Spectrum Prediction in Cognitive Radio Networks Xiaoshuang Xing 1 ,Tao Jing 1 ,Wei Cheng 2 Yan Huo 1 , Xiuzhen Cheng 3 1 School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing, China 2 Department of Computer Science, University of Massachusetts Lowell, Massachusetts, USA 3 Department of Computer Science, The George Washington University, Washington DC, USA E-mail: {10120170,tjing}@bjtu.edu.cn, wcheng@cs.uml.edu, yhuo@bjtu.edu.cn, cheng@gwu.edu Abstract—Spectrum sensing, spectrum decision, spectrum sharing, and spectrum mobility are four major functions of cognitive radio (CR) systems. Spectrum sensing is utilized to identify primary users’ spectrum occupancy status, based on which CR users can dynamically access the available channels through the regulation processes of spectrum decision, spectrum sharing, and spectrum mobility. To alleviate the processing delays involved in these four functions and to improve the efficiency of spectrum utilization, spectrum prediction has been extensively studied in literature. This article surveys the state-of-the-art of spectrum prediction in CR networks. We summarize the major spectrum prediction techniques, illustrate their applications, and present the relevant open research challenges. I. I NTRODUCTION Currently, the use of the wireless frequencies is mainly regulated by centralized authorities (Federal Communications Commission (FCC) in the US) that allocate the spectrum statically in temporal and spatial dimensions such that the spectrum band assigned to each user is valid for an extended period of time (usually decades) and for a large geographical region (country wide). An illustration of this static spectrum assignment policy is presented in Fig. 1(a). Obviously, large portions of the spectrum remain temporally and/or spatially under-utilized/unused. But due to the proliferation of mobile devices in recent years, the demand on bandwidth continues to increase, making dynamic spectrum access a better choice for managing the spectrum resource. Cognitive Radio (CR), which provides the capability to harness the potential of unused/underutilized spectrum (spec- trum holes) in an opportunistic manner, is a key enabling technology for dynamic spectrum access. An illustration of the cognitive radio technology is presented in Fig. 1(b), from which it is easy to observe that CR can significantly improve the overall spectrum utilization when the CR users are allowed to utilize the spectrum holes. A cognitive radio network typically involves two types of users: primary users (PUs), who are incumbent licensed users of the spectrum, and CR users (also known as secondary users), who try to opportunistically access the unused licensed spectrum as long as the harmful interference to primary users is limited. To effectively implement the concept of cognitive radio networking, CR systems need the capability to perform the following functions [1]: spectrum sensing, spectrum deci- sion, spectrum sharing, and spectrum mobility. In spectrum sensing, CR users sense the PU spectrum occupancy status and recognize the spectrum holes in the licensed bands that (a) Illustration of the static spectrum assignment policy (b) Illustration of the cognitive radio technology Fig. 1. Static spectrum assignment policy and Cognitive radio technology can be used for their own communications. Based on the sensing results, CR users determine which spectrum band to use (spectrum decision), how to share the spectrum with other CR users (spectrum sharing), and when to evacuate the current spectrum band for the returned PUs (spectrum mobility). Considering the fact that all these four functions introduce time delays that undermine the spectrum sensing accuracy as well as the spectrum utilization efficiency of CR systems, and PU activities exhibit regularity in both the time and spatial domains, spectrum prediction has been proposed. Prediction in cognitive radio networks is a challenging prob- lem that involves several subtopics such as channel status pre- diction, PU activity prediction, radio environment prediction, and transmission rate prediction. In this article, we present an overview on the most important prediction techniques in