A Spectrum Handoff Scheme for Optimal Network Selection in NEMO Based Cognitive Radio Vehicular Networks

Abstract

When a mobile network changes its point of attachments in Cognitive Radio (CR) vehicular networks, the Mobile Router (MR) requires spectrum handoff. Network Mobility (NEMO) in CR vehicular networks is concerned with the management of this movement. In future NEMO based CR vehicular networks deployment, multiple radio access networks may coexist in the overlapping areas having different characteristics in terms of multiple attributes. The CR vehicular node may have the capability to make call for two or more types of nonsafety services such as voice, video, and best effort simultaneously. Hence, it becomes difficult for MR to select optimal network for the spectrum handoff. This can be done by performing spectrum handoff using Multiple Attributes Decision Making (MADM) methods which is the objective of the paper. The MADM methods such as grey relational analysis and cost based methods are used. The application of MADM methods provides wider and optimum choice among the available networks with quality of service. Numerical results reveal that the proposed scheme is effective for spectrum handoff decision for optimal network selection with reduced complexity in NEMO based CR vehicular networks.

Data Dissemination Based on Fuzzy Logic and Network Coding in Vehicular Networks

“…However, the quality enhancement of wireless communications in light of MAC and physical layers could further improve the performance of data dissemination schemes [36]. Furthermore, to model the transmission ability of vehicles thoroughly in theory will be a challenging but significant attempt in the future.…”

A Distributed Collaborative Game-Theoretic Approach in Cognitive Satellite Communication Networks

“…is the action profile of all the cognitive users excluding n, s n and p n indicate the channel selection and power control strategy of cognitive user n, respectively. In equation (5), the first term indicates the negative reciprocal of SINR for cognitive user n, i∈Fn(sn) (p n g ni /p i g ii ) is the penalty term, which represents the weighted interference of cognitive user n to other cognitive users, the weight factor is p i g ii (i ∈ F n (s n )).…”

“…The difference is that the Maximum power algorithm 2 does not consider the cooperation between users and directly optimizes the throughput. In contrast, the Maximum power algorithm 1 considers the cooperation between users, maximize the utility function (5) presented in this paper. The step size δ for the JIC and the JICEE is set to 0.5, and the number of power control strategies for the JID and the JIDEE is set to 6.…”

“…Spectrum sharing between them has become a promising option. As a spectrum sharing technology, cognitive radio has been extensively investigated in many fields, such as device-to-device communication [3], vehicular communication [4], unmanned aerial vehicle (UAV) communication [5], and wireless sensor network [6]. However, cognitive satellite communications have many inherent characteristics, such as considerable propagation delay, wide beam coverage, cumulative uplink interference, sensitivity to interference, and so on [7].…”

Intelligent Process of Spectrum Handoff/Mobility in Cognitive Radio Networks

“…In [13], the author presented the appropriate solutions and needs for spectrum mobility in CRNs. In [14], the authors proposed an SH procedure in which the ideal spectral band was chosen on the basis of multiplexing criteria, taking into account the probability of the presence of PU, time of spectrum availability, and time of transmission. But the probability relative to the arrival of PU on the channel was not mentioned.…”