Cooperation Improves Delay in Cognitive Networks With Hybrid Random Walk

In this paper, we study the capacity and delay scaling laws of cognitive radio networks (CRN) with static primary nodes (PNs) and mobile secondary nodes (SNs). The primary network consists of randomly distributed primary nodes of density n, which have a higher priority to access the spectrum. The secondary network consists of randomly distributed secondary nodes of density m = n^β, where β represents the density relationship in CRN. Secondary nodes move according to hybrid random walk models with parameter α (0 ≤ α < 1/2), and the step size of the mobility model is Θ(m^-2α). Motivated by observation that the performance of CRN can benefit from the cooperation among primary nodes and secondary nodes, we propose a novel cooperative scheduling mechanism to fully utilize the mobility and geographic information of secondary nodes to enhance the performance of the primary network. For both networks, the delay performance varies with α. We show that the delay performance of primary network can be significantly improved from O(n/log n) [16] to Θ(n^β/3log n) when β < 3 for an optimal value of α, while a near-optimal throughput of Θ(1/log n) is obtained. Furthermore, the secondary network can still achieve the same throughput and delay scaling laws as a stand-alone network simultaneously.