Advances in cognitive radio networks: A survey <s> 4) Other Techniques: <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> Advances in cognitive radio networks: A survey <s> 4) Other Techniques: <s> Sensing (signal detection) is a fundamental problem in cognitive radio. The statistical covariances of signal and noise are usually different. In this paper, this property is used to differentiate signal from noise. The sample covariance matrix of the received signal is computed and transformed based on the receiving filter. Then two detection methods are proposed based on the transformed sample covariance matrix. One is the covariance absolute value (CAV) detection and the other is the covariance Frobenius norm (CFN) detection. Theoretical analysis and threshold setting for the algorithms are discussed. Both methods do not need any information of the signal, the channel and noise power as a priori. Simulations based on captured ATSC DTV signals are presented to verify the methods. <s> BIB002 </s> Advances in cognitive radio networks: A survey <s> 4) Other Techniques: <s> Sensing (signal detection) is a fundamental problem in cognitive radio. In this paper, a new method is proposed based on the eigenvalues of the covariance matrix of the received signal. It is shown that the ratio of the maximum eigenvalue to the minimum eigenvalue can be used to detect the signal existence. Based on some latest random matrix theories (RMT), we can quantize the ratio and find the threshold. The probability of false alarm is also found by using the RMT. The proposed method overcomes the noise uncertainty difficulty while keeps the advantages of the energy detection. The method can be used for various sensing applications without knowledge of the signal, the channel and noise power. Simulations based on randomly generated signals and captured ATSC DTV signals are presented to verify the methods. <s> BIB003 </s> Advances in cognitive radio networks: A survey <s> 4) Other Techniques: <s> The primary task in any cognitive radio (CR) network is to dynamically explore the radio spectrum and reliably determine portion(s) of the frequency band that may be used for the communication link(s). Accordingly, each CR node in the network has to be equipped with a spectrum analyzer. In this paper, we propose filter banks as a tool for spectrum sensing in CR systems. Various choices of filter banks are suggested and their performance are evaluated theoretically and through numerical examples. Moreover, the proposed spectrum analyzer is contrasted with the Thomson's multitaper (MT) method - a method that in the recent literature has been recognized as the best choice for spectrum sensing in CR systems. A novel derivation of the MT method that facilitates our comparisons as well as reveals an important aspect of the MT method that has been less emphasized in the recent literature is also presented. <s> BIB004 </s> Advances in cognitive radio networks: A survey <s> 4) Other Techniques: <s> Quickest detection is applied to frequency spectrum sensing in cognitive radio systems. Distribution change in frequency domain is detected for vacating secondary radio networks from licensed frequency band. A successive refinement based quickest detection is proposed to tackle the problem of unknown parameters of primary radio signal. Cooperative quickest detection is used to enhance the performance of quickest spectrum sensing in secondary radio systems without data fusion centers. Performance is evaluated using theoretical analysis and numerical simulations. <s> BIB005 </s> Advances in cognitive radio networks: A survey <s> 4) Other Techniques: <s> We address the problem of rapidly discovering spectrum opportunities for seamless service provisioning for secondary users (SUs) in cognitive radio networks (CRNs). Specifically, we propose an efficient sensing-sequence that incurs a small opportunity-discovery delay by considering (1) the probability that a spectrum band (or a channel) may be available at the time of sensing, (2) the duration of sensing on a channel, and (3) the channel capacity. We derive the optimal sensing-sequence for channels with homogeneous capacities, and a suboptimal sequence for channels with heterogeneous capacities for which the problem of finding the optimal sensing-sequence is shown to be NP-hard. To support the proposed sensing-sequence, we also propose a channel-management strategy that optimally selects and updates the list of backup channels. A hybrid of maximum likelihood (ML) and Bayesian inference is also introduced for flexible estimation of ON/OFF channel-usage patterns and prediction of channel availability when sensing produces infrequent samples. The proposed schemes are evaluated via in-depth simulation. For the scenarios we considered, the proposed suboptimal sequence is shown to achieve close-to-optimal performance, reducing the opportunity-discovery delay by up to 47% over an existing probability-based sequence. The hybrid estimation strategy is also shown to outperform the ML-only strategy by reducing the overall opportunity-discovery delay by up to 34%. <s> BIB006 </s> Advances in cognitive radio networks: A survey <s> 4) Other Techniques: <s> Available spectrum for wireless communications is a limited resource which gains importance with the increasing demand for mobile communication services with high data rates. Measurements show that assigned frequency bands (FBs) are not used efficiently. One approach for increasing the efficiency in spectrum use is the concept of overlay systems, which can be seen as an enabling technology for cognitive radio and dynamic spectrum access by providing frequency agility. In this paper, we propose an approach for the detection of spectral resources based on reinforcement learning, allowing the cognitive radio to select the FBs with the most available resources. <s> BIB007 </s> Advances in cognitive radio networks: A survey <s> 4) Other Techniques: <s> In this paper, an efficient approach to Medium Access Control layer spectrum sensing algorithm is introduced for the purpose of peer-to-peer communications with cognitive radio. The proposed algorithm is designed specifically for the application scenario of information gathering in a full-loaded legacy voice and data band, where the cognitive radio tries to utilize other available licensed bands through spectrum sensing. Efficiency is achieved in the proposed algorithm through the use of a knowledge-based reasoning approach to fine sensing, where the optimal range of channels to finely sense is determined using proactive fast sensing and channel quality information as priors. Simulation results demonstrate that the proposed spectrum sensing algorithm is capable of achieving improved performance over existing techniques by drawing a good balance between performance and operational complexity. <s> BIB008 </s> Advances in cognitive radio networks: A survey <s> 4) Other Techniques: <s> Spectrum sensing, i.e., detecting the presence of primary users in a licensed spectrum, is a fundamental problem in cognitive radio. Since the statistical covariances of the received signal and noise are usually different, they can be used to differentiate the case where the primary user's signal is present from the case where there is only noise. In this paper, spectrum-sensing algorithms are proposed based on the sample covariance matrix calculated from a limited number of received signal samples. Two test statistics are then extracted from the sample covariance matrix. A decision on the signal presence is made by comparing the two test statistics. Theoretical analysis for the proposed algorithms is given. Detection probability and the associated threshold are found based on the statistical theory. The methods do not need any information about the signal, channel, and noise power a priori. In addition, no synchronization is needed. Simulations based on narrow-band signals, captured digital television (DTV) signals, and multiple antenna signals are presented to verify the methods. <s> BIB009
There are several other spectrum sensing techniques proposed in recent literature, and some of them are variations inspired by the above-mentioned sensing techniques. Statistical Covariance-Based Sensing: Since the statistical covariance matrices of the received signal and noise are generally different, the difference is used in BIB009 and BIB002 to differentiate the desired signal component from background noise. The eigenvalues of the covariance matrix of the received signal can also be used for primary detection BIB003 . Based on random matrix theory  , the ratio of the maximum eigenvalue to the minimum eigenvalue is quantized, and the detection threshold can be found among them. From the simulation on detecting digital TV signals, these methods based on statistical covariances are shown to be more robust to noise uncertainty while requiring no a priori information of the signal, the channel, and noise power. Filter-Based Sensing: Application of a specific class of filter banks is proposed in BIB004 for spectrum sensing in CR systems. When filter banks are used for multicarrier communications in CR networks, the spectrum sensing can be performed by only measuring the signal power at the outputs of subcarrier channels with virtually no computational cost. The multitaper method BIB001 can also be thought as a filter bank spectrum estimation with multiple filter banks. Fast Sensing: By utilizing the theory of quickest detection, which performs a statistical test to detect the change of distribution in spectrum usage observations as quickly as possible, an agile and robust spectrum sensing is achieved in BIB005 . The unknown parameters after a primary user appears can be estimated using the proposed successive refinement, which combines both generalized likelihood ratio and parallel cumulative sum tests. An efficient sensing-sequence is developed in BIB006 to reduce the delay due to spectrum opportunity discovery. The probability that a frequency band is available at sensing, the sensing duration and the channel capacity are three factors that determine the sensing sequence. Learning/Reasoning-Based Sensing: An approach based on reinforcement learning for the detection of spectral resources in a multi-band CR scenario is investigated in BIB007 , where the optimal detection strategy is obtained by solving a Markov decision process (MDP). A medium access control layer spectrum sensing algorithm using knowledge-based reasoning is proposed in BIB008 , where the optimal range of channels to finely sense is determined through proactive fast sensing and channel quality information.