Multilayer sensors for the Sensorial Radio Bubble

Palicot, Jacques; Moy, Christophe; Hachemani, Rachid

doi:10.1016/j.phycom.2009.03.003

Cited by 13 publications

(6 citation statements)

References 27 publications

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“…These functionalities can be implemented in different layers of a widely used Open System Interconnection (OSI) protocol stack. While relating a CR architecture with the OSI model, the following mapping can be made between OSI layers and cognitive capabilities [23]: (i) context awareness for higher layer (application), (ii) inter-operability for intermediate layers (transport and network), and (iii) link adaptation for lower layers (physical and data link). The focus of this survey paper will be on the lower layers of this protocol stack, mostly from physical layer perspective.…”

Section: Enabling Techniques For Cr Communicationsmentioning

confidence: 99%

Cognitive Radio Techniques Under Practical Imperfections: A Survey

Sharma

Bogale

Chatzinotas

et al. 2015

IEEE Commun. Surv. Tutorials

197

111

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Abstract-Cognitive Radio (CR) has been considered as a potential candidate for addressing the spectrum scarcity problem of future wireless networks. Since its conception, several researchers, academic institutions, industries, regulatory and standardization bodies have put their significant efforts towards the realization of CR technology. However, as this technology adapts its transmission based on the surrounding radio environment, several practical issues may need to be considered. In practice, several imperfections such as noise uncertainty, channel/interference uncertainty, transceiver hardware imperfections, signal uncertainty, synchronization issues, etc. may severely deteriorate the performance of a CR system. To this end, the investigation of realistic solutions towards combating various practical imperfections is very important for successful implementation of the cognitive technology. In this direction, first, this survey paper provides an overview of the enabling techniques for CR communications. Subsequently, it discusses the main imperfections that may occur in the most widely used CR paradigms and then reviews the existing approaches towards addressing these imperfections. Finally, it provides some interesting open research issues.

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Section: Enabling Techniques For Cr Communicationsmentioning

confidence: 99%

Cognitive Radio Techniques Under Practical Imperfections: A Survey

Sharma

Bogale

Chatzinotas

et al. 2015

IEEE Commun. Surv. Tutorials

197

111

View full text Add to dashboard Cite

show abstract

“…Thus, depending on the considered environment, specific sensors are to be designed [4,31,32]. The captured -and/or computed-metrics by the sensors are then processed by the decision making engine.…”

Section: Basic Cognitive Cyclementioning

confidence: 99%

Decision making for cognitive radio equipment: analysis of the first 10 years of exploration

Jouini

Moy

Palicot

2012

J Wireless Com Network

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This article draws a general retrospective view on the first 10 years of cognitive radio (CR). More specifically, we explore in this article decision making and learning for CR from an equipment perspective. Thus, this article depicts the main decision making problems addressed by the community as general dynamic configuration adaptation (DCA) problems and discuss the suggested solution proposed in the literature to tackle them. Within this framework dynamic spectrum management is briefly introduced as a specific instantiation of DCA problems. We identified, in our analysis study, three dimensions of constrains: the environment's, the equipment's and the user's related constrains. Moreover, we define and use the notion of a priori knowledge, to show that the tackled challenges by the radio community during first 10 years of CR to solve decision making problems have often the same design space, however they differ by the a priori knowledge they assume available. Consequently, we suggest in this article, the "a priori knowledge" as a classification criteria to discriminate the main proposed techniques in the literature to solve configuration adaptation decision making problems. We finally discuss the impact of sensing errors on the decision making process as a prospective analysis.

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“…It gives communication systems the ability to explore the radio environment in order to gain knowledge on the spatial and spectrum environment while providing the system with some context awareness. The sensors of the SRB are fully described in [2].…”

Section: Blind Standard Identification For Cognitive Radio Receiversmentioning

confidence: 99%

“…The author claimed that this parameter was enough to discriminate all commercialized wireless standards, which was true in 2003, however with the growing number of new standards introduced on the market it is not true anymore and a full BSRS is needed (cf. Figure 1 and paper [2] In this paper, we use the bandwidth shape parameters to identify blindly a set of standard-like signals. For the sake of simplicity we constrain our experiments to three types of signals: GSM-like, UMTS-like, WiFi-OFDM-like (Section V).…”

Section: A the Bandwidth Recognitionmentioning

confidence: 99%

“…Consequently, designing simple, accurate and reliable sensors enable the CR equipment to adapt itself in the best possible way to its surrounding environment. In [2], the concept of "Sensorial Radio Bubble" (SRB) has been introduce to model and process the environment seen from the several sensors of the equipment. Thus it can be seen as the smallest cluster associated to the CR equipment.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Blind standard identification with bandwidth shape and GI recognition using USRP platforms and SDR4all tools

Wang

Jouini

Nafkha

et al. 2010

Proceedings of the 5th International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications

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Abstract-In this paper, focusing on identifying standards blindly, we propose a bandwidth shape sensor and a GI (guard interval) sensor using USRP (Universal Software Radio Peripheral) platforms and SDR4all tools. These sensors are fundamental parts of the so-called Blind Standard Recognition Sensor. The blind standard bandwidth sensor is based on a Radial Basis Function Neuronal Network designed in Matlab. We have presented first experience of using blind standard bandwidth sensor in a previous work. We will provide in this paper further details on the results of this sensor (simulations, preliminary implementations and validations). The GI sensor is implemented in order to improve the detection performance in the case of two identical bandwidth shapes. The SDR4all driver offers a simple yet efficient interface between the Matlab signal processing codes and the USRP transmitting and receiving platforms. These simple and easily accessible software defined radio tools were used to design and implement two sensors. The inducted simulations and experiments show that the designed system is indeed able to discriminate three standard-like spectrums (e.g., GSM-like, UMTS-like and OFDM-like) under simple yet real transmission conditions using their different bandwidth shapes and to identify a GI-OFDM-like system using cyclic autocorrelation method.

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Multilayer sensors for the Sensorial Radio Bubble

Cited by 13 publications

References 27 publications

Cognitive Radio Techniques Under Practical Imperfections: A Survey

Cognitive Radio Techniques Under Practical Imperfections: A Survey

Decision making for cognitive radio equipment: analysis of the first 10 years of exploration

Blind standard identification with bandwidth shape and GI recognition using USRP platforms and SDR4all tools

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