Joint Topology Control and Stable Routing Based on PU Prediction for Multihop Mobile Cognitive Networks

Abstract

Link stability significantly suffers from dynamical primary user activities and random node movement in mobile cognitive networks (MCNs). In multihop MCNs, it will become even worse due to potential interference among multiple links so that stable routing will become more important and also more challenging than that in traditional wireless networks. In this paper, we first formulate the joint topology control and stable routing (JTCSR) problem based on primary user (PU) activity prediction. Then, we propose a PU prediction model to reveal channel utilization patterns of PUs. Next, we propose a novel routing metric PU prediction-based stability metric (PPSM), which quantitatively measures PU activities and node mobility, and design a min-max PPSM matrix construction algorithm. Finally, we propose and develop a PU prediction-based (PP) JTCSR algorithm for maximizing network throughput, which can find out the most stable and the shortest path. Theoretical analysis validates the effectiveness and efficiency of our approach. NS2-based simulation results further demonstrate that our PP-JTCSR can generate stable and efficient topology through predicting PU activities quantitatively, and outperforms related proposals in terms of path stability, average throughput, and packet loss rate.