HBLP: A Hybrid Underlay-Interweave Mode CRN for the Future 5G-Based Internet of Things

Abstract

Enhancing spectrum utilization efficiency (SUE) to accommodate the multitude of 5G-based IoT devices within the available scarce spectrum has been a pivotal point of research in the current decade. Equipped with interweave and underlay modes, cognitive radio networks (CRNs) are envisioned to be the most promising technology for SUE enhancement. Since the 5G-based IoT is swiftly transforming into a heterogeneous network, hybrid underlay-interweave mode CRNs appear to be the optimal key for SUE enhancement. Besides enhancing SUE, ensuring fairness among secondary users (SUs) to equitably utilize the network’s resources in the wake of primary users’ (PUs) service emergence has been a critical problem in CRNs. Considering the importance of SUE enhancement, in this paper, we investigate two problems. Firstly, we precisely analyze, in a novel way, the performance of hybrid underlay-interweave mode of CRNs for SUE enhancement from the standpoint of SUs. For this purpose, we propose a hybrid underlay-interweave mode enabled CRN scheme and apply it on a legacy CRN. We model the proposed scheme with continuous time Markov chain and derive closed form expressions for various SUE-related quality of service parameters. The analysis is conducted under dynamic reservation of channels for interrupted users, considering the effects of multi-levels network traffic loads and varying channel failure rates. Secondly, we propose a multi-attribute based fairness driven algorithm for determination and interruption of SUs’ services to ensure fairness among services in the network’s resources utilization. We evaluate our proposed scheme under both perfect and imperfect spectrum sensing scenarios. The obtained results demonstrate that, as compared to the state-of-the-art, the proposed scheme significantly enhances SUE while the proposed algorithm achieves a noticeable fairness among SUs’ services.