Miguel López-Benítez

Improved energy detection spectrum sensing for cognitive radio

Energy detection constitutes a preferred approach for spectrum sensing in cognitive radio owing to its simplicity and applicability (it works irrespective of the signal format to be detected) as well as its low computational and implementation costs. The main drawback, however, is its well-known detection performance limitations. Various alternative detection methods have been shown to outperform energy detection, but at the expense of increased complexity and confined field of applicability. In this context, this work proposes and evaluates an improved version of the energy detection algorithm that is able to outperform the classical energy detection scheme while preserving a similar level of algorithm complexity as well as its general applicability regardless of the particular signal format or structure to be detected. The performance improvement is evaluated analytically and corroborated with the experimental results.

Evaluation of Spectrum Occupancy in Spain for Cognitive Radio Applications

Cognitive radio (CR) has been identified as a promising solution to the so-called spectrum scarcity problem. The basic idea of this paradigm is to allow unlicensed users to access in an opportunistic and non-interfering manner some licensed bands that are temporarily unoccupied by licensed users. CR is expected to significantly increase current spectrum usage. However, before this paradigm can turn into reality, a full understanding of the dynamic usage of spectrum is required. Current spectrum utilization has already been evaluated in some measurement campaigns. However, most of them were performed in USA and therefore evaluated the American spectrum regulation and utilization. Few studies have been carried out in other places. In this context, this paper presents spectrum occupancy measurements conducted in the frequency range from 75 MHz to 3 GHz in an outdoor environment in urban Barcelona, Spain. The measurements are analyzed and compared to the official spectrum regulations. The obtained results demonstrate the existence of a significant amount of spectrum available for the deployment of future CR networks.

Methodological aspects of spectrum occupancy evaluation in the context of cognitive radio

Several spectrum measurement campaigns have been performed in diverse locations and scenarios in order to assess the degree to which spectrum is currently used in real wireless communication systems. Although such measurement campaigns follow similar approaches, there is a lack of common and appropriate evaluation methodology, which would be desirable not only to prevent inaccurate results but also to enable the direct comparison of results from different sources. In this context, this work presents a comprehensive and in-depth discussion of several important methodological aspects to be accounted for when evaluating spectrum occupancy. Moreover, a quantitative evaluation of the impact of different factors on the obtained results and various useful guidelines are also provided. The results presented in this work highlight the importance of carefully designing an appropriate methodology when evaluating spectrum occupancy in the context of cognitive radio.

Spectral occupation measurements and blind standard recognition sensor for cognitive radio networks

Cognitive radio has been claimed to be a hopeful solution to the existing conflicts between spectrum demand growth and spectrum underutilization. The basic underlying idea of cognitive radio is to allow unlicensed users to access in an opportunistic and non-interfering manner some licensed bands temporarily unoccupied by licensed users. The cognitive radio concept relies on two basic premises: the current spectrum underutilization, which has been demonstrated in some spectrum measurements campaigns, and the ability of unlicensed users to effectively detect and identify the presence of different licensed technologies in order not to cause harmful interference. In this context, this paper reports the joint work on these two areas that is currently being carried out in the framework of the FP7 Network of Excellence in Wireless COMmunications (NEWCOM++). Concretely, this paper presents spectrum occupancy measurements conducted in the frequency range from 75 MHz to 7075 MHz that demonstrate the low degree to which spectrum is currently used in an urban outdoor environment and also describes the blind standard recognition sensor concept, a sensor embedded in a cognitive radio equipment to enable the identification of many commercial wireless standards without the need to connect to any network. The joint research in both areas is a key step in promoting and validating the idea of dynamic spectrum usage.

Time-Dimension Models of Spectrum Usage for the Analysis, Design, and Simulation of Cognitive Radio Networks

This paper addresses the problem of accurately modeling the spectrum occupancy patterns of real radio communication systems, which is an essential aspect in the study of cognitive radio (CR) networks. The main drawbacks and limitations of previous works are identified, and the methodological procedures on which they rely are improved and extended. Two sophisticated measurement platforms, providing low and high time resolutions, are used to obtain extensive real-world data from a multiband spectrum measurement campaign, embracing a wide variety of spectrum bands of practical interest for CR applications. A comprehensive, systematical, and rigorous analysis of the statistical properties observed in the measurement data is then performed to find accurate models capable of capturing and reproducing, within reasonable complexity limits, the statistical properties of temporal patterns, at both short and long timescales, in real wireless systems. Innovative modeling approaches capable of simultaneously describing statistical properties at both timescales are developed as well. In summary, this paper contributes realistic and accurate time-dimension spectrum usage models for their application to the study and development of CR.

Empirical Time-Dimension Model of Spectrum Use Based on a Discrete-Time Markov Chain With Deterministic and Stochastic Duty Cycle Models

The spectrum occupancy models widely used to date in dynamic spectrum access/cognitive radio (DSA/CR) research frequently rely on assumptions and oversimplifications that have not been validated with empirical measurement data. In this context, this paper presents an empirical time-dimension model of spectrum use that is appropriate for DSA/CR studies. Concretely, a two-state discrete-time Markov chain with novel deterministic and stochastic duty cycle models is proposed as an adequate mean to accurately describe spectrum occupancy in the time domain. The validity and accuracy of the proposed modeling approach is evaluated and corroborated with extensive empirical data from a multiband spectrum measurement campaign. The obtained results demonstrate that the proposed approach is able to accurately capture and reproduce the relevant statistical properties of spectrum use observed in real-world channels of various radio technologies. The importance of accurately modeling spectrum use in the design and evaluation of novel DSA/CR techniques is highlighted with a practical case study.

Versatile, Accurate, and Analytically Tractable Approximation for the Gaussian Q-Function

The existing approximations for the Gaussian Q-function have been developed bearing in mind applications that require high estimation accuracies (e.g., derivation of the error probability for digital modulation schemes). Unfortunately, the associated mathematical expressions are too complex to be easily employed in many other analytical studies, even if they do not require such a high accuracy. This letter proposes a simple, yet accurate mathematical approximation to the Gaussian Q-function. When compared to other existing approximations, the proposed model provides an adequate balance between accuracy and analytical tractability. Its simplicity enables its application over a wider range of analytical studies at reasonable accuracy levels. As an illustrative example, the proposed approximation is employed to obtain a new and simple closed-form expression for the probability of detection of an energy detector under Rayleigh fading channels.

Discrete-time spectrum occupancy model based on Markov Chain and duty cycle models

This paper presents an empirical time-dimension model of spectrum use in the context of dynamic spectrum access. Concretely, a discrete-time two-state Markov chain with novel duty cycle models is proposed as an adequate mean to accurately describe spectrum occupancy in the time domain. The validity and accuracy of the proposed model is assessed and corroborated with extensive empirical data from a multi-band spectrum measurement campaign. The obtained results demonstrate that the proposed approach is able to capture and reproduce with significant accuracy the statistical properties of spectrum use observed in real channels of various technologies.

Common Radio Resource Management Algorithms for Multimedia Heterogeneous Wireless Networks

Heterogeneous wireless systems are envisaged as the integration and joint cooperative management of diverse radio access networks and technologies through which network providers can satisfy the wide variety of user/service demands in a more efficient manner by exploiting their varying characteristics and properties. To achieve this objective, a key tool is common radio resource management technique designed to jointly manage the radio resources from different radio access technologies. In this context, this work proposes and optimizes new common radio resource management techniques designed to efficiently distribute traffic among the available radio access technologies while providing adequate quality of service levels under heterogeneous traffic scenarios. The obtained results demonstrate the ability of the proposed solutions to provide high user/service satisfaction levels while adequately exploiting the overall system resources.

Signal Uncertainty in Spectrum Sensing for Cognitive Radio

The inability to perfectly know the system noise properties to infinite precision, referred to as noise uncertainty, results in noise power calibration errors that have been proven to impose fundamental limitations on the detection performance of any spectrum sensing (signal detection) method in cognitive radio networks. In this work we argue that the inability of cognitive radio users to perfectly know beforehand the primary signals that might be present in the sensed band and their properties, referred to as signal uncertainty in this work, also results in an additional detection performance degradation. The noise uncertainty consequences have widely been studied, verified experimentally and distilled into tractable mathematical models. However, the potential effects of the particular primary signal properties on the resulting detection probability of generic spectrum sensing algorithms, such as energy detection, have not been taken into account in the analysis and performance evaluation of spectrum sensing in cognitive radio networks. In this context, this work develops a mathematical model for signal uncertainty and, based on such model, analyzes the impact of signal uncertainty on the resulting detection performance of spectrum sensing, with and without noise uncertainty, and compares the practical consequences of both degrading effects.