Spectrum Sensing Methodologies for Cognitive Radio Systems: A Review <s> II. COMPLEXITY OF SPECTRUM SENSING CONCEPT <s> Cognitive radio is viewed as a novel approach for improving the utilization of a precious natural resource: the radio electromagnetic spectrum. The cognitive radio, built on a software-defined radio, is defined as an intelligent wireless communication system that is aware of its environment and uses the methodology of understanding-by-building to learn from the environment and adapt to statistical variations in the input stimuli, with two primary objectives in mind: /spl middot/ highly reliable communication whenever and wherever needed; /spl middot/ efficient utilization of the radio spectrum. Following the discussion of interference temperature as a new metric for the quantification and management of interference, the paper addresses three fundamental cognitive tasks. 1) Radio-scene analysis. 2) Channel-state estimation and predictive modeling. 3) Transmit-power control and dynamic spectrum management. This work also discusses the emergent behavior of cognitive radio. <s> BIB001 </s> Spectrum Sensing Methodologies for Cognitive Radio Systems: A Review <s> II. COMPLEXITY OF SPECTRUM SENSING CONCEPT <s> The spectrum sensing problem has gained new aspects with cognitive radio and opportunistic spectrum access concepts. It is one of the most challenging issues in cognitive radio systems. In this paper, a survey of spectrum sensing methodologies for cognitive radio is presented. Various aspects of spectrum sensing problem are studied from a cognitive radio perspective and multi-dimensional spectrum sensing concept is introduced. Challenges associated with spectrum sensing are given and enabling spectrum sensing methods are reviewed. The paper explains the cooperative sensing concept and its various forms. External sensing algorithms and other alternative sensing methods are discussed. Furthermore, statistical modeling of network traffic and utilization of these models for prediction of primary user behavior is studied. Finally, sensing features of some current wireless standards are given. <s> BIB002 </s> Spectrum Sensing Methodologies for Cognitive Radio Systems: A Review <s> II. COMPLEXITY OF SPECTRUM SENSING CONCEPT <s> A cooperative approach to the sensing task of wireless cognitive radio (CR) networks is introduced based on a basis expansion model of the power spectral density (PSD) map in space and frequency. Joint estimation of the model parameters enables identification of the (un)used frequency bands at arbitrary locations, and thus facilitates spatial frequency reuse. The novel scheme capitalizes on two forms of sparsity: the first one introduced by the narrow-band nature of transmit-PSDs relative to the broad swaths of usable spectrum; and the second one emerging from sparsely located active radios in the operational space. An estimator of the model coefficients is developed based on the Lasso algorithm to exploit these forms of sparsity and reveal the unknown positions of transmitting CRs. The resultant scheme can be implemented via distributed online iterations, which solve quadratic programs locally (one per radio), and are adaptive to changes in the system. Simulations corroborate that exploiting sparsity in CR sensing reduces spatial and frequency spectrum leakage by 15 dB relative to least-squares (LS) alternatives. <s> BIB003 </s> Spectrum Sensing Methodologies for Cognitive Radio Systems: A Review <s> II. COMPLEXITY OF SPECTRUM SENSING CONCEPT <s> Spectrum sensing is a key function of cognitive radio to prevent the harmful interference with licensed users and identify the available spectrum for improving the spectrum's utilization. However, detection performance in practice is often compromised with multipath fading, shadowing and receiver uncertainty issues. To mitigate the impact of these issues, cooperative spectrum sensing has been shown to be an effective method to improve the detection performance by exploiting spatial diversity. While cooperative gain such as improved detection performance and relaxed sensitivity requirement can be obtained, cooperative sensing can incur cooperation overhead. The overhead refers to any extra sensing time, delay, energy, and operations devoted to cooperative sensing and any performance degradation caused by cooperative sensing. In this paper, the state-of-the-art survey of cooperative sensing is provided to address the issues of cooperation method, cooperative gain, and cooperation overhead. Specifically, the cooperation method is analyzed by the fundamental components called the elements of cooperative sensing, including cooperation models, sensing techniques, hypothesis testing, data fusion, control channel and reporting, user selection, and knowledge base. Moreover, the impacting factors of achievable cooperative gain and incurred cooperation overhead are presented. The factors under consideration include sensing time and delay, channel impairments, energy efficiency, cooperation efficiency, mobility, security, and wideband sensing issues. The open research challenges related to each issue in cooperative sensing are also discussed. <s> BIB004
Spectrum opportunity is conventionally defined in literature as "a band of frequencies that are not used by a primary user of that band at a particular time and specific geographic location," BIB001 . This definition therefore introduces multi-dimensional spectrum awareness, since a spectrum hole is a function of frequency, time and geolocation. Since noise is present all the time in the entire radio spectrum, then an empty frequency bin doesn't exist. BIB003 Therefore it is important to be able to differentiate a band occupied by a primary user signal (PU) and the one from a spectrum hole that contains noise only signal. The traditional definition of spectrum sensing only exploits the three dimensions of the spectrum space. These are frequency, time and geo-location. Traditional methods usually relate to sensing the spectrum using these three BIB002 . However there are other dimensions that can be exploited for further spectrum opportunity. For example the code dimension of the spectrum space has not been extensively explored in details in literature therefore the traditional spectrum sensing algorithms find it challenging to deal with signals that makes use of spread spectrum, time or frequency hopping codes. As a result, this type of signals causes a lot of challenges in spectrum sensing as discussed in the later part of this paper. Also the angle dimension is another area which is coming up as there are recent advances in multi-antenna technologies such as beam forming, multiple users can be multiplexed into the same channel at the same time in the same geo-location. Hence, in angle dimension, a primary user and a secondary user can be in the same geo-location and share the same channel. Spectrum sensing should include the process of recognizing occupancy in all dimensions of spectrum space and find spectrum holes. For instance, a certain frequency can be occupied for a given time, but may be empty in another time. Hence, a temporal dimension is as important as frequency dimension. The idle periods between bursty transmissions of local area network (WLAN) signals are exploited for opportunistic usage BIB004 . As a result of this requirement, advanced spectrum sensing algorithms that offers spectrum awareness in multiple dimensions can be developed. This involves the availability of a specific part of the spectrum in time. In other words, the band is not continuously used. Hence there would be times where it would be available for opportunistic usage.