On the impact of primary traffic correlation in TV White Space☆
Introduction
Nowadays, regulators worldwide are beginning to allow unlicensed access to unused segments of TV spectrum, known as TV White Space (TVWS) [1]. Secondary users (SUs) can access to the TVWS only if harmful interference on the primary users (PUs) is avoided. To this aim, the general consensus among FCC, Ofcom and ECC is on obviating the spectrum sensing [2], [3], [4] as the mechanism for the SUs to recognize and exploit portions of the TVWS spectrum whenever they are vacated by the licensed users. Instead, they require the SUs to periodically access to a geolocated database service [5], [6], [7], known as White Space DataBase (WSDB). Specifically, any SU must acquire the spectrum availability by accessing to a WSDB with a fixed timeframe. Within such a timeframe, the SU can freely access to the WSDB on-demand to update the spectrum availability information, but the specifics of the on-demand access are not detailed by the standards. The choice of whether or not to update the spectrum availability information through the on-demand access affects the overall performance of any secondary network. In fact, whenever the SU accesses to the WSDB, it can acquire some new knowledge on the current PU activities over the different channels. Hence, the more frequent are the on-demand accesses, the better the SU can exploit such availabilities to increase its communication opportunities. On the other hand, the more frequent are the on-demand accesses, the higher is the induced overhead.
Despite its importance, the on-demand database access issue in TVWS is still largely unexplored, since current research focuses on security issues [8], spectrum leasing [9], local sensing [10], or video streaming [11]. In [12] some preliminary results are obtained by assuming the PU traffic modeled as a Bernoulli process. Such an assumption is simplistic in TV scenarios, since the TV signal patterns are correlated [13], [14], as confirmed by Fig. 1 reporting the PU activity experimentally measured [14] over a time interval equal to the timeframe between two mandatory database accesses as specified by FCC rulings [5], i.e., 24 h.
Hence, in the following, we investigate the on-demand database access by modeling the correlation among the PU traffic patterns through a two-state Markov process. As shown in Fig. 1, such a model is able to effectively describe the typical TVWS traffic patterns by properly setting the transition probabilities according to experimental measurements [14]. Specifically, the objective of this work is to determine whenever it is convenient for an SU to access to the WSDB on-demand in presence of correlated PU activity. This problem is not trivial, since the PU traffic correlation greatly complicates any theoretical analysis. Through the manuscript, we first model the WSDB accesses through the general notions of reward and cost. Then, by modeling the correlation among the PU traffic activities through a Markov process, we prove that the choice of the on-demand access maximizing the total reward available to the SU can be formulated as a Markov Decision Process. Furthermore, closed-form expressions for the decision transition probabilities are derived. Stemming from these results, we design a computational-efficient algorithm allowing any SU to a-priori establish whenever an on-demand access should be performed. Finally, we validate the analysis through numerical simulations.
To the best of our knowledge, this is the first work investigating the on-demand database access for TVWSs in presence of correlation among the PU traffic patterns.
The rest of the paper is organized as follows. In Section 2, we describe the network model along with some preliminaries. In Section 3, we design the optimal strategy, whereas in Section 4 we validate the analytical framework through numerical simulations. Finally, in Section 5, we conclude the paper.
Section snippets
Network model and preliminaries
In this section, we first describe the system model, and then we collect several definitions that will be used through the paper.
We consider a secondary network operating within the TVWS spectrum according to current regulations [5], [6], [7] and standards [15]. The SU time is organized into1 L slots of duration T, with K T denoting the duration of a database access
Optimal database access strategy
At first, in Section 3.1, we formulate the optimal strategy problem as a Markov Decision Process, and we prove there exists an optimal strategy with the attractive property of being deterministic. Then, in Section 3.2, we design a computational-efficient algorithm for finding such a strategy.
Performance evaluation
In this section, we validate the theoretical results derived in Section 3 by simulating a secondary network operating within the TVWS in urban scenarios, i.e., for small values of M [13].
In the first experiment, we compare the performance of the proposed optimal strategy (Algorithm 1) with those obtained through different database access strategies. More specifically, Fig. 2 presents the expected reward given in (4) as a function of the discrete time. The adopted simulation set is as follows:
Conclusions
In TVWS, an SU can access on-demand to a geolocated database to update the spectrum availability information. The more frequent are the on-demand accesses, the higher can be the communication opportunities available to the SU but the higher is the induced overhead. For this, in this manuscript, the on-demand access is investigated to a-priori determine whether to access or not by accounting for the correlation exhibited by primary traffic patterns. Specifically, is proved that the on-demand
Angela Sara Cacciapuoti received the Dr. Eng. degree with the highest honors (summa cum laude) in telecommunications engineering in 2005, and the Ph.D degree with the highest honors (excellent) in electronic and telecommunications engineering in 2009, both from University of Naples Federico II. Currently, she is an assistant professor with the Department of Electrical Engineering and Information Technologies (DIETI), University of Naples Federico II. She is also an area editor of Computer
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Angela Sara Cacciapuoti received the Dr. Eng. degree with the highest honors (summa cum laude) in telecommunications engineering in 2005, and the Ph.D degree with the highest honors (excellent) in electronic and telecommunications engineering in 2009, both from University of Naples Federico II. Currently, she is an assistant professor with the Department of Electrical Engineering and Information Technologies (DIETI), University of Naples Federico II. She is also an area editor of Computer Networks (Elsevier) Journal. From 2010 to 2011, she has been with the Broadband Wireless Networking Laboratory, Georgia Institute of Technology, as visiting researcher. In 2011, she has also been with the NaNoNetworking Center in Catalunya (N3Cat), Universitat Politècnica de Catalunya (UPC), as visiting researcher. Her current research interests are in cognitive radio networks and nanonetworks.
Marcello Caleffi received the Dr. Eng. degree with the highest grade (summa cum laude) in computer science engineering from the University of Lecce, Lecce, Italy, in 2005, and the Ph.D. degree in electronic and telecommunications engineering from the University of Naples Federico II, Naples, Italy, in 2009. Currently, he is an assistant professor with the Department of Electrical Engineering and Information Technologies, University of Naples Federico II. From 2010 to 2011, he was with the Broadband Wireless Networking Laboratory, Georgia Institute of Technology, Atlanta, GA, USA, as a visiting researcher. In 2011, he was also with the NaNoNetworking Center in Catalunya (N3Cat), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain, as a visiting researcher. He serves as area editor for Elsevier Ad Hoc Networks. His research interests are in cognitive radio networks and biological networks.
Luigi Paura received the Dr. Eng. degree (summa cum laude) in electronic engineering from the University of Napoli Federico II, Naples, Italy, in 1974. From 1979 to 1984, he was with the Department of Biomedical, Electronic and Telecommunications Engineering, University of Naples Federico II, first as an assistant professor and then as an associate professor. Since 1994, he has been a full professor of telecommunications: first, with the Department of Mathematics, University of Lecce, Lecce, Italy; then, with the Department of Information Engineering, Second University of Naples, Naples, Italy; and, finally, since 1998 he has been with the Department of Electrical Engineering and Information Technologies, University of Naples Federico II. He also held teaching positions with the University of Salerno, Salerno, Italy; University of Sannio, Benevento, Italy; and University Parthenope of Naples, Naples, Italy. In 1985–1986 and in 1991, he was a visiting researcher with the Signal and Image Processing Lab, University of California, Davis, CA, USA. His research interests are mainly in digital communication systems and cognitive radio networks.
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This work has been partially supported by the Italian PON projects SIRIO, FERSAT and CHIS, and by the Campania POR project myOpenGov.