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Top1. Introduction
Cognitive Radio Network (CRN) is an emerging solution for Opportunistic Spectrum Access (OSA) of licensed RF spectrum which are exclusively assigned to particular service for a long term and vast geographic area. Recent studies in (Akyildiz, Lee, Vuran, & Mohanty, 2006) (Rawat, Amin, & Song, 2015) (Rawat, Reddy, Sharma, Bista, & Shetty, 2015) have shown that the static RF spectrum assignment results in inefficient use of the spectrum as the most portion of the spectrum remains under-utilized or idle most of the time. In CRN, unlicensed secondary users (SUs) are required to access the spectrum opportunities dynamically without creating harmful interference to licensed primary users (PUs). The SUs could use one of the two approaches (Zhao, & Sadler, 2007) (Rawat, & Popescu, 2012) (Rawat, Song, & Shetty, 2015): spectrum underlay and spectrum overlay. In spectrum underlay, PUs and SUs coexist and transmit simultaneously in the same RF band; however, the SUs are not allowed to transmit with higher powers than the pre-specified levels so as not to create harmful interference to PUs. This could prevent the long range communications for SUs in CRNs. In spectrum overlay, the SUs are required to sense the RF bands to identify idle spectrum or search for the spectrum opportunities in a spectrum database before using them for a given time and geographic location. In this approach, the SUs can transmit with high power as long as they do not exceed the mandated upper limit by government bodies such as Federal Communications Commission (FCC). For example, the FCC mandates that the Wi-Fi enabled wireless devices are not allowed to use higher transmit power than 4 watts in the U.S.
In this paper, we present algorithms for admission control to permit or stop SUs to use spectrum opportunities in a CRN and radio resource allocation for active/admitted SUs in spectrum overlay CRN using a game theoretic approach. The admission control algorithm uses the geolocations SUs and location of spectrum opportunities to make sure that the SUs are not creating any harmful interference to active PUs. Furthermore, the proposed admission control algorithm ensures there are not more SUs than the intended number to have reliable communications among already active/admitted SUs. To store the geolocation of spectrum opportunities in a database, we consider that the spectrum sensors are deployed to sense the RF spectrum bands and report the spectrum occupancy information to the spectrum servers.
Spectrum server processes the data and stores the geolocation in a geolocation database. The geolocation database is maintained in server located in the cloud, and the secondary users search the idle spectrum in the geolocation database instead of sensing the spectrum and identifying free bands by themselves (FCC, 2010a) (FCC, 2010b) (Mauri, Ghafoor, Rawat, & Perez, 2014). As spectrum sensors report the real-time status of wide range of spectrum bands to cloud based spectrum server/provider, there is a need for stream processing and continuous computation to big data in the cloud computing environment. In (Rawat, Shetty, & Raza,) (Rawat, Song, & Shetty, 2015) (Rawat, Shetty, & Raza, 2014) (Rawat, Amin, & Song, 2015), the authors have proposed a distributed computing system based on Storm programming model to improve the performance of the algorithm in the presence of streaming spectrum sensor information. Furthermore, the admission control process is carried out with the help of cloud computing for matching geolocation of SUs and spectrum opportunities in the database. Once a SU gets an admission to access idle spectrum opportunity, it uses the specified band and adapts its transmit power in distributed manner based on network condition in terms of estimated average packet error rate while satisfying the QoS requirements in terms of SINR using a game theory.
Our contributions in this paper are given as: