With the rapid growth of wireless applications and services in the recent decade, spectrum resources are facing huge demands. The radio spectrum is a limited resource and is regulated by government agencies such as the Federal Communications Commission (FCC) in the United States. Within the current spectrum regulatory framework, all of the frequency bands are exclusively allocated to specific services and no violation from unlicensed users is allowed. The spectrum scarcity problem is getting worse due to the emergence of new wireless services. Fortunately, the worries about spectrum scarcity are being shattered by a recent survey made by a Spectrum Policy Task Force (SPTF) within FCC. It indicates that the actual licensed spectrum is largely under-utilized in vast temporal and geographic dimensions . For instance, a field spectrum measurement, which is taken in New York City, has shown that the maximum total spectrum occupancy is only 13.1% from 30 MHz to 3 GHz . The exciting findings shed light on the problem of spectrum scarcity and motivate a new direction to solve the conflicts between spectrum scarcity and spectrum under-utilization. A remedy to spectrum scarcity is to improve spectrum utilization by allowing secondary users to access under-utilized licensed bands dynamically when/where licensed users are absent. Recently, FCC has issued a Notice of Proposed Rule Making (NPRM-FCC 03-322 ) advocating cognitive radio technology as a candidate to implement opportunistic spectrum sharing. Meanwhile, IEEE has also endeavored to formulate a novel wireless air interface standard based on cognitive radios: the IEEE 802.22 working group. The IEEE 802.22 WG aims to develop wireless regional area network physical (PHY) and medium access control (MAC) layers for use by unlicensed devices in the spectrum allocated to TV bands . Cognitive radio is a novel technology which improves the spectrum utilization by allowing secondary networks (users) to borrow unused radio spectrum from primary licensed networks (users) or to share the spectrum with the primary networks (users) [5-7]. As an intelligent wireless communication system, cognitive radio is aware of the radio frequency environment, selects the communication parameters (such as carrier frequency, bandwidth and transmission power) to optimize the spectrum usage and adapts its transmission and reception accordingly. One of most critical components of cognitive radio technology is spectrum sensing. By sensing and adapting to the environment, a cognitive radio is able to fill in spectrum holes and serve its users without causing harmful interference to the licensed user. To do so, the cognitive radio must continuously sense the spectrum it is using in order to detect the re-appearance of the primary user. Once the primary user is detected, the cognitive radio should withdraw from the spectrum instantly so as to minimize the interference it may possibly incur. This is a very difficult task as the various primary users will be employing different modulation schemes, data rates and transmission powers in the presence of variable propagation environments and interference generated by other secondary users. Another great challenge of implementing spectrum sensing is the hidden terminal problem, which occurs when the cognitive radio is shadowed, in severe multipath fading or inside buildings with high penetration loss while a primary user is operating in the vicinity . Due to the hidden terminal problem, a cognitive radio fails to see the presence of the primary user and then will access the licensed channel and cause interference to the licensed users. In order to deal with the hidden terminal problem in cognitive radio networks, multiple cognitive users can cooperate to conduct spectrum sensing. Cooperative communications has been recently recognized as a powerful solution that can overcome the limitation of wireless systems . The basic idea behind cooperative transmission rests on the observation that in a wireless environment, the signal transmitted or broadcast by a source to a destination node, each employing a single antenna, is also received by other terminals, which are often referred to as relays or partners. The relays process and retransmit the signals they receive. The destination node then combines the signals coming from the source and the partners, thereby creating spatial diversity and taking advantage of the multiple receptions of the same data at the various terminals and transmission paths. In addition, the interference among terminals can be dramatically suppressed by distributed spatial processing technology. By allowing multiple cognitive radios to cooperate in spectrum sensing, the hidden terminal problem can be addressed [10, 11]. Cooperative spectrum sensing in cognitive radio networks has an analogy to a distributed decision in wireless sensor networks, where each sensor makes a local decision and those decision results are reported to a fusion center to give a final decision according to some fusion rule . The main difference between these two applications lies in the wireless environment. Compared to wireless sensor networks, cognitive radios and the fusion center (or common receiver) are distributed over a larger geographic area. This difference brings out a much more challenging problem to cooperative spectrum sensing because sensing channels (from the primary user to cognitive radios) and reporting channels (from cognitive radios to the fusion center or common receiver) are normally subject to fading or heavy shadowing. In this chapter, a survey of cooperative spectrum sensing for cognitive radios is given. We shall also review some well-known spectrum sensing techniques and introduce the concept and principle of cooperative spectrum sensing. The performance analysis of cooperative spectrum sensing over realistic fading channels is given. Several robust cooperative spectrum sensing techniques are are also proposed.
ASJC Scopus subject areas