ABSTRACT
The future wireless systems are emerging toward ad hoc networks with uncertain topologies and the main idea in future wireless networks is to design networks that are self-configuring, self-organizing, self-optimizing, and self-protecting. Therefore, such cognitive wireless networks (CWNs) should be able to learn and adapt instantaneously to their operating environment depending on their operating radio-frequency (RF) environment, thus providing much needed flexibility and functional scalability. Moreover, in order to adapt to their operating parameters automatically and wisely, signal processing for spectrum sensing is regarded as a fundamental step in these types of networks. Currently existing wireless communication systems and networks are operating based on fixed spectrum assignment to the service providers and their users for exclusive use on a long-term basis and over a vast geographic area. The exclusive RF spectrum assignment, which is licensed by government regulatory bodies, such as the Federal Communications Commission (FCC) in the United States, was an efficient way for interference mitigation among adjacent bands. However, the fixed spectrum assignment leads to inefficient use of spectrum creating spectrum drought [1] since most of the channels actively transmit the information only for short duration while a certain portion of the spectrum is idle when and where the licensed users are not transmitting [2-5]. This implies that the inefficient radio spectrum usage has been a serious bottleneck for deployment of larger density of wireless devices. We note that the scarcity of RF spectrum is not a result of lack of spectrum but a result of wasteful static spectrum allocations. In order to alleviate the spectrum scarcity, cognitive radio (CR) for secondary user (SU) [6-13] has been introduced to facilitate the spectrum sharing [14-20] to increase the spectrum efficiency so that the larger density of wireless users can be accommodated without
5.3.3.3 Filter Bank-Based Spectrum Sensing 191 5.3.3.4 Compressive Radio Spectrum Sensing 191
5.4 Comparison of Radio Spectrum Sensing Methods 192 5.5 Conclusions 195 References 196 Biographical Sketches 199
creating a new RF spectrum band. For SUs with software-defined radios along with some intelligence to work automatically according to their operating environment, the radio components are implemented in software rather than in hardware; therefore, it is possible to adapt system configurations to any frequency to transmit and receive the data. It is important to note that the primary users (PUs) are the authorized users of the licensed frequency band, and SUs, who are not the PUs but want to use the licensed frequency band, are the cognitive users in CWNs. It is also worth mentioning that allowing an SU to access the licensed spectrum (imposing some constraints on SUs) improves the spectrum utilization. In CWNs, devices detect each other’s presence as interference and try to avoid the interference autonomously by changing their behavior accordingly. In dynamic spectrum sharing, SUs are not allowed to cause harmful interference to the incumbent PUs. It is worth noting that the SUs are essentially invisible to the PUs in CWNs; hence, possibly no changes are needed for licensed users/devices. In such a scenario, SUs can either be allowed to transmit at low power as in the ultra wideband (UWB) system or be allowed to use spectrum opportunities dynamically to transmit without causing the harmful interference to PUs. In the latter case, the CWN autonomously detects and exploits the idle spectrum where and when the PUs are not active. This helps to increase the system capacity and efficiency, and the dynamic spectral access implies that the SU be able to work in multiband, different wireless channels, and support multimedia services and/or applications.
