Application of optimization methods for resource allocation in cognitive radio-supported vehicular networks

Abstract

The highly anticipated era of vehicular communication networks which is also an integral aspect of Intelligent Transportation Systems (ITS) will undeniably improve transport safety and significantly reduce road accidents. To promote the communication of mobile vehicles, US FCC officially allocated a meagre 75 MHz spectrum in the 5.9 GHz band to enable vehicular communication. Cognitive Radio Networks (CRNs) are adaptive, intelligent and reconfigurable wireless communications systems with CR technologies capable of learning from their surroundings and deciding their operations based on the learning. The application of CR technology to vehicular networks in order to increase the spectrum resource opportunities is studied in this research. Applying CR technology to vehicular networks is crucial especially when the officially allocated 75 MHz spectrum in 5.9 GHz band is not enough due to high demands as a result of increasing number of connected vehicles as already foreseen in the near era of Internet of vehicles (IoVs), which is also known as vehicular ad hoc networks (VANETs). We proposed a novel CR Assisted Vehicular NETwork (CRAVNET) framework which empowers CR assisted vehicles to make opportunistic usage of licensed spectrum bands on the highways and developed a novel co-operative three-state spectrum sensing and allocation solution which makes CR vehicular SUs aware of additional spectrum resources opportunities on their current and future positions. Furthermore, a novel Adaptive CR Enabled Vehicular NETwork (ACRAVNET) framework is proposed to ensure high spectrum sensing efficiency and provide quality of service (QoS) support. To avoid heavy overhead usually incurred during spectrum sensing, we developed a novel CR adaptive spectrum sensing (CRASS) scheme that can reduce the spectrum sensing cost and improve sensing performance effectively. We also applied the concept of Nash Bargaining Solution (NBS) to guarantee fairness in spectral resources allocation and proposed a generalized non-symmetric NBS (GNNBS) to perform a non-symmetric cognitive inter-cell spectrum allocation in the proposed ACRAVNET framework. Both the simulation and theoretical analysis have demonstrated that our solution can significantly improve the performance of a cooperative spectrum sensing and sharing schemes and provide vehicles with additional spectrum opportunities with zero interference against the PUs activities. Additionally, the problem of joint optimal subcarrier and transmission power allocation with QoS support for enhanced packet transmission over a cognitive radio-enabled IoVs network system is also considered in this research study. To tackle the problem, a novel Symmetric Nash bargaining solution (SNBS) based wireless radio resource scheduling scheme in orthogonal frequency division multiple access (OFDMA) CR enabled IoVs network systems is proposed. The objective of the optimization model applied in this study is to maximize the overall system throughput of the CR enabled IoVs system without harmful interference to transmissions of the shared channels' licensed owners (or primary users (PUs)), guarantee the proportional fairness and minimum data-rate requirement of each CR vehicular secondary user (CRV-SU) and efficient transmission power allocation amongst CRV-SUs. To avoid the iterative processes associated with searching the optimal solution numerically through iterative programming methods, this study developed a low-complexity algorithm. Theoretical analysis and simulation results demonstrate that under similar conditions, the proposed solutions outperform the reference scheduler schemes.

Date of Award	Jul 2021
Original language	English
Awarding Institution	University of Bedfordshire
Supervisor	Sijing Zhang (Supervisor) & Enjie Liu (Second supervisor)