Software-defined routing protocol for mobile cognitive radio networks : a cross-layer perspective

The growing demand for wireless applications, combined with inefficient spectrum use, necessitates developing a new wireless communication paradigm that focuses on dynamic spectrum access rather than the fixed spectrum using cognitive radio technology. The unlicensed user, known as secondary user or cognitive user, uses cognitive radio technology to grow opportunistic communication over licensed spectrum bands and improve spectrum management performance. The routing protocol in Cognitive Radio Networks (CRNs) serves as a communication backbone, allowing data packets to transfer between cognitive user nodes through multiple paths and channels. However, the problem of routing in CRNs is to create a robust-stable route over higher channel availability. The previously developed protocols missed opportunities to exploit the time-variant channel estimation technique, which selects the best route using the cross-layer routing decision engine to track the adverse impact of cognitive user mobility and primary user activity. This study aims to construct a robust routing path while limiting interference with primary user activity, delaying routing, and maximizing routing throughput. Here, a new routing framework is created in this study to explore new extended routing functions and features from the lower layers (Physical layer and Data Link layer) feedback to improve routing performance. Then, the link-oriented channel availability and channel quality have been developed based on two reliable metrics, which are channel availability probability and channel quality, to estimate and select a channel that maximizes link-throughput. Furthermore, this study proposes a novel cross layer routing protocol, namely, the Software-Defined Routing Protocol. It is a cross-layer method to combine the lower layer (Physical layer and Data Link layer) sensing derived from the channel estimation model. It periodically updates the routing table for optimal route decision making. The output simulation of the channel estimation method has shown that it has produced a powerful channel selection strategy to maximize the average rate of link throughput and achieved a channel estimate under the time-variant effect. Extensive simulation experiments have been performed to evaluate the proposed protocol in compression with the existing benchmark protocols, namely, dual diversity cognitive Ad-hoc routing protocol and cognitive Ad-hoc on-demand distance vector. The proposed protocol outperforms the benchmarks, resulting in increasing the packet delivery ratio by around (11.89%-12.80%), reducing delay by around (2.74%-4.05%), reducing overhead by around (14.31%-18.36%), and increasing throughput by around (23.94%-28.35%). The software-defined routing protocol, however, lacks the ability to determine the better idle channel at high-speed node mobility. In conclusion, the cross-layer routing protocol successfully achieves high routing performance in finding a robust route, selecting high channel stability, and reducing the probability of interference with primary users for continued communication