2676 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 9, NO. 8, AUGUST 2010 Outage Probability Analysis of Cognitive Transmissions: Impact of Spectrum Sensing Overhead Yulong Zou, Student Member, IEEE, Yu-Dong Yao, Senior Member, IEEE, and Baoyu Zheng, Member, IEEE Abstract—In cognitive radio networks, a cognitive source node requires two essential phases to complete a cognitive transmission process: the phase of spectrum sensing with a certain time duration (also referred to as spectrum sensing overhead) to detect a spectrum hole and the phase of data transmission through the detected spectrum hole. In this paper, we focus on the outage probability analysis of cognitive transmissions by considering the two phases jointly to examine the impact of spectrum sensing overhead on system performance. A closed-form expression of an overall outage probability that accounts for both the probability of no spectrum hole detected and the probability of a channel outage is derived for cognitive transmissions over Rayleigh fading channels. We further conduct an asymptotic outage analysis in high signal-to-noise ratio regions and obtain an optimal spectrum sensing overhead solution to minimize the asymptotic outage probability. Besides, numerical results show that a minimized overall outage probability can be achieved through a tradeoff in determining the time durations for the spectrum hole detection and data transmission phases. In this paper, we also investigate the use of cognitive relay to improve the outage performance of cognitive transmissions. We show that a significant improvement is achieved by the proposed cognitive relay scheme in terms of the overall outage probability. Index Terms—Cognitive radio, spectrum sensing, overhead, cognitive relay transmission, outage probability. I. I NTRODUCTION T HERE are increasing demands for the wireless radio spectrum with the emergency of many new wireless communication networks (e.g., wireless local area networks, wireless sensor networks, Bluetooth and so on). Meanwhile, according to the Federal Communications Commission (FCC), large portions of the licensed wireless spectrum resources are Manuscript received January 26, 2010; revised April 7, 2010; accepted May 28, 2010. The associate editor coordinating the review of this paper and approving it for publication was J. Olivier. Y. Zou is with the Institute of Signal Processing and Transmission, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu 210003, China, and with the Electrical and Computer Engineering Depart- ment, Stevens Institute of Technology, Hoboken, NJ 07030, USA (e-mail: zouyulong198412@126.com, yzou1@stevens.edu). Y.-D. Yao is with the Electrical and Computer Engineering Depart- ment, Stevens Institute of Technology, Hoboken, NJ 07030, USA (e-mail: yyao@stevens.edu). B. Zheng is with the Institute of Signal Processing and Transmission, Nan- jing University of Posts and Telecommunications, Nanjing, Jiangsu 210003, China (e-mail: zby@njupt.edu.cn). This work was partially supported by the Postgraduate Innovation Pro- gram of Scientific Research of Jiangsu Province (Grant Nos. CX08B 080Z, CX09B 150Z) and the National Natural Science Foundation of China (Grant No. 60972039). Digital Object Identifier 10.1109/TWC.2010.061710.100108 under utilized [1]. In order to address this issue, cognitive radio [2] has been proposed by allowing a cognitive user to access a spectrum hole (that is a frequency band licensed to a primary user but not utilized by that user at a particular time and a specific geographic location [3]), which promotes the efficient utilization of the licensed spectrum. As stated in [3], cognitive radio is an intelligent wireless communication system, which learns from its surrounding environment and adapts its internal states to statistical variations of the environment. In cognitive radio networks, a cognitive source node typi- cally requires two essential phases to complete its transmission to its destination: 1) a spectrum sensing phase (also known as a spectrum hole detection phase), in which the cognitive source attempts to detect an available spectrum hole with a certain time duration (referred to as spectrum sensing overhead), and 2) a data transmission phase, in which data is transmitted to the destination through the detected spectrum hole. The two phases have been studied individually in terms of different detection [5] - [13] or different transmission [14] - [22] techniques. In spectrum sensing, the energy detection [5], [6] and the matched filter detection [7], [8] have been proposed first and investigated extensively. It has been shown that the energy detection can not differentiate signal types, which could lead to more false detections triggered by some unintended inter- ference signals [5]. Although the matched filter is an optimal detector in stationary Gaussian noise scenario, it requires prior information of the primary user signal, such as the pulse shape, modulation type and so on [4]. As an alternative, the cyclostationary feature detector has been presented in [9], [10], which can differentiate the modulated signal from the interference and additive noise. The advantage of cyclosta- tionary detection comes at the expense of high computational complexities since it requires an extra training process to extract significant features. Meanwhile, in order to combat fading effects, a collaborative spectrum sensing approach has been proposed [11], where the detection results from multiple cognitive users are pooled together at a fusion center by using a logic rule. Recently, in [12], [13], the authors have applied cooperative diversity [16], [17] to the detection of the primary user and shown that the detection time can be reduced greatly through the cooperation between the cognitive users. In the wireless transmission research, a large number of studies are motivated to combat the large-scale and small- scale fading. Two wireless transmission technologies, i.e., the 1536-1276/10$25.00 c ⃝ 2010 IEEE