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.

A survey of spectrum sensing algorithms for cognitive radio applications

This paper considers the detection of the presence/absence of signals in uncertain low SNR environments. Small modeling uncertainties are unavoidable in any practical system and so robustness to them is a fundamental performance metric. The impact of these modeling uncertainties can be quantified by the position of the "SNR wall" below which a detector will fail to be robust, no matter how long it can observe the channel. We propose simple mathematical models for the uncertainty in the noise and fading processes. These are used to show what aspects of the model lead to SNR walls for differing levels of knowledge of the signal to be detected. These results have implications for wireless spectrum regulators. The context is opportunistically sharing spectrum with primary users that must be detected in order to avoid causing harmful interference on a channel. Ideally, a secondary system would be able to detect primaries robustly without having to know much about their signaling strategies. We argue that the tension between primary and secondary users is captured by the technical question of computing the optimal tradeoff between the primary user's capacity and the secondary user's sensing robustness as quantified by the SNR wall. This is an open problem, but we compute this tradeoff for some simple detectors.

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SNR Walls for Signal Detection

Cognitive radio is an exciting emerging technology that has the potential of dealing with the stringent requirement and scarcity of the radio spectrum. Such revolutionary and transforming technology represents a paradigm shift in the design of wireless systems, as it will allow the agile and efficient utilization of the radio spectrum by offering distributed terminals or radio cells the ability of radio sensing, self-adaptation, and dynamic spectrum sharing. Cooperative communications and networking is another new communication technology paradigm that allows distributed terminals in a wireless network to collaborate through some distributed transmission or signal processing so as to realize a new form of space diversity to combat the detrimental effects of fading channels. In this paper, we consider the application of these technologies to spectrum sensing and spectrum sharing. One of the most important challenges for cognitive radio systems is to identify the presence of primary (licensed) users over a wide range of spectrum at a particular time and specific geographic location. We consider the use of cooperative spectrum sensing in cognitive radio systems to enhance the reliability of detecting primary users. We shall describe spectrum sensing for cognitive radios and propose robust cooperative spectrum sensing techniques for a practical framework employing cognitive radios. We also investigate cooperative communications for spectrum sharing in a cognitive wireless relay network. To exploit the maximum spectrum opportunities, we present a cognitive space-time-frequency coding technique that can opportunistically adjust its coding structure by adapting itself to the dynamic spectrum environment.

/pdf/cooperative-communications-for-cognitive-radio-networks-48j13lw4r8.pdf

Cooperative Communications for Cognitive Radio Networks

A location reporting paging communication system comprising space satellites, ground stations and a remote receiving unit adapted to resolve a global position from signals transmitted from a communication transmitter. The subscriber in possession of the remote receiving unit updates the paging network with global positioning information. A caller paging a subscriber in possession of the remote receiving unit may request the global location of the remote receiving unit. The paging network could divulge or block such information from a caller depending on the requirements of the subscriber.

Location reporting satellite paging system with optional blocking of location reporting

The automatic recognition of the modulation format of a detected signal, the intermediate step between signal detection and demodulation, is a major task of an intelligent receiver, with various civilian and military applications. Obviously, with no knowledge of the transmitted data and many unknown parameters at the receiver, such as the signal power, carrier frequency and phase offsets, timing information and so on, blind identification of the modulation is a difficult task. This becomes even more challenging in real-world scenarios with multipath fading, frequency-selective and time-varying channels. With this in mind, the authors provide a comprehensive survey of different modulation recognition techniques in a systematic way. A unified notation is used to bring in together, under the same umbrella, the vast amount of results and classifiers, developed for different modulations. The two general classes of automatic modulation identification algorithms are discussed in detail, which rely on the likelihood function and features of the received signal, respectively. The contributions of numerous articles are summarised in compact forms. This helps the reader to see the main characteristics of each technique. However, in many cases, the results reported in the literature have been obtained under different conditions. So, we have also simulated some major techniques under the same conditions, which allows a fair comparison among different methodologies. Furthermore, new problems that have appeared as a result of emerging wireless technologies are outlined. Finally, open problems and possible directions for future research are briefly discussed.

Survey of automatic modulation classification techniques: classical approaches and new trends

Spectrum awareness is currently one of the most challenging problems in cognitive radio (CR) design. Detection and classification of very low SNR signals with relaxed information on the signal parameters being detected is critical for proper CR functionality as it enables the CR to react and adapt to the changes in its radio environment. In this work, the cycle frequency domain profile (CDP) is used for signal detection and preprocessing for signal classification. Signal features are extracted from CDP using a threshold-test method. For classification, a Hidden Markov Model (HMM) has been used to process extracted signal features due to its robust pattern-matching capability. We also investigate the effects of varied observation length on signal detection and classification. It is found that the CDP-based detector and the HMM-based classifier can detect and classify incoming signals at a range of low SNRs.

Cyclostationary Approaches to Signal Detection and Classification in Cognitive Radio

The problem of detecting the presence of spread-spectrum phase-shift-keyed signals in variable noise and interference backgrounds is considered, and the performances of four detectors are evaluated and compared. The detectors include the optimum radiometer, the optimum modified radiometer that jointly estimates the noise level and detects the signal, and the maximum-SNR spectral-line regenerator for spectral-line frequencies equal to the chip rate and the doubled carrier frequency. It is concluded that the spectral-line regenerators can outperform both types of radiometers by a wide margin. The performance advantages are quantified in terms of receiver operating characteristics for several noise and interference environments and receiver collection times. >

Signal interception: performance advantages of cyclic-feature detectors

The problem of characterizing the sine-wave components in the output of a polynomial nonlinear system with a cyclostationary random time-series input is investigated. The concept of a pure nth-order sine wave is introduced, and it is shown that pure nth-order sine-wave strengths in the output time-series are given by scaled Fourier coefficients of the polyperiodic temporal cumulant of the input time-series. The higher order moments and cumulants of narrowband spectral components of time-series are defined and then idealized to the case of infinitesimal bandwidth. Such spectral moments and cumulants are shown to be characterized by the Fourier transforms of the temporal moments and cumulants of the time-series. It is established that the temporal and spectral cumulants have certain mathematical and practical advantages over their moment counterparts. To put the contributions of the paper in perspective, a uniquely comprehensive historical survey that traces the development of the ideas of temporal and spectral cumulants from their inception is provided. >

The cumulant theory of cyclostationary time-series. I. Foundation

For pt.I see ibid., vol.42, no.12, p.3387-3408 (1994). The development of the theory of nonlinear processing of cyclostationary time-series that is initiated in Part I is continued. A new type of cumulant for complex-valued variables is introduced and used to generalize the temporal and spectral moments and cumulants for cyclostationary time-series from real-valued to complex-valued time-series. The relations between the temporal and spectral moments and cumulants at the inputs and outputs of several signal processing operations are determined. Formulas for the temporal and spectral cumulants of complex-valued pulse-amplitude-modulated time-series are derived. Estimators for the temporal moments and cumulants and for the cyclic polyspectra are presented and their properties are discussed. The performance of these estimators is illustrated by several computer simulation examples for pulse-amplitude-modulated time-series. The theory is applied to the problems of weak-signal detection and interference-tolerant time-delay estimation. >

The cumulant theory of cyclostationary time-series. II. Development and applications

Several researchers have recently proposed the use of higher-order cumulants as features for automatic communication-signal classification. Cumulants can be associated with a stationary signal model or with a cyclostationary signal model, in which case they are periodically time-varying with Fourier components called cyclic cumulants. These researchers limit the maximum order to four, and it is commonly believed that sixth- and higher-order cumulants provide no additional benefit due to their relatively large measurement-error variance. However, we have observed experimentally that classification performance can be improved by the use of sixth-order cyclic cumulants. In this paper, we present the results of a simulation study of the cumulant variance for signals corrupted by cochannel interference and AWGN. Situations in which the use of sixth-order cumulants provides a classification benefit are provided and the benefit is quantified.

Chad M. Spooner

Papers

Cyclostationary Approaches to Signal Detection and Classification in Cognitive Radio

Signal interception: performance advantages of cyclic-feature detectors

The cumulant theory of cyclostationary time-series. I. Foundation

The cumulant theory of cyclostationary time-series. II. Development and applications

On the utility of sixth-order cyclic cumulants for RF signal classification