There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

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

From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

It is shown that, particularly for terrestrial cellular telephony, the interference-suppression feature of CDMA (code division multiple access) can result in a many-fold increase in capacity over analog and even over competing digital techniques. A single-cell system, such as a hubbed satellite network, is addressed, and the basic expression for capacity is developed. The corresponding expressions for a multiple-cell system are derived. and the distribution on the number of users supportable per cell is determined. It is concluded that properly augmented and power-controlled multiple-cell CDMA promises a quantum increase in current cellular capacity. >

On the capacity of a cellular CDMA system

In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to:  • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information.  Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

IEEE transactions on pattern analysis and machine intelligence

Cognitive Radio Communications and Networks gives comprehensive and balanced coverage of the principles of cognitive radio communications, cognitive networks, and details of their implementation, including the latest developments in the standards and spectrum policy. Case studies, end-of-chapter questions, and descriptions of various platforms and test beds, together with sample code, give hands-on knowledge of how cognitive radio systems can be implemented in practice. Extensive treatment is given to several standards, including IEEE 802.22 for TV White Spaces and IEEE SCC41. Written by leading people in the field, both at universities and major industrial research laboratories, this tutorial text gives communications engineers, R&D engineers, researchers, undergraduate and post graduate students a complete reference on the application of wireless communications and network theory for the design and implementation of cognitive radio systems and networks

Cognitive radio communications and networks: principles and practice

In this paper, we present an efficient implementation of a non-contiguous orthogonal frequency division multiplexing (NC-OFDM) transceiver for cognitive radio systems. NC-OFDM is designed to transmit information in the presence of incumbent users, deactivating subcarriers located in the vicinity of these users to avoid interference. Given that the. core, component of an NC-OFDM transceiver is the fast Fourier transform (FFT), and that several of the subcarriers are deactivated, it is possible to reduce the execution time by "pruning" the FFT. We propose an algorithm that efficiently and quickly primes the FFT for NC-OFDM transceivers. Results show that the proposed algorithm substantially outperforms other FFT pruning algorithms when a medium to large number of subcarriers have been deactivated

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An Efficient Implementation of NC-OFDM Transceivers for Cognitive Radios

This paper presents a genetic-algorithm driven, cognitive radio decision engine that determines the optimal radio transmission parameters for single and multicarrier systems Determining the appropriate radio parameters, given a dynamic wireless channel environment is the primary feature of cognitive radios for wireless communication systems Genetic algorithms (GAs) are designed to select the optimal transmission parameters by scoring a subset of parameters and evolving them until the optimal value is reached for a given goal Although there have been implementations of GA-based single carrier cognitive radio engines, the performance of these algorithms has not been thoroughly analyzed nor have the fitness functions employed by the algorithms been explored in detail Multicarrier systems are common in today's communication environment, thus cognitive techniques that account for only single-carrier systems neglect the practical issues of multiple carriers A set of accurate single carrier and multicarrier fitness functions for our GA implementation that completely control the evolution of the algorithm have been derived The performance analysis results illustrate the trade-offs between the convergence time of the GA and the size of the GA search space Copyright © 2007 John Wiley & Sons, Ltd

/pdf/cognitive-engine-implementation-for-wireless-multicarrier-2xaiyc9mcy.pdf

Cognitive engine implementation for wireless multicarrier transceivers

Dynamic spectrum access networks and wireless spectrum policy reforms heavily rely on accurate spectrum utilization statistics, which are obtained via spectrum surveys. In this paper, we propose a generic spectrum-surveying framework that introduces both standardization and automation to this process, as well as enables a distributed approach to spectrum surveying. The proposed framework outlines procedures for the collection, analysis, and modeling of spectrum measurements. Furthermore, we propose two techniques for processing spectrum data without the need for a priori knowledge. In addition, these techniques overcome the challenges associated with spectrum data processing, such as a large dynamic range of signals and the variation of the signal-to-noise ratio across the spectrum. Finally, we present mathematical tools for the analysis and extraction of important spectrum occupancy parameters. The proposed processing techniques have been validated using empirical spectrum measurements collected from the FM, television (TV), cellular, and paging bands. Results show that the primary signals in the FM band can be classified with a miss-detection rate of about 2% at the cost of 50% false-alarm rate, while nearly 100% reliability in classification can be achieved with the other bands. However, the classification accuracy depends on the duration and the range of frequencies over which data are collected, as well as the RF characteristics of the spectrum measurement receiver.

A Spectrum Surveying Framework for Dynamic Spectrum Access Networks

We present discrete adaptive bit loading algorithms for multicarrier systems with uniform (nonadaptive) power allocation operating in a frequency selective fading environment. The algorithms try to maximize the overall throughput of the system while guaranteeing that the mean bit error rate (BER) remains below a prescribed threshold. We also study the impact of imperfect subcarrier signal-to-noise ratio information on throughput performance. Results show that the proposed algorithms have approximately the same throughput and mean BER as the optimal allocation while having a significantly lower computational complexity relative to other algorithms with near-optimal allocations. Moreover, when compared with algorithms that employ approximations to water filling, the computational complexity is comparable while the overall throughput is closer to the optimum.

/pdf/bit-loading-with-ber-constraint-for-multicarrier-systems-4q9tfbo1t8.pdf

Alexander M. Wyglinski

Papers

Cognitive radio communications and networks: principles and practice

An Efficient Implementation of NC-OFDM Transceivers for Cognitive Radios

Cognitive engine implementation for wireless multicarrier transceivers

A Spectrum Surveying Framework for Dynamic Spectrum Access Networks

Bit loading with BER-constraint for multicarrier systems