Digital implementations of spectral correlation analyzers

Brown, W. A.; Loomis, H.H.

doi:10.1109/78.193211

Cited by 93 publications

(31 citation statements)

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“…For weak signals in noise, a AtAf > 512 is often required for meaningful estimation. In fact, it is shown in [9] that the feature-to-noise ratio of the cyclic spectrum analyzer is given by Pout = /7~t-~'~-fei 2, where r/is a factor which depends on the feature of the spectral correlation function being computed and on the value of Af. Correction of Fig.…”

Section: Uniprocessor Description Of the Digital Frequency Smoothing mentioning

confidence: 99%

Parallel computation structures for a class of cyclic spectral analysis algorithms

Roberts

Loomis

1995

Journal of VLSI Signal Processing

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Section: Uniprocessor Description Of the Digital Frequency Smoothing mentioning

confidence: 99%

Parallel computation structures for a class of cyclic spectral analysis algorithms

Roberts

Loomis

1995

Journal of VLSI Signal Processing

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“…Fact 7: For radio-signal analysis, including detection, classification, modulation recognition, source location, etc., the cyclic spectrum analyzer and related algorithms that exploit cyclostationarity have proven to be ideally suited (Gardner, , 1986bc, 1987a(Gardner, ,b, 1988b(Gardner, ,c, 1990a(Gardner, ,c, 1991aBrown, 1987;Roberts, 1989;Roberts, Brown, and Loomis, 1991;Brown and Loomis, 1992;Spooner andGardner, 1991, 1992a,b;Spooner, 1990, 1992a;Spooner, 1992).…”

Section: What About Terminology?mentioning

confidence: 99%

Workshop on Cyclostationary Signals

Gardner¹

1993

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“…The digital implementation of spectral correlation analyzers has been well studied in [7]. Two computationally efficient algorithms for cyclic spectral analysis have been suggested in [74], namely the FFT accumulation and the strip spectral correlation.…”

Section: Cyclostationary Feature Detectionmentioning

confidence: 99%

“…However, in case of emergency the public networks may get overloaded. Moreover, the relief network must be able to handle multimedia signals and has to deal 7 Twente Institute for Wireless Mobile Communications website: www.ti-wmc.nl 8 IEEE 802.22 Standard: http://www.ieee802.org/22 9 XG radio website: http://www.darpa.mil/sto/smallunitops/xg.html Chapter 1 Introduction with large, possibly unpredictable amounts of data. The generally available public networks are not considered to be reliable enough for emergency situations because public networks lack capacity (e.g.…”

Section: The Aaf Projectmentioning

confidence: 99%

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Cognitive radio on a reconfigurable platform

Zhang¹

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Due to the explosive growth of wireless communication, the demands for radio spectrum are rapidly increasing. It is very difficult to accommodate new wireless services under the current spectrum allocation scheme. On the other hand, the allocated spectrum is not efficiently utilized. Cognitive Radio is proposed as a technology to solve the imbalance between spectrum scarcity and spectrum under-utilization. Spectrum utilization can be improved by making it possible for a user who does not have the license for spectrum (secondary user) to access the spectrum which is not occupied by the licensed user (primary user). This secondary user has the awareness of the spectrum and adapts its transmission accordingly on a non-interference basis. This spectrum access and awareness scheme is referred to as Cognitive Radio. The idea is also known as Dynamic Spectrum Access (DSA) or Open Spectrum Access (OSA). Cognitive Radio is seen as the final point of software defined radio (SDR) platform evolution. A fully flexible and efficient software defined radio platform will be the enabling technology for Cognitive Radio. Cognitive Radio imposes a number of requirements on the processing platform such as flexibility, energy efficiency and guaranteed throughput/latency. The trend in the implementation of SDR is moving towards Multiprocessor System-on-Chip (MPSoC) platforms.The work of this PhD thesis is part of the Ad-hoc Adaptive Freeband (AAF) project. The aim of the AAF project is to design a Cognitive Radio based wireless ad-hoc network for emergency situations. Although the AAF project addresses Cognitive Radio in a holistic fashion from physical layer to networking issues, the work of this thesis mainly focuses on the design of the adaptive physical layer (baseband processing). The physical layer considered in this thesis mainly consists of two parts: transmission and spectrum sensing. A reconfigurable MPSoC platform is used to support the adaptive baseband processing of Cognitive Radio. A coarse-grain reconfigurable processor called the Montium, developed at the University of Twente, is considered in this thesis as a key element of the proposed MPSoC platform. v The Montium targets the 16 bit Digital Signal Processing (DSP) algorithm domain. It offers a combination of flexibility and energy efficiency.Although MPSoCs offer many advantages, it is a challenging task to map applications onto MPSoCs, especially highly dynamic applications such as Cognitive Radio. There is a gap between the application models used for the specification of such applications and an optimized implementation of the application on an MPSoC. To close the gap, we propose to use a task transaction level (TTL) interface approach both for developing the Cognitive Radio application at system level and for the platform interface between the application and the proposed MPSoC platform. The TTL approach is used throughout the thesis as the system-level design methodology and its advantages are elaborated by mapping adaptive physical layer algorithms for...

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Digital implementations of spectral correlation analyzers

Cited by 93 publications

References 10 publications

Parallel computation structures for a class of cyclic spectral analysis algorithms

Parallel computation structures for a class of cyclic spectral analysis algorithms

Workshop on Cyclostationary Signals

Cognitive radio on a reconfigurable platform

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