A distributed solution for cooperative spectrum sensing scheduling in multi-band cognitive radio networks. (15th September 2017)

• Record Type: Journal Article
• Authors: Nath, Arijit; Sarma, Nityananda
• Is Part Of: Journal of network and computer applications. Volume 94(2017)
• Page Start: 69
• Page End: 77
• Publication Date: 2017-09-15
• Subjects: Cognitive Radio (CR) -- Cooperative Sensing Scheduling (CSS) -- Primary User (PU) -- Secondary User (SU) -- Coalitional Game Theory; Microcomputers -- Periodicals; Computer networks -- Periodicals; Application software -- Periodicals; Micro-ordinateurs -- Périodiques; Réseaux d'ordinateurs -- Périodiques; Logiciels d'application -- Périodiques; Application software; Computer networks; Microcomputers; Periodicals; 004.05; 004
• DOI: 10.1016/j.jnca.2017.07.003
• Languages: English
• ISSNs: 1084-8045

Abstract

Abstract: Cognitive Radio (CR) is a promising technology that overcomes the problem of spectrum scarcity and underutilization in an efficient manner. Spectrum sensing is one of the most important functionalities of a Cognitive Radio Network (CRN). Cooperative sensing where multiple sensors cooperatively sense the spectrum proves to be the most efficient way of sensing. However, one of the main challenges in a CRN is how to appropriately schedule the Secondary Users (SU) to sense the potential Primary User (PU) channels cooperatively so that the throughput obtained in the network is optimum. At the same time, in order to protect the quality of service of the PU channels, a minimum sensing performance criteria in terms of probability of detection and false alarm must be satisfied. It can be referred to as Cooperative Sensing Scheduling (CSS) problem. The problem is formulated as a non-linear binary optimization problem that has a high computational complexity. In this paper, the CSS problem is reformulated as an equivalent Integer Linear Programming (ILP) problem which is known to be NP-Complete. A distributed algorithm using coalitional game theoretic approach is proposed that provides near optimal solutions. Numerical results are provided to evaluate the performance of the proposed algorithm and compared with the theoretically optimal results.