the rendezvous framework to explicitly incorporate network topology, introducing the \emph{multichannel topology discovery problem}. We propose a novel \emph{pseudo-random sweep algorithm with forward replacement}, designed to minimize correlation between consecutive unsuccessful rendezvous attempts, thereby significantly reducing the expected time-to-discovery (ETTD). Additionally, we introduce a \emph{threshold-based stick-together strategy} that dynamically synchronizes user hopping sequences based on partially known information, further enhancing discovery efficiency. Extensive simulation results validate our theoretical analysis, demonstrating that the proposed algorithms substantially outperform conventional (sequential) sweep methods.

Paper Structure

This paper contains 10 sections, 1 theorem, 19 equations, 5 figures, 5 algorithms.

Table of Contents

  1. Introduction
  2. Problem formulation
  3. The baseline algorithms
  4. The sweep algorithm
  5. The ${\bf \Pi}$-algorithm
  6. The Proposed algorithms
  7. The pseudo-random sweep algorithm with forward replacement
  8. The stick-together strategy
  9. Simulations
  10. Conclusion

Key Result

Theorem 2

Let $\{X_t\}_{t\ge1}$ be a sequence of Bernoulli random variables generated by a stationary, homogeneous two-state Markov chain on $\{0,1\}$ with and transition probabilities Let $T=\inf\{t \ge 1: X_t=1\}$ be the first time that a successful trial occurs. Also let be the Pearson correlation coefficient between consecutive trials. Then for fixed $p$, ${\bf\sf E}[T]$ is a strictly increasing func

Figures (5)

  • Figure 1: The multichannel topology discovery problem in a CRN.
  • Figure 2: A motivating illustration for using pseudo-random sweep.
  • Figure 3: A line graph with three nodes.
  • Figure 4: The ETTD comparison of the six algorithms.
  • Figure 5: The MTTD comparison of the six algorithms.

Theorems & Definitions (2)

  • Definition 1
  • Theorem 2