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

In this survey paper, we characterize the learning problem in cognitive radios (CRs) and state the importance of artificial intelligence in achieving real cognitive communications systems. We review various learning problems that have been studied in the context of CRs classifying them under two main categories: Decision-making and feature classification. Decision-making is responsible for determining policies and decision rules for CRs while feature classification permits identifying and classifying different observation models. The learning algorithms encountered are categorized as either supervised or unsupervised algorithms. We describe in detail several challenging learning issues that arise in cognitive radio networks (CRNs), in particular in non-Markovian environments and decentralized networks, and present possible solution methods to address them. We discuss similarities and differences among the presented algorithms and identify the conditions under which each of the techniques may be applied.

A Survey on Machine-Learning Techniques in Cognitive Radios

Cognitive radio techniques are being applied to many different communications systems. They hold promise for increasing utilization of radio frequencies that are underutilized today, allowing for improved commercial data services, and allowing for new emergency and military communications services. For example, these techniques are being considered by the U.S. FCC for communications services in unlicensed VHF and UHF TV bands. Although traditionally these techniques are closely associated with software-defined radios, many standards such as WiFi (IEEE 802.11), Zigbee (IEEE 802.15.4), and WiMAX (IEEE 802.16) already include some degree of CR technology today. Further advances are occurring rapidly. IEEE 802.22 will be the first cognitive radio-based international standard with tangible frequency bands for its operation. Standardization is at the core of the current and future success of cognitive radio. Industry stakeholders are participating in international standards activities governing the use of cognitive radio techniques for dynamic spectrum access and coexistence, next-generation radio and spectrum management, and interoperability in infrastructure-less wireless networks. This article provides a review of standardization activities for cognitive radio technologies and comments on prospects and issues for future standardization.

IEEE Standards Supporting Cognitive Radio and Networks, Dynamic Spectrum Access, and Coexistence

Cognitive radio for M2M and Internet of Things: A survey

Progression on spectrum sensing for cognitive radio networks: A survey, classification, challenges and future research issues

A method and system of cognitive communication for generating non-interfering transmission, includes conducting radio scene analysis to find grey spaces using external signal parameters for incoming signal analysis without having to decode incoming signals. The disclosed cognitive communications system combines the areas of communications, signal processing, pattern classification and machine learning to detect the signals in the given spectrum of interests, extracts their features, classifies the signals in types, learns the salient characteristics and patterns of the signal and predicts their future behaviors. In the process of signal analysis, a classifier is employed for classifying the signals. The designing of such a classifier is initially performed based on selection of features of a signal detected and by selecting a model of the classifier.

Cognitive radio methodology, physical layer policies and machine learning

This article describes recent advances in cognitive communications. We combine the concepts of signal processing, communications, pattern classification, and machine learning to make dynamic use of the spectrum, such that the emanated signals do not interfere with the existing ones. Unlike other programs such as neXt Generation communications of the Defense Advanced Research Projects Agency, where radio scene analysis is performed to find the spectrum holes or the white space, we make use of the white, as well as the gray space for non- interfering signal transmission. We examine the possibility of employing machine perception and autonomous machine learning technologies to the autonomous design and analysis of air interfaces. The underlying premise is that a learning module will facilitate adaptation in the standard classification process so that the presence of new types of waveforms can be detected, features that best facilitate classification of the previously and newly identified signals can be determined, and waveforms can be generated by using the basis-set orthogonal to the ones present in the environment. Incremental learning and prediction allows knowledge enhancement as more snapshots of data are processed, resulting in improved decisions. Some of the contributions of this project include technological advances in signal detection, feature identification, signal classification, sub-space tracking, adaptive waveform design, machine learning, and prediction.

Recent advances in cognitive communications

This paper describes new ideas and results on machine learning based cognitive communications in White as well as the Gray space. We combine the concepts of signal processing, communications, pattern classification and machine learning to make a dynamic use of the spectrum such that the emanated signals do not interfere with the existing ones. Unlike other programs such as the neXt Generation (XG) communications program of the Defense Advanced Research Projects Agency (DARPA), where radio scene analysis is carried out to find the spectrum holes also known as the White space, we make use of the White as well as the Gray space for non-interfering signal transmission. Our assumption is that a learning module will facilitate adaptation in the signal classification process, so that the presence of new types of waveforms can be detected, features that best facilitate classification of the previously and newly identified signals can be determined, and waveforms can be generated by using the basis-set orthogonal to the ones present in the environment. Incremental learning and prediction allow knowledge enhancement as more snap-shots of data are processed, resulting in improved decisions. Use of non-competitive policy set results in zero interference with the already existing signals with a modest increase in the White and Gray space utilization. On the other hand competitive policy set utilizes machine learning to predict the future behavior of the signals which results in more than 90% utilization of spectrum at an expense of some interference due to errors in prediction.

Machine Learning based Cognitive Communications in White as Well as the Gray Space

A modified GNN/DA subsystem processes angle only measurements from at least two sensors (but can be replicated to n sensors using a similar track fusion framework per sensor as a local track center and then fusing them via a multiple local track fusion architecture) to reconstruct a complete battle space picture consisting of multiple moving targets. In some cases, the sensor is an EO/IR camera and the moving targets are UAVs. The modified GNN/DA is used as part of a Fire Control Solution (FCS) either implemented on a ground-based vehicle or on-board a projectile.

Global nearest neighbor (gnn) based target tracking and data association

The system and method of dynamic weapon to target assignment (DWTA) using a control based methodology to dynamically assign each projectile to a target in a multiple target engagement situation. In some cases, closest proximity is used in a real-time, to accomplish the DWTA functional requirement and performance criteria. In some cases, g pulling acceleration and projectile fin deflection motion are also used to assess the best matched pair for each projectile and each target with an end goal of intercepting the target or guiding the projectile to an acceptable error basket for target destruction via detonation. For the closest distance criterion for projectile/target pairing, a cutoff time is used to ensure the pairing is conducted within an acceptable duration while still being able to intercept the target or meet a required miss distance basket (e.g., <3 m).

Ned B. Thammakhoune

Papers

Cognitive radio methodology, physical layer policies and machine learning

Recent advances in cognitive communications

Machine Learning based Cognitive Communications in White as Well as the Gray Space

Global nearest neighbor (gnn) based target tracking and data association

Dynamic weapon to target assignment using a control based methodology