Wideband cyclostationary spectrum sensing and characterization for cognitive radios

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Wideband Cyclostationary

Spectrum Sensing and

Characterization for Cognitive

Radios

Tassadaq Nawaz

Department of Electrical, Electronics, Telecommunications

Engineering and Naval Architecture

University of Genova

A thesis submitted for the degree of

Doctor of Philosophy

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Abstract

Motivated by the spectrum scarcity problem, Cognitive Radios (CRs) have been proposed as a solution to opportunistically communicate over unused spectrum licensed to Primary users (PUs). In this context, the unlicensed Secondary users (SUs) sense the spectrum to detect the presence or ab-sence of PUs, and use the unoccupied bands without causing interference to PUs. CRs are equipped with capabilities such as, learning, adaptabil-ity, and reconfigurabiladaptabil-ity, and are spectrum aware. Spectrum awareness comes from spectrum sensing, and it can be performed using different techniques. Most popular techniques include matched filters, energy de-tectors, and cyclostationary feature detector (CFD). Among these, CFD is used for spectrum sensing since matched filtering yields a high complexity architecture, and energy detection is sensitive to noise and fails to differ-entiate among signals. CFD utilize the cyclostationarity of the signals by detecting the spectral peaks in spectral correltion function (SCF), which is sparse in both angular an cyclic frequency domain. Moreover, CFD can differentiate among different signals, interfering signals and noise by using the cyclostationary spectral correlation features.

Wide-band spectrum sensing is a key enabling functionality for CRs, since sensing multiple channels simultaneously increases the probability of find-ing spectrum holes and hence increase the throughput of the system. Due to prioritized spectrum sharing between CRs and PUs, it is not only suf-ficient to detect occupancy but also distinguish among different users in order to manage interference and jamming situations in CR networks. For, this purpose CFD is proposed as a means of extracting the cyclic features of the detected signals in each frequency sub-band. Once the sig-nal features are extracted, a previously trained Artificial Neural Network (ANN) is applied to determine the types of the signals (licit or illicit). When the CRs are operating on a wide-band (from few hundred MHz to several GHz), the spectrum sensing task becomes more complex and could

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impose a large overhead to the sensing system due to high sampling rate analog-to-digital (A/D) converters, and heavy memory overhead. Com-pressive sensing is adopted as a solution to reduce the overhead of A/D conversion and allow us to estimate wide-band with sub-Nyquist rate sam-pling. After, the Nyquist rate signal has been recovered, spectral correla-tion funccorrela-tion (SCF) is applied to extract the cyclic features of wide-band signal. An ANN is trained for sub-Nyquist rate samples to classify each signal as a legitimate or jamming signal.

Finally, a large dataset is created which consists of real-data generated by spectrum measurements. The main purpose for which the dataset has been created is research in the field of PHY-layer security and CR. A Software Defined Radio (SDR) platform has been used to generate modu-lated signals in a specified band which are stored for off-line applications. Further, the cyclic intelligent algorithm is validated on this real dataset.

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List of Figures

2.1 The NTIA’s frequency allocation chart [34] . . . 7

2.2 Measured spectrum utilization for 0 − 6 GHz in downtown Berkeley [12] 7 2.3 Spectrum utilization with temporal variations for 0 − 2.5 GHz in down-town Berkeley [12]. Licensed user inactivity is shown as green color. . 8

2.4 Spectrum utilization from 30 MHz to 6 GHz in Singapore [52]. . . 9

2.5 Cognitive cycle . . . 10

2.6 Energy detection block diagram . . . 11

2.7 Matched filter detection block diagram . . . 12

2.8 Cyclostationary feature detection block diagram . . . 12

3.1 (a) Wide-band spectrum divided into multiple sub-bands (SB) and each SB is occupied by a narrow-band signal. (b) Narrow-band jammer (Tone) jumps to the neighboring SB to jam licit (BPSK or QPSK) signal. 20 3.2 A comparison of common sensing methods . . . 21

3.3 SCF of a wide-band spectrum hosting BPSK and QPSK modulated signals and a non-modulated tone signal. . . 23

3.4 α-profile of a wide-band spectrum hosting BPSK, QPSK and a tone signal at different cyclic frequencies. . . 24

3.5 Proposed Neural Network . . . 25

3.6 Neural Network performance for different number of neurons . . . 27

3.7 Detection probability vs. SNR with 10% false alarm rate . . . 29

3.8 Classification results for different types of signals . . . 30

3.9 Overall classification results . . . 31

4.1 (a)Wide-band spectrum divided into multiple sub-bands (SB) and each SB is occupied by a narrow-band signal or free (b) Narrow-band stealthy jammer (BPSK) jumps to the SB1 to jam licit (BPSK) signal . . . . 34

4.2 SCF of a wide-band spectrum hosting a BPSK and a QPSK modulated signals. . . 35

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4.3 α-profile of a wide-band spectrum hosting a BPSK and a QPSK

mod-ulated signal at different cyclic frequencies. . . 37

4.4 Proposed Neural Network . . . 38

4.5 Classification results for different types of signals . . . 41

4.6 Overall classification results . . . 42

5.1 (a) Wide-band spectrum divided into multiple sub-bands (SB) and each SB is occupied by a narrow-band signal. (b) Narrow-band jammer (QPSK) jumps to the neighboring SB to jam licit (BPSK) signal. . . 46

5.2 SCF of a wide-band spectrum hosting two BPSK signals and a QPSK modulated jamming signal. . . 48

5.3 α-profile of a wide-band spectrum hosting two BPSK signals and a QPSK modulated jamming signal at different cyclic frequencies. . . . 49

5.4 A generic block diagram of the implementation of proposed algorithm. 51 5.5 Performance of Jammer Detection Algorithm over various compression ratios and SNR = 0 dB. SB-1 is used by BPSK, SB-5 is occupied by QPSK and SB-3 is occupied by jammer. . . 53

5.6 Performance of Jammer Detection Algorithm over various compression ratios and SNR = 0 dB. SB-1 is occupied by BPSK signal while jammer has jumped from SB-3 to SB-5 to jam QPSK signal. . . 54

5.7 Performance of Jammer Detection Algorithm over various compression ratios and 0 dB SNR. SB-1 and SB-5 are used by BPSK signals and SB-3 is occupied by modulated jammer . . . 55

5.8 Performance of Jammer Detection Algorithm over various compression ratios and 0 dB SNR. SB-5 is occupied by BPSK signal while modu-lated jammer has jumped from SB-3 to SB-1 to jam BPSK signal. . . 56

5.9 Performance of Jammer Detection Algorithm over various compression ratios and 0 dB SNR. SB-1 and SB-5 are used by BPSK signals and stealthy jammer target licit signal in SB-1 . . . 58

5.10 Performance of Jammer Detection Algorithm over various compression ratios and 0 dB SNR. The licit users changed modulation scheme to QPSK in SB-1 and SB-5 and stealthy jammer target licit signal in SB-1 . 59 6.1 (a) Wide-band spectrum divided into multiple sub-bands (SB) and each SB is occupied by a narrow-band signal. (b) Narrow-band jammer (Tone) jumps to the neighbouring SB to jam licit (BPSK or QPSK) signal. . . 63

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6.2 SCF of a wide-band spectrum hosting BPSK and QPSK modulated

signals and a non-modulated tone signal. . . 64

6.3 Cyclic frequency profile of a wide-band spectrum hosting BPSK, QPSK and a tone signal at different cyclic frequencies. . . 66

6.4 A generic block diagram of the implementation of proposed algorithm. 67 6.5 Proposed Neural Network . . . 70

6.6 Neural Network performance for different number of neurons . . . 71

6.7 Performance comparison of the two proposed ANN based classifiers. One is trained at Nyquist rate samples and the other is trained for samples obtained with different compression ratios. . . 72

6.8 Performance of Jammer Detection Algorithm over various compression ratios and SNRs. SB-1 is used by BPSK, SB-5 is occupied by QPSK and SB-3 is occupied by jammer. . . 73

6.9 Performance of Jammer Detection Algorithm over various compression ratios and SNRs. SB-1 is occupied by BPSK signal while jammer has jumped from SB-3 to SB-5 to jam QPSK signal. . . 74

6.10 Performance of Jammer Detection Algorithm over various compression ratios and SNRs. SB-1 is occupied by BPSK signal while jammer has jumped from SB-3 to SB-5 to jam QPSK signal. . . 75

6.11 Performance comparison with database matching method . . . 77

6.14 Robustness of proposed algorithm . . . 80

7.1 Diagram of the Cognitive Cycle for the proposed Cyclyc Spectral In-telligence algorithm. . . 86

7.2 SDR testbed utilised to generate the dataset with wideband spectrum measurements: a) hardware platform, b) diagram of the main compo-nents of the testbed and their connections. . . 88

7.3 Spectrum observation in the 0-120 MHz (1 burst) which includes the SBW signal at 61 MHz and with transmitt power equal to -3dBm. . . 89

7.4 Spectrum observation (1 burst) with 16 different configuration for the

carrier frequency and transmit power. In both the figures, the SBW signal is overlapped to the wideband spectrum for the sake of clarification. 91

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7.5 Pre-processing applied to the WB signal (in both the pictures, only the SBW signal is shown): (a) WB signal, smoothed signal, thresholding and bin grouping, and threshold ; (b) also includes the waveform after

group smoothing. . . 92

7.6 Wideband spectrum measurement before pre-processing (blue line) and

after pre-processing (red line). . . 93

7.7 CSF of two of the detected signals in the WB spectrum: (a) BB signal

and (b) SBW signal. . . 95

7.8 Proposed Artificial Neural Network used as classifier in the CSI

algo-rithm with I inputs, one hidden layer with N neurons, and 2 outputs. 97

7.9 Confusion matrix of the proposed ANN with two classes: FR and PM. 100

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List of Tables

4.1 Optimal jamming signals in a coherent scenario . . . 36

6.1 Signal classifications for CR = 0.40 at -5 dB . . . 82

6.2 Signal classifications for CR = 0.40 at 0 dB . . . 82

7.1 Configurations for transmit parameters of the SBW signal in the

col-lected dataset: carrier frequency and transmitting power. . . 90

7.2 Confusion matrices for the testing step obtained by feeding the ANN

with the α-profile of 1000 independent samples at 7 dBm transmit power.102

with the α-profile of 1000 independent samples at 4 dBm transmit power.102

with the α-profile of 1000 independent samples at -3 dBm transmit

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Chapter 1 Introduction

1.1 Motivation and Objectives

Cognitive radios have been proposed as a promising solution for the future spec-trum scarcity problem. The term cognitive radio has been used to intelligent radio devices that are capable of learning and adapting to their radio frequency environ-ment. Cognitive radios are built upon software defined radios, in which most of the signal processing tasks are being handled by general-purpose processors, instead of specific-purpose hardware as in legacy radio systems.

In order for cognitive radio to work, spectrum sensing is the primary task needed to be performed by a cognitive radio terminal. Many sensing techniques have been proposed over the last decade based on matched filter, energy detection and cyclosta-tionary detection. The matched filter detector can detect the primary signals with very high accuracy, but the comprehensive prior knowledge for primary user’s signal is needed. Moreover, if the environment changes, the detector’s performance degrades. Energy detector is easy to implement but it fails at low signal to noise ratios. More-over, it is unable to differentiate among different types of signals. On the other hand, cyclostationary feature detector, due to its good performance at low signal to noise ratios, has gained much attention from the research community. Furthermore, cyclo-stationary feature detector is able to accurately characterize or classify the spectrum, which is a key aspect for certain cognitive radio application areas, and is an essential intermediate step between detection and demodulation. Specifically in communica-tion electronic warfare, a cognitive radio system may be required to classify different types of signals in wide-band spectrum to avoid any potentially harmful interferes / jammers.

Radio frequency jamming is the process of illegitimate radio frequency transmis-sion on one or more channels with the aim of disrupting the communication of the

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targeted system. Jamming attacks can noticeably affect the performance of wire-less communication systems and can lead to significant overload in terms of data re-transmission and increased power consumption. This thesis is not only focused on detection of jamming attacks, but also to classify legitimate wave forms (modulation scheme) in wide-band radios. It is anticipated that studies presented in this thesis will create an impact on future tactical battlefield solutions. Furthermore, it is expected that outcome of the presented research will be helpful in increasing the throughput of the commercial communication systems, by allowing multiple signals to coexist within the same band.

1.2 Thesis Contributions

The main research contributions of this thesis can be summarized as follows:

• Designed a new algorithm for wide-band spectrum characterization using spec-tral correlation and artificial neural network.

• Designed a new algorithm for wide-band spectrum characterization using com-pressed sensing for sub-Nyquist rate spectrum estimation.

• Designed an robust and energy efficient algorithm for signal classification in wide-band radios using compressed sensing and artificial neural network. • Performances of the above designed algorithms are evaluated for various

jam-ming attacks. For example, tone, modulated and stealthy (reactive and adap-tive) jamming attacks.

• A large dataset is created consisting of real-data generated by spectrum mea-surements. The main purpose of the dataset is to use it for research in the field of PHY-layer security and cognitive radios. A military Software Defined Ra-dio (SDR) platform has been used to generate modulated signals in a specified band, which are stored for off-line applications.

• Practically implemented, designed spectrum characterization algorithm on the created dataset.

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1.3 Thesis Organization

The thesis consist of eight chapters in total which are based on various peer-reviewed journal and conference papers. Each of the chapters serve as a self-contained text, which introduce all the relevant concepts that are required to comprehend the topic they discuss. The thesis is organized as follows:

• Chapter 2 gives background on cognitive radio technology, spectrum sensing, signal classification and other important topics relevant to this thesis. Vari-ous spectrum sensing and signal classification techniques are briefly discussed. The motivation for wide-band spectrum sensing is elaborated and compressed sensing is reviewed in context of wide-band spectrum estimation and charac-terization. Moreover, various existing implementations of the cognitive radio test-beds and platforms are discussed briefly in the end.

• Merely, identification of white spaces is not sufficient in cognitive radios. The spectrum may contain jamming/interfering entities that can target the ongoing licit transmission. Therefore, for secure communication, it is important to clas-sify the sensed spectrum into friendly and jamming waveforms. In this context, a new jammer detection algorithm is proposed in Chapter 3, using spectral correlation and artificial neural network. The proposed approach assumes a Wide-band spectrum occupied by various narrow-band signals, which can be either legitimate or jamming signals. The second order statistics, namely, the spectral correlation function and artificial neural network are used to classify each narrow band signal as a legitimate or jamming signal. The algorithm performance is shown with the help of simulations.

• Chapter 4 presents a new physical layer approach for stealthy jammer detection in wide-band cognitive radio networks. Stealthy jammer is an adaptive reactive jammer with same capabilities as a cognitive radio user. These type of jammers only transmit when a legitimate transmission is detected and stop transmitting when the legitimate transmission stops. Therefore, the jamming activities of such jammers are hard to detect by the popular sensing approaches such as energy detection at physical layer . Hence, we presented in this chapter a novel cyclostationary feature detector based approach to detect such types of jammers at physical layer. The proposed algorithm considers a wide-band consisted of multiple narrow-band sub-bands, which can be occupied by licit or stealthy jamming signals . The cyclostationary spectral analysis is performed on this

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wide-band spectrum to compute spectral correlation function. The alpha profile is extracted from the spectral correlation function and used as input features to artificial neural network, which classify each signal as a licit signal or a stealthy jamming signal. In the end, the performance of the proposed approach is shown with the help of Monte-Carlo simulations under different empirical setups. • When the radios are operating on a wide-band, the sensing task becomes more

complex due to high-rate sampling, analog-to-digital (A/D) converter require-ments. In chapter 5, a novel algorithm is proposed for jammer detection in wide-band cognitive radios using compressed sensing and cyclic spectral analy-sis. First, the received wideband signal is recovered from sub-Nyquist rate sam-ples using compressed sensing. Compressed sensing is used to alleviate Nyquist rate sampling requirements at the receiver A/D converter. After the Nyquist rate signal has been recovered, a cyclostationary spectral analysis is performed on this estimated WB signal, to compute spectral correlation function. The alpha profile is then extracted from spectral correlation function and used to classify each narrow band signal as a licit signal or illicit signal. This algorithm is applied to detect three different types of jammers, namely; tone, modulated and stealthy jammers. The performance is shown with the help of simulations as jammer detection rate versus the compression rate.

• Chapter 6 presents a cyclostationary-based jammer detection algorithm for wide-band radios using compressed sensing and artificial neural network. The proposed approach considers a wide-band spectrum, which is occupied by sev-eral narrow-band signals. These narrow-band signals can be legitimate signals or jamming signals. Compressed sensing is used to reduce the overhead of the analog-to-digital (A/D) conversion and allows one to estimate a wide-band spec-trum with sub-Nyquist rate sampling. Therefore, proposed algorithm is able to recover the received wide-band signal from sub-Nyquist rate samples using com-pressed sensing. After the Nyquist rate signal has been recovered, the second order statistics, namely spectral correlation function, is computed to extract the cyclic features of wide-band signal. Finally, a pre-trained artificial neural net-work is able to characterize each narrow-band signal as a legitimate or jamming signal. In end, the performance of proposed algorithm is evaluated for various compression ratios and SNRs to observe the effect of various parameters on the classification performance.

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• Chapter 7 is focused on the creation of a large dataset consisting of experimental data generated by spectrum measurements. A software defined radio platform is used to generate modulated signals in a specified band which are stored for off-line applications. Further, newly developed cyclic intelligent algorithm is validated on real dataset for radio frequency interference mitigation. The experimental results are shown in form of confusion matrices for various system configurations.

• Chapter 8 presents some of the major conclusions drawn from the presented research and details a few future directions that would be taken to further enhance the presented work.

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Chapter 2 Background and related work

2.1 Spectrum Scarcity

The Radio Frequency (RF) spectrum is a precious natural resource whose allocation is governed by various government bodies around the world. Due to fixed assignment policies, most portions of the usable RF spectrum are already allocated to different services and systems as shown in Fig. 2.1. On the other hand, transition from voice-only to multimedia communications is further amplified the need for higher data rate applications. This fixed assignment of the frequencies and the growing demands for radio spectrum due to emergence of new technologies has caused spectrum scarcity [36]. Without addressing spectrum scarcity issue, the development of radio technolo-gies become unsustainable.

In order to address this problem, innovative techniques must be developed with the goal of providing new and more efficient methods of utilizing the usable spectrum. In several spectrum measurement surveys, it has been shown that vast portions of the licensed spectrum are highly under utilized in various geographical locations at various times [36, 34, 12]. This is situation is shown in Figures 2.2, 2.3 and 2.4.

2.2 Cognitive Radio

The Cognitive Radio (CR) concept was first introduced by Mitola as ” a radio or a system that senses and is aware of its operational RF environment and can dynam-ically and autonomously adjust its radio operating parameters accordingly” [67, 68]. Therefore, a CR is distinguished from legacy communication device by two main as-pects: the cognition capability and reconfigurability. In order to make CRs flexible, the burden has been shifted from the analog to the digital domain. After a decade of extensive research, CRs have gained a lot of interest have shown much promise. The

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THIS CHART WAS CREATED BY DELMON C. MORRISON JUNE 1, 2011

UNITED

STATES

THE RADIO SPECTRUM

NON-FEDERAL EXCLUSIVE

FEDERAL/NON-FEDERAL SHARED FEDERAL EXCLUSIVE

RADIO SERVICES COLOR LEGEND

ACTIVITY CODE

PLEASE NOTE: THE SPACING ALLOTTED THE SERVICES IN THE SPECTRUM

SEGMENTS SHOWN IS NOT PROPORTIONAL TO THE ACTUAL AMOUNT OF SPECTRUM OCCUPIED.

ALLOCATION USAGE DESIGNATION

SERVICE EXAMPLE DESCRIPTION

Primary FIXED Capital Letters

Secondary Mobile 1st Capital with lower case letters

U.S. DEPARTMENT OF COMMERCE

National Telecommunications and Information Administration

Office of Spectrum Management JANUARY 2016

* EXCEPT AERONAUTICAL MOBILE (R) ** EXCEPT AERONAUTICAL MOBILE ALLOCATIONS

FREQUENCY

ST ANDARD FREQUENCY AND TIME SIGNAL (20 kHz) FIXED MARITIME MOBILE Radiolocation FIXED MARITIME MOBILE FIXED MARITIME MOBILE MARITIME MOBILE

FIXED AERONAUTICALRADIONA

VIGA TION Aeronautical Mobile AERONAUTICAL RADIONAVIGATION MaritimeRadionavigation (radiobeacons) Aeronautical Mobile AERONAUTICALRADIONA VIGA TION Aeronautical Radionavigation (radiobeacons)

NOT ALLOCATED RADIONAVIGATION

MARITIME MOBILE FIXED Fixed FIXED MARITIME MOBILE 0 kHz MARITIME RADIONA VIGA TION (radiobeacons) 0 9 14 19.95 20.05 5961 70 90 110 130 160 190 200 275285 300 Radiolocation 300 kHz FIXED MARITIME MOBILE ST ANDARD FREQUENCY AND TIME SIGNAL (60 kHz) Aeronautical Radionavigation (radiobeacons) MARITIME RADIONAVIGATION(radiobeacons) Aeronautical Mobile Maritime Radionavigation (radiobeacons) Aeronautical Mobile Aeronautical Mobile RADIONA VIGA TION AERONAUTICAL RADIONA VIGA TION MARITIME MOBILE Aeronautical Radionavigation MARITIME MOBILE MOBILE

BROADCASTING(AM RADIO)

MARITIME MOBILE(telephony)

MOBILE

FIXED STANDARD FREQ.

AND TIME SIGNAL (2500kHz) FIXED AERONAUTICALMOBILE (R) RADIO-LOCATION FIXED MOBILE AMA TEUR RADIOLOCA TION MOBILE FIXED MARITIME MOBILE MARITIME MOBILE FIXED MOBILE BROADCASTING AERONAUTICAL RADIONA VIGA TION (radiobeacons)

MOBILE (distress and calling)

MARITIME MOBILE(ships only) AERONAUTICAL RADIONA

VIGA

TION

(radiobeacons)AERONAUTICAL RADIONA

VIGA

TION

(radiobeacons) MARITIME MOBILE(telephony)

MOBILE except aeronautical mobile

MOBILE

except aeronautical mobile

MOBILE

MARITIME MOBILE

MOBILE (distress and calling)

MARITIME MOBILE

MOBILE except aeronautical mobile BROADCASTING

AERONAUTICAL RADIONAVIGATION (radiobeacons)

Non-Federal Travelers Information Stations (TIS), a mobile service, are authorized in the 535-1705 kHz band. Federal TIS operates at 1610 kHz.

300 kHz 3 MHz

Maritime Mobile

3MHz 30 MHz

AERONAUTICAL MOBILE (OR)

FIXED

MOBILE

except aeronautical mobile (R)

FIXED

AERONAUTICALMOBILE (R)

AMATEUR MARITIME MOBILE

FIXED

MARITIMEMOBILE

FIXED

MOBILE

AERONAUTICAL

MOBILE (R)

AERONAUTICAL

MOBILE (OR)

MOBILE

FIXED ST ANDARD FREQUENCY AND TIME SIGNAL (5 MHz) FIXED MOBILE FIXED FIXED AERONAUTICAL MOBILE (R) AERONAUTICAL MOBILE (OR) FIXED MOBILE

MARITIME MOBILE AERONAUTICAL MOBILE (R) AERONAUTICAL MOBILE (OR) FIXED AMA TEUR SA TELLITE AMA TEUR AMA TEUR BROADCASTING FIXED MOBILE except aeronautical mobile (R) MARITIME MOBILE FIXED AERONAUTICAL MOBILE (R) AERONAUTICAL MOBILE (OR) FIXED BROADCASTING FIXED STANDARD FREQUENCY AND TIME SIGNAL (10 MHz) AERONAUTICAL MOBILE (R) AMA TEUR FIXED Mobile except aeronautical mobile (R) AERONAUTICAL MOBILE (OR) AERONAUTICAL MOBILE (R) FIXED BROADCASTING FIXED MARITIME MOBILE AERONAUTICAL MOBILE (OR) AERONAUTICAL MOBILE (R) RADIO ASTRONOMY FIXED Mobile

BROADCASTING

FIXED

Mobile

AMA

TEUR

Mobile

FIXED ST ANDARD FREQUENCY AND TIME SIGNAL (15 MHz) AERONAUTICAL MOBILE (OR)

BROADCASTING MARITIME MOBILE

AERONAUTICAL MOBILE (R) AERONAUTICAL MOBILE (OR) FIXED AMA TEUR SA TELLITE AMA TEUR SA TELLITE FIXED 3.0 3.025 3.155 3.23 3.4 3.5 4.0 4.063 4.438 4.65 4.7 4.75 4.85 4.995 5.005 5.06 5.45 5.68 5.73 5.59 6.2 6.525 6.685 6.765 7.0 7.1 7.3 7.4 8.1 8.195 8.815 8.965 9.04 9.4 9.9 9.995 10.005 10.1 10.15 11.175 11.275 11.4 11.6 12.1 12.23 13.2 13.26 13.36 13.41 13.57 13.87 14.0 14.25 14.35 14.99 15.01 15.1 15.8 16.36 17.41 17.48 17.9 17.97 18.03 18.068 18.168 18.78 18.9 19.02 19.68 19.8 19.99 20.01 21.0 21.45 21.85 21.924 22.0 22.855 23.0 23.2 23.35 24.89 24.99 25.01 25.07 25.21 25.33 25.55 25.67 26.1 26.175 26.48 26.95 26.96 27.23 27.41 27.54 28.0 29.7 29.8 29.89 29.91 30.0

BROADCASTING MARITIME MOBILEBROADCASTING

FIXED FIXEDMARITIME MOBILE FIXED

ST ANDARD FREQUENCY AND TIME SIGNAL (20 MHz) Mobile Mobile FIXED BROADCASTING FIXED AERONAUTICAL MOBILE (R) MARITIME MOBILE AMA TEUR SA TELLITE AMA TEUR FIXED Mobile

FIXEDAERONAUTICAL

MOBILE (OR)

MOBILE

FIXED AMA TEUR SA TELLITE AMA TEUR STANDARD FREQ. AND TIME SIGNAL (25 MHz)

LAND MOBILE MARITIME MOBILE LAND MOBILE

FIXED

MOBILE

RADIO ASTRONOMYBROADCASTING MARITIME MOBILE LAND MOBILE

MOBILE

FIXED

LAND MOBILE

FIXED

MOBILE

FIXED FIXED MOBILE FIXED AMA TEUR SA TELLITE AMA TEUR

LAND MOBILE FIXED

FIXED MOBILE FIXED AMA TEUR MOBILE

AMA

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BROADCASTING MARITIME MOBILE

MOBILE except aeronautical mobile 300 325 335 405 415 435 495 505 510 525 535 1605 1615 1705 1800 1900 2000 2065 2107 2170 2173.5 2190.5 2194 2495 2505 2850 3000 30 MHz 300 MHz FIXED

MOBILE LAND MOBILE MOBILE LAND MOBILE MOBILE LAND MOBILE MOBILE FIXED FIXED FIXED FIXED FIXED FIXED

LAND MOBILE LAND MOBILE Radio astronomy FIXED MOBILE FIXED MOBILE LAND MOBILE MOBILE FIXED FIXED LAND MOBILE LAND MOBILE FIXED MOBILE LAND MOBILE FIXED MOBILE

AMATEUR BROADCASTING(TELEVISION )

FIXED MOBILE RADIO ASTRONOMY MOBILE FIXED AERONAUTICAL RADIONA VIGA TION MOBILEMOBILE FIXEDFIXED BROADCASTING (TELEVISION) BROADCASTING (FM RADIO) RADIONAVIGATIONAERONAUTICAL

AERONAUTICALMOBILE (R)AERONAUTICAL AERONAUTICALMOBILE (R)

MOBILE AERONAUTICAL MOBILE AERONAUTICAL MOBILE (R) AERONAUTICAL MOBILE (R) MOBILE-SA TELLITE (space-to-Earth)MOBILE-SA TELLITE (space-to-Earth)

Mobile-satellite (space-to-Earth) Mobile-satellite (space-to-Earth) SPACE RESEARCH (space-to-Earth)SPACE RESEARCH (space-to-Earth)SPACE RESEARCH (space-to-Earth)SPACE RESEARCH (space-to-Earth)

SP

ACE OPERA

TION

(space-to-Earth)SPACE OPERA

TION

TION (space-to-Earth) MET . SATELLITE (space-to-Earth)MET . SATELLITE (space-to-Earth)MET . SATELLITE (space-to-Earth)MET . SATELLITE (space-to-Earth) FIXED

MOBILEAMATEUR- SA

TELLITE AMA TEUR AMA TEURFIXED MOBILE MOBILE-SA TELLITE (Earth-to-space) FIXED MOBILE FIXED LAND MOBILE FIXED LAND MOBILE RADIONA VIGA TION-SATELLITE

MARITIME MOBILE MARITIME MOBILE MARITIME MOBILE MOBILE except aeronautical mobileFIXED

LAND MOBILE

MARITIME MOBILE

MARITIME MOBILE (AIS)

MOBILE except aeronautical mobile FIXEDFIXED

Land mobile

FIXED

MOBILE

FIXED

Mobile

FIXED

FIXED MOBILE

LAND MOBILE

MARITIME MOBILE (distress, urgency

, safety and calling)

MARITIME MOBILE (AIS)

MOBILE except aeronautical mobile FIXED Amateur AERONAUTICALMOBILE (R) MOBILE-SA TELLITE (Earth-to-space) BROADCASTING (TELEVISION) FIXED AMA TEUR

Land mobileFixed

30.0 30.56 32.0 33.0 34.0 35.0 36.0 37.0 37.5 38.0 38.25 39.0 40.0 42.0 43.69 46.6 47.0 49.6 50.0 54.0 72.0 73.0 74.6 74.8 75.2 75.4 76.0 88.0 108.0 117.975 121.9375 123.0875 123.5875 128.8125 132.0125 136.0 137.0 137.025 137.175 137.825 138.0 144.0 146.0 148.0 149.9 150.05 150.8 152.855 154.0 156.2475 156.7625 156.8375 157.0375 157.1875 157.45 161.575 161.625 161.775 161.9625 161.9875 162.0125 163.0375 173.2 173.4 174.0 216.0 217.0 219.0 220.0 222.0 225.0 300.0 FIXED Fixed Land mobile LAND MOBILE LAND MOBILE 300.0 328.6 335.4 399.9 400.05 400.15 401.0 402.0 403.0 406.0 406.1 410.0 420.0 450.0 454.0 455.0 456.0 460.0 462.5375 462.7375 467.5375 467.7375 470.0 512.0 608.0 614.0 698.0 763.0 775.0 793.0 805.0 806.0 809.0 849.0 851.0 854.0 894.0 896.0 901.0 902.0 928.0 929.0 930.0 931.0 932.0 935.0 940.0 941.0 944.0 960.0 1164.0 1215.0 1240.0 1300.0 1350.0 1390.0 1392.0 1395.0 1400.0 1427.0 1429.5 1430.0 1432.0 1435.0 1525.0 1559.0 1610.0 1610.6 1613.8 1626.5 1660.0 1660.5 1668.4 1670.0 1675.0 1695.0 1710.0 1761.0 1780.0 1850.0 2000.0 2020.0 2025.0 2110.0 2180.0 2200.0 2290.0 2300.0 2305.0 2310.0 2320.0 2345.0 2360.0 2390.0 2395.0 2417.0 2450.0 2483.5 2495.0 2500.0 2655.0 2690.0 2700.0 2900.0 3000.0 300 MHz AERONAUTICAL RADIONA VIGA TION FIXED MOBILE RADIONA VIGA TION SA TELLITE MOBILE SA TELLITE (Earth-to-space) STANDARD FREQUECY AND TIME SIGNAL - SA TELLITE (400.1 MHz) MET . AIDS (Radiosonde)

MOBILE SAT (S-E) SPACE RES. (S-E) Space Opn. (S-E)

MET

. SAT.

(S-E)

MET. AIDS

(Radiosonde)

SPACE OPN. (S-E)

MET-SA T. (E-S) EAR TH EXPL SAT. (E-S) Earth Expl Sat(E-S)Earth Expl Sat(E-S)

EARTH EXPL SAT. (E-S) MET -SAT. (E-S) MET. AIDS (Radiosonde) Met-Satellite (E-S) Met-Satellite (E-S) METEOROLOGICAL AIDS (RADIOSONDE) MOBILE SA TELLITE (Earth-to-space) RADIO ASTRONOMY FIXED MOBILE FIXED MOBILE

SPACE RESEARCH (space-to-space)

RADIOLOCA TION Amateur LAND MOBILE FIXED LAND MOBILE LAND MOBILE FIXED LAND MOBILE MeteorologicalSatellite (space-to-Earth) LAND MOBILE FIXED LAND MOBILE FIXED LAND MOBILE LAND MOBILE LAND MOBILE

FIXED BROADCASTING(TELEVISION) FIXED BROADCASTING(TELEVISION)

LAND MOBILE (medical telemetry and medical telecommand)

RADIO ASTRONOMY BROADCASTING(TELEVISION)

BROADCASTING (TELEVISION) MOBILE FIXED MOBILE FIXED MOBILE FIXED MOBILE FIXED

MOBILELAND MOBILE

FIXED LAND MOBILE AERONAUTICAL MOBILE LAND MOBILE AERONAUTICAL MOBILE FIXED LAND MOBILE FIXED LAND MOBILE FIXED MOBILE RADIOLOCA TION FIXED FIXED LAND MOBILE FIXED MOBILE FIXED LAND MOBILE FIXED FIXED LAND MOBILE FIXED MOBILE FIXEDFIXED AERONAUTICAL RADIONAVIGATION RADIONA VIGA TION-SA TELLITE (space-to-Earth)(space-to-space) EARTH EXPLORATION-SATELLITE(active) RADIO-LOCATION RADIONA VIGA TION-SATELLITE (space-to-Earth) (space-to-space) SPACE RESEARCH(active) Space research(active) Earth exploration-satellite (active) RADIO-LOCATION SPACE RESEARCH(active) AERONAUTICALRADIO - NAVIGATION Amateur AERONAUTICAL RADIONA VIGA TION FIXED MOBILE RADIOLOCA TION FIXED MOBILE ** Fixed-satellite (Earth-to-space) FIXED MOBILE **

LAND MOBILE (medical telemetry and medical telecommand)

SPACE RESEARCH(passive) RADIO ASTRONOMY EARTH EXPLORA TION - SA TELLITE (passive)

LAND MOBILE (telemetry and telecommand) LAND MOBILE (medical telemetry and medical telecommand Fixed-satellite (space-to-Earth)

FIXED (telemetry andtelecom

mand)

LAND MOBILE

(telemetry & telecommand)

FIXED

MOBILE **

MOBILE (aeronautical telemetry)MOBILE SA

TELLITE (space-to-Earth) AERONAUTICAL RADIONA VIGA TION-SA TELLITE (space-to-Earth)(space-to-space) MOBILE SA TELLITE (Earth-to-space) RADIODETERMINA TION-SATELLITE (Earth-to-space) MOBILE SA TELLITE (Earth-to-space) RADIODETERMINA TION-SATELLITE (Earth-to-space) RADIO ASTRONOMY MOBILE SA TELLITE (Earth-to-space) RADIODETERMINA TION-SATELLITE (Earth-to-space) Mobile-satellite (space-to-Earth) MOBILE SA TELLITE(Earth-to-space) MOBILE SA TELLITE (Earth-to-space)

RADIO ASTRONOMYRADIO ASTRONOMY

FIXED

MOBILE **

METEOROLOGICAL

AIDS

(radiosonde)METEOROLOGICALSATELLITE (space-to-Earth)METEOROLOGICALSATELLITE (space-to-Earth)

FIXED MOBILE FIXED MOBILE SPACE OPERA TION (Earth-to-space) FIXED MOBILE MOBILE SA TELLITE (Earth-to-space) FIXEDMOBILE SP

ACE RESEARCH (passive)RADIO ASTRONOMY METEOROLOGICAL

AIDS (radiosonde) SPACE RESEARCH (Earth-to-space) (space-to-space) EARTH SATELLITE (Earth-to-space) (space-to-space) FIXED MOBILE SPACE OPERA TION (Earth-to-space) (space-to-space) MOBILE FIXED SPACE RESEARCH (space-to-Earth) (space-to-space) EARTH EXPLORATION- SATELLITE (space-to-Earth) (space-to-space) SPACE OPERATION (space-to-Earth) (space-to-space) MOBILE (ling of sight only including aeronautical telemetry, but excluding flight testing of mannedaircraft) FIXED (line of sight only)

FIXED

SPACE RESEARCH (space-to-Earth) (deep space) MOBILE**Amateur FIXED MOBILE** Amateur RADIOLOCA TION RADIOLOCA TION MOBILE FIXED Radio- location Mobile Fixed BROADCASTING - SA TELLITE Fixed Radiolocation Fixed Mobile Radio- location BROADCASTINGSATELLITE FIXED MOBILE RADIOLOCA TION RADIOLOC A TION MOBILE MOBILE AMA TEUR AMA TEUR Radiolocation MOBILE FIXED Fixed AmateurRadiolocation MOBILE SA TELLITE (space-to-Earth) RADIODETERMINA TION-SATELLITE (space-to-Earth) MOBILE SA TELLITE (space-to-Earth) RADIODETERMINA TION-SA TELLITE (space-to-Earth) FIXED MOBILE** MOBILE** FIXED Earth exploration-satellite (passive) Space research (passive) Radio astronomy MOBILE** FIXEDEARTH EXPLORATION-SATELLITE(passive) RADIO ASTRONOMY

SPACE RESEARCH(passive) AERONAUTICAL RADIONA VIGA TION METEOROLOGICAL AIDS Radiolocation Radiolocation RADIOLOCATION MARITIME RADIO-NAVIGATION MOBILE FIXED BROADCASTING BROADCASTING Radiolocation Fixed(telemetry) FIXED (telemetry andtelecom

mand)

LAND MOBILE (telemetry & telecommand)

AERONAUTICAL RADIONA VIGA TION AERONAUTICAL RADIONA VIGATION AERONAUTICAL RADIONA VIGA TION AERONAUTICAL RADIONA VIGATION AERONAUTICAL RADIONA VIGA TION

Space research(active)

Earthexploration-satellite (active)

EARTH EXPLORATION-SATELLITE(active)

Fixed FIXED FIXED MOBILE ISM – 24.125 ± 0.125 ISM – 5.8 ± .075 GHz 3GHz Radiolocation Amateur AERONAUTICAL RADIONA VIGA TION (ground based) RADIOLOCA TION Radiolocation FIXED-SA TELLITE (space-to-Earth) Radiolocation FIXED AERONAUTICAL RADIONA VIGA TION MOBILE FIXED MOBILE RADIO ASTRONOMY

Space Research (passive)

RADIOLOCA TION RADIOLOCA TION RADIOLOCA TION METEOROLOGICAL AIDS Amateur FIXED

SPACE RESEARCH (deep space)(Earth-to-space)

Fixed FIXED-SA TELLITE (space-to-Earth) AERONAUTICAL RADIONA VIGA TION RADIOLOCA TION Radiolocation MARITIME RADIONA VIGA TION RADIONA VIGA TION Amateur FIXED

RADIO ASTRONOMY BROADCASTING-SA

TELLITE Fixed Mobile Fixed Mobile FIXED MOBILE SPACE RESEARCH(passive)

RADIO ASTRONOMY

EAR

TH EXPLORA

TION

-SATELLITE (passive) FIXED

FIXED MOBILE FIXED-SA TELLITE (space-to-Earth) FIXED MOBILE MOBILE AERONAUTICAL RADIONA VIGA TION

Standard frequencyand time signal(Earth-to-space)satellite

FIXED FIXED MOBILE** FIXED MOBILE** FIXED SA TELLITE (Earth-to-space) Amateur MOBILE BROADCASTING-SA TELLITE FIXED-SA TELLITE (space-to-Earth) MOBILE FIXED MOBILE INTER-SA TELLITE AMA TEUR AMA TEUR-SA

TELLITERadio- location

Amateur RADIO- LOCA TION FIXED INTER-SA TELLITE RADIONA VIGA TION RADIOLOCA TION-SA TELLITE (Earth-to-space) FIXED-SA TELLITE (Earth-to-space) MOBILE-SA TELLITE (Earth-to-space) MOBILE INTER-SA TELLITE 30 GHz Earth exploration-satellite(active) Space research (active)

RADIOLOCA TION RADIOLOCA TION AERONAUTICAL RADIONA VIGA TION (ground based) FIXED-SATELLITE(space-to-Earth) FIXED RADIONA VIGA TION-SA TELLITE (Earth-to-space) AERONAUTICAL RADIONA VIGA TION AERONAUTICAL RADIONA VIGA TION RADIONA VIGA TION-SA TELLITE (space-to-Earth)(space-to-space) AERONAUTICAL RADIONA VIGA TION FIXED-SA TELLITE (Earth-to-space) Earth exploration-satellite (active) Space research Radiolocation EARTH EXPLORATION-SATELLITE(active) SPACE RESEARCH (active) RADIOLOCA TION Earth exploration-satellite (active) Radiolocation Space research(active) EARTH EXPLORATION-SATELLITE(active) SPACE RESEARCH (active) RADIOLOCA TION Radiolocation Space research (active) EARTH EXPLORATION-SATELLITE(active) SPACE RESEARCH (active) RADIOLOCATION AERONAUTICAL RADIONAVIGATION Earth exploration-satellite (active) Radiolocation Space research (active) EARTH EXPLORATION-SATELLITE (active) SPACE RESEARCH (active) RADIOLOCATION RADIONAVIGATION Earth exploration-satellite (active) Space research (active) EARTH EXPLORATION-SATELLITE(active) SPACE RESEARCH (active) MARITIME RADIONA VIGA TION RADIOLOCA TION MARITIME RADIONA VIGA TION RADIOLOCA TION MARITIME RADIONA VIGA TION Amateur RADIOLOCA TION MOBILE FIXED-SA TELLITE (Earth-to-space) FIXED FIXED-SA TELLITE (Earth-to-space) FIXED FIXED-SA TELLITE (Earth-to-space)(space-to-Earth) FIXED FIXED-SA TELLITE (Earth-to-space)(space-to-Earth) MOBILE FIXED-SA TELLITE (Earth-to-space) MOBILE FIXED MOBILE FIXED FIXEDFIXED SPACE RESEARCH (Earth-to-space)FIXEDMOBILE-SA

TELLITE (space-to-Earth) FIXED Mobile-satellite (space-to-Earth) FIXED-SA TELLITE (space-to-Earth) FIXED Mobile-satellite (space-to-Earth) METEOROLOGICAL SATELLITE (space-to-Earth)

FIXED-SA TELLITE (space-to-Earth) FIXED Mobile-satellite (space-to-Earth) FIXED-SA TELLITE (space-to-Earth) FIXED-SA TELLITE (Earth-to-space) MOBILE-SA TELLITE (Earth-to-space) FixedFIXED Mobile-satellite(Earth-to-space)(no airborne)

FIXED SA TELLITE (Earth-to-space) EAR TH EXPLORA SA TELLITE (space-to-Earth)

Mobile-satellite(Earth-to-space)(no airborne)

FIXED EARTH EXPLORA TION- SATELLITE (space-to-Earth) FIXED-SA TELLITE (Earth-to-space) METEOROLOGICAL- SATELLITE (space-to-Earth) FIXED

Mobile-satellite(Earth-to-space)(no airborne)

FIXED-SA TELLITE (Earth-to-space) EAR TH EXPLORA TION-SA TELLITE (space-to-Earth)

Space research (deep space)(space-to-Earth)

SPACE RESEARCH (deep space)(space-to-Earth)

FIXED

SP

ACE RESEARCH (space-to-Earth)

FIXED Earth exploration -satellite (active) Radio-location Space research (active) EARTH EXPLORATION-SATELLITE (active) RADIO-LOCATION SPACE RESEARCH (active) Radiolocation RADIOLOCA TION RadiolocationRadiolocation Radiolocation Meteorological Aids Earth exploration - satellite (active) Radio-location Space research (active) EARTH EXPLORATION SATELLITE (active) RADIO-LOCATION SPACE RESEARCH (active) Radiolocation Radiolocation Amateur-satellite Amateur Radiolocation RADIOLOCA TION RADIOLOCA TION FIXED EARTH EXPLORA TION-SA TELLITE (passive)

SPACE RESEARCH (passive)

EAR

TH EXPLORA

TION-SA

TELLITE (passive)

FIXED-SA TELLITE (space-to-Earth) FIXED FIXED-SA TELLITE (space-to-Earth) FIXEDFIXED FIXED-SA TELLITE (Earth-to-space) Space research (active) EARTH EXPLORATION -SATELLITE (active) SPACE RESEARCH (active) AeronatuicalRadionavigation EARTH EXPLORATION SATELLITE (active) RADIO -LOCATIONSPACE RESEARCH Radio-location Space research RADIO - LOCATION Space research FIXED-SATELLITE (Earth-to-space) Space research Radio - location FIXED-SA TELLITE (Earth-to-space) Mobile-satellite (Earth-to-space)

Space researchMobile-satellite (space-to-Earth)

FIXED-SA

TELLITE (Earth-to-space)

Mobile-satellite (Earth-to-space) Space researchMOBILE

SP ACE RESEARCH FixedFIXED SP ACE RESEARCH Mobile FIXED-SA TELLITE (Earth-to-space) AERONAUTICALRADIONA VIGA TION AERONAUTICAL RADIONA VIGA TION RADIOLOCA TION

Space research (deep space)(Earth-to-space)

RADIOLOCA TION RADIOLOCA TION EARTH EXPLORATION- SATELLITE (active) RADIO-LOCATION SPACE RESEARCH (active) Earth exploration-satellite (active) Radio-location Space research (active) Radiolocation FIXED-SA

TELLITE (Earth-to-space)FIXED

FIXED-SA

TELLITE (space-to-Earth)

SPACE RESEARCH(passive)

EAR TH EXPLORA TION -SA TELLITE (passive) FIXED-SA TELLITE (space-to-Earth) FIXED-SA TELLITE (space-to-Earth) MOBILE-SA TELLITE (space -to-Earth) Standard frequency and time signal satellite (space-to-Earth) FIXED-SA TELLITE (space-to-Earth) MOBILE-SA TELLITE (space-to-Earth) FIXED EAR TH EXPLORA TION -SATELLITE (passive) SP

ACE RESEARCH(passive)FIXED

MOBILE** EARTH EXPLORATION- SATELLITE (passive) MOBILE** FIXED SP ACE RESEARCH (passive) RADIOASTRONOMY MOBILE FIXED FIXED MOBILE FIXED MOBILE EAR TH EXPLORA TION -SATELLITE - (passive) SPACE RESEARCH(passive) RADIO ASTRONOMY Earth exploration -satellite (active) RADIONA VIGA TION FIXED-SA TELLITE (Earth-to-space) FIXED

Standard frequency and time signal satellite (Earth-to-space) FIXEDFIXED EARTH EXPLORATION -SATELLITE (space-to-Earth) SPACE RESEARCH (space-to-Earth) MOBILE INTER-SATELLITE Inter-satellite FIXED INTER-SA TELLITE FIXED-SA TELLITE (Earth-to-space) FIXED-SA TELLITE (Earth-to-space) RADIOLOCA TION MARITIME RADIONA VIGA TION AERONAUTICAL RADIONA VIGA TION INTER-SA TELLITE Inter-satellite Earth exploration -satellite (active) FIXED FIXED-SA TELLITE (Earth-to-space) FIXED Space research Radiolocation Radiolocation Radiolocation RADIOLOCA TION RADIOLOCA TION Earth exploration-satellite (active) 3.0 3.1 3.3 3.5 3.6 3.65 3.7 4.2 4.4 4.5 4.8 4.94 4.99 5.0 5.01 5.03 5.15 5.25 5.255 5.35 5.46 5.47 5.57 5.6 5.65 5.83 5.85 5.925 6.425 6.525 6.7 6.875 7.025 7.075 7.125 7.145 7.19 7.235 7.25 7.3 7.45 7.55 7.75 7.85 7.9 8.025 8.175 8.215 8.4 8.45 8.5 8.55 8.65 9.0 9.2 9.3 9.5 9.8 10.0 10.45 10.5 10.55 10.6 10.68 10.7 11.7 12.2 12.7 13.25 13.4 13.75 14.0 14.2 14.4 14.5 14.7145 14.8 15.1365 15.35 15.4 15.43 15.63 15.7 16.6 17.1 17.2 17.3 17.7 17.8 18.3 18.6 18.8 19.3 19.7 20.2 21.2 21.4 22.0 22.21 22.5 22.55 23.55 23.6 24.0 24.05 24.25 24.45 24.65 24.75 25.05 25.25 25.5 27.0 27.5 29.5 30.0 MOBILE FIXED-SA TELLITE (space-to-Earth) FIXED-SA TELLITE (space-to-Earth) FIXED-SA TELLITE (Earth-to-space) Earth exploration -satellite (active) Amateur-satellite(space-to-Earth) FIXED-SA TELLITE (Earth-to-space) FIXED - SATELLITE (Earth-to-space) MOBILE - SATELLITE (Earth-to-space) Standard Frequency and Time Signal Satellite (space-to-Earth) FIXED MOBILE RADIO ASTRONOMY SPACE RESEARCH (passive) EAR TH EXPLORA TION -SATTELLITE (passive) RADIONA VIGA TION INTER-SA TELLITE RADIONA VIGA TIONRadiolocation FIXED FIXED MOBILE MobileFixed BROADCASTING MOBILE SP

ACE RESEARCH (passive)

EAR

TH EXPLORA

TION-SATELLITE (passive) SPACE RESEARCH (passive)

EAR TH EXPLORA TION-SA TELLITE (passive) EAR TH EXPLORA TION-SATELLITE (passive) SP ACE RESEARCH (passive) MOBILE FIXED MOBILE SATELLITE (space-to-Earth) MOBILE- SATELLITE RADIO NA VIGA TION RADIONAVIGA TION-SATELLITE FIXED-SATELLITE (space-to-Earth) AMA TEUR AMA TEUR-SA TELLITE SPACE RESEARCH (passive) RADIO ASTRONOMY EARTH EXPLORATION-SATELLITE (passive) MOBILE FIXED RADIO- LOCA TION INTER-SA TELLITE RADIO-NAVIGATION RADIO- NAVIGATION-SATELLITE AMA TEUR AMA TEUR - SA TELLITE RADIOLOCA TION EAR TH EXPLORA TION- SATELLITE (passive)

SPACE RESEARCH (passive) RADIO ASTRONOMY MOBILE FIXED RADIO ASTRONOMY INTER-SATELLITE RADIONA VIGA TION RADIONA VIGA TION-SATELLITE SPACE RESEARCH (Passive) RADIO ASTRONOMY EARTH EXPLORATION-SATELLITE(Passive) MOBILE FIXED MOBILE FIXED MOBILE FIXED FIXED-SATELLITE (space-to-Earth) RADIOLOCA TION AMA TEUR AMA TEUR-SA TELLITE Amateur

Amateur-satelliteEARTH EXPLORA

TION-

SATELLITE (passive)

MOBILE

SPACE RESEARCH

(deep space) (space-to-Earth) MOBILE

MOBILE SATELLITE (space-to-Earth) SPACE RESEARCH (Earth-to-space) FIXED-SATELLITE (space-to-Earth) BROADCASTING-SATELLITE INTER- SA TELLITE EAR TH EXPLORA TION-SA TELLITE (passive)

FIXED MOBILE** SPACE RESEARCH (passive) EAR TH EXPLORA TION-SATELLITE (passive) RADIONA VIGA TION RADIO- LOCA TION SPACE RESEARCH

(deep space) (Earth-to-space)

Radio- location Space research (deep space) (Earth-to-space)

Radiolocation RADIOLOCA TION EARTH EXPLORA TION -SATTELLITE (active) RADIO LOCATION SPACE RESEARCH (active) Earth

exploration -sattellite (active)

Radio location Space research (active) EAR TH EXPLORA TION -SA TELLITE(passive) FIXED MOBILE

SPACERESEARCH (passive)

FIXED MOBILE FIXED-SA TELLITE (space-to-Earth) EAR TH EXPLORA TION SATELLITE (Earth-to-space)

Earth explorationsatellite

(space-to-Earth) FIXED-SATELLITE (space-to-Earth) FIXED MOBILE BROADCASTING-SATELLITE BROADCASTING

FIXED- SATELLITE(space-to-Earth)

FIXED MOBILE BROADCASTING BROADCASTING SA TELLITE FIXED MOBILE** FIXED-SA TELLITE (Earth-to-space) RADIO ASTRONOMY FIXED-SA TELLITE (Earth-to-space) MOBILE-SA TELLITE (Earth-to-space) MOBILE MOBILE-SA TELLITE (Earth-to-space) MOBILE-SA TELLITE (Earth-to-space) MOBILE FIXEDFIXED MOBILE FIXED-SA TELLITE (Earth-to-space) FIXED MOBILE FIXED-SA TELLITE (Earth-to-space) MOBILE-SA TELLITE (Earth-to-space) FIXED MOBILE FIXED-SA TELLITE (Earth-to-space) EAR TH EXPLORA TION-SA TELLITE (passive)

SA TELLITE SA TELLITE EARTH EXPLORA TION-SA TELLITE (passive)

FIXED MOBILE EAR TH EXPLORA TION-SA TELLITE (passive)

SA TELLITE FIXED MOBILE INTER- SATELLITE EARTH EXPLORA TION-SA TELLITE (passive)

MOBILE FIXEDRADIO- LOCA

TION SA TELLITE FIXED MOBILE SA TELLITE SA TELLITE EAR TH EXPLORA TION-SA TELLITE SPACE RESEARCH FIXED MOBILE ** SA TELLITE MOBILE BROADCASTING FIXED- SATELLITE (space-to-Earth) Space research (space-to-Earth) MOBILE Amateur RADIO ASTRONOMY RADIOLOCA TION

Space research (space-to-Earth)

Amateur RADIOLOCA TION Space research(space-to-Earth) AMA TEUR RADIOLOCA TION FIXED-SATELLITE (Earth-to-space) MOBILE-SATELLITE (Earth-to-space) Space research (space-to-Earth) FIXED MOBILE FIXED-SATELLITE (Earth-to-space) FIXED MOBILE EARTH EXPLORATION-SATELLITE (active) SPACE RESEARCH (active) RADIO-LOCATION RADIO- LOCA TION MOBILE FIXED FIXED MOBILE RADIO ASTRONOMY RADIO-LOCATION RADIO-NAVIGATION RADIO- NAVIGATION-SATELLITE RADIO ASTRONOMYSPACE RESEARCH (passive) EAR TH EXPLORA TION-SATELLITE (passive) SP ACE

RESEARCH (passive)FIXED

MOBILE SPACE RESEARCH(passive) EAR TH EXPLORA TION-SATELLITE (passive) SP ACE RESEARCH (passive) EAR TH EXPLORA TION-SATELLITE (passive) SP ACE RESEARCH (passive) INTER-SA TELLITE FIXED MOBILE Amateur FIXED-SATELLITE (space-to-Earth) MOBILE-SATELLITE (space-to-Earth) Radio astronomy FIXED MOBILE INTER-SATELLITE EAR TH EXPLORA TION-SATELLITE (active) RADIO ASTRONOMY Radio astronomy Amateur - satellite Amateur FIXED MOBILE RADIO ASTRONOMY

RADIO ASTRONOMY EAR TH EXPLORA TION-SATELLITE (passive) FIXED MOBILE RADIO ASTRONOMY RADIOLOCA TION EAR TH EXPLORA TION-SATELLITE (passive) FIXED RADIO ASTRONOMY FIXED-SATELLITE (space-to-Earth) MOBILE- SATELLITE (space-to-Earth) FIXED MOBILE FIXED MOBILE FIXED-SATELLITE (space-to-Earth) INTER-SATELLITE EAR TH EXPLORA TION- SATELLITE (passive) SP

ACE RESEARCH(passive)

INTER-SA

TELLITE

EAR TH EXPLORA TION- SATELLITE (passive) EAR TH EXPLORA TION- SATELLITE (passive) INTER-SATELLITE SPACE RESEARCH(passive)

EAR

TH EXPLORA

TION-

SATELLITE (passive)

FIXED MOBILE MOBILE SATELLITE INTER-SATELLITE SPACE RESEARCH(passive) EAR TH EXPLORA TION- SATELLITE (passive) RADIO ASTRONOMYFIXED MOBILE FIXED-SATELLITE (Earth-to-space) RADIO ASTRONOMY

FIXED FIXED-SA TELLITE (Earth-to-space) RADIO ASTRONOMY MOBILE FIXED MOBILE FIXED-SATELLITE (space-to-Earth) EAR TH EXPLORA TION- SATELLITE (passive) SP

ACE RESEARCH(passive)

FIXED-SA TELLITE (space-to-Earth) RADIO-NA VIGA TION RADIO-NA VIGA TION-SATELLITE RADIO-LOCATIONRADIOLOCA TION RADIOASTRONOMY Radioastronomy

RADIOASTRONOMY FIXED MOBILE MOBILE-SA TELLITE (Earth-to-space) RADIO ASTRONOMY RADIONA VIGA TION-SA TELLITE RADIO NA VIGA TION FIXED FIXED-SA TELLITE (Earth-to-space) NOT ALLOCA TED MOBIL-ESA TELLITE (space-to-Earth) RADIOLOCA TION RADIOLOCA TION MOBILE FIXED-SA TELLITE (space-to-Earth) Amateur FIXED FIXED-SA TELLITE (space-to-Earth) MOBILE MOBILE-SATELLITE (space-to-Earth) MOBILE FIXED MOBILE FIXED FIXEDFIXED 30.0 31.0 31.3 31.8 32.3 33.0 33.4 34.2 34.7 35.5 36.0 37.0 37.5 38.0 38.6 39.5 40.0 40.5 41.0 42.0 42.5 43.5 45.5 46.9 47.0 47.2 48.2 50.2 50.4 51.4 52.6 54.25 55.78 56.9 57.0 58.2 59.0 59.3 64.0 65.0 66.0 71.0 74.0 76.0 77.0 77.5 78.0 81.0 84.0 86.0 92.0 94.0 94.1 95.0 100.0 102.0 105.0 109.5 111.8 114.25 116.0 122.25 123.0 130.0 134.0 136.0 141.0 148.5 151.5 155.5 158.5 164.0 167.0 174.5 174.8 182.0 185.0 190.0 191.8 200.0 209.0 217.0 226.0 231.5 232.0 235.0 238.0 240.0 241.0 248.0 250.0 252.0 265.0 275.0 300.0 30GHz 300 GHz Amateur- satellite Amateur-satellite Amateur-satellite RADIO ASTRONOMY RADIOASTRONOMY RADIOASTRONOMY RADIOASTRONOMY BROADCASTING SATELLITE SPACE RESEARCH(space-to-Earth) RADIONA VIGA TION-SATELLITE RADIO-NAVIGA TION-SA TELLITE

Space research (space-to-Earth)Space research(space-to-Earth)

RADIOASTRONOMY RADIOASTRONOMY

ISM - 6.78 ± .015 MHz ISM - 13.560 ± .007 MHz ISM - 27.12 ± .163 MHz

ISM - 40.68 ± .02 MHz

3 GHz

ISM - 915.0± .13 MHz ISM - 2450.0± .50 MHz

3 GHz

ISM - 122.5± 0.500 GHz This chart is a graphic single-point-in-time portrayal of the Table of Frequency Allocations used by the FCC and

NTIA. As such, it may not completely reflect all aspects, i.e. footnotes and recent changes made to the Table of Frequency Allocations. Therefore, for complete information, users should consult the Table to determine the current status of U.S. allocations.

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ISM - 61.25± 0.25 GHz ISM - 245.0± 1 GHz AERONAUTICAL MOBILE AERONAUTICAL MOBILE SATELLITE AERONAUTICAL RADIONAVIGATION AMATEUR AMATEUR SATELLITE BROADCASTING BROADCASTING SATELLITE EARTH EXPLORATION SATELLITE FIXED FIXED SATELLITE INTER-SATELLITE LAND MOBILE LAND MOBILE SATELLITE MARITIME MOBILE SATELLITE MARITIME RADIONAVIGATION METEOROLOGICAL METEOROLOGICAL SATELLITE MARITIME MOBILE MOBILE MOBILE SATELLITE RADIO ASTRONOMY RADIODETERMINATION SATELLITE RADIOLOCATION RADIOLOCATION SATELLITE RADIONAVIGATION RADIONAVIGATION SATELLITE SPACE OPERATION SPACE RESEARCH STANDARD FREQUENCY AND TIME SIGNAL

STANDARD FREQUENCY AND TIME SIGNAL SATELLITE

MOBILE SA TELLITE (space-to-Earth) FIXED MOBILE BROADCASTINGSATELLITE RADIO ASTRONOMY MOBILE FIXED RADIONA VIGA TION Radiolocation FIXED RADIO ASTRONOMY MOBILE LAND MOBILE Radiolocation FIXED-SATELLITE (space-to-Earth) FIXED SA TELLITE (space-to-Earth) RADIOLOCA TION RADIO ASTRONOMY RADIO ASTRONOMY RADIO ASTRONOMY MOBILE MOBILE FIXED FIXED RADIO ASTRONOMY RADIO ASTRONOMY RADIO ASTRONOMY RADIO ASTRONOMY RadiolocationRadiolocation Radiolocation Radiolocation RADIO ASTRONOMY FIXED-SA TELLITE (space-to-Earth) SPACE RESEARCH(space-to-Earth) AERONAUTICALMOBILE (R) MOBILE ** SP ACE OPERA TION (Earth-to-space)

Figure 2.1: The NTIA’s frequency allocation chart [34]

Figure 2.2: Measured spectrum utilization for 0 − 6 GHz in downtown Berkeley [12]

fundamental idea behind a CR is the exploitation of exiting white spaces, which are licensed frequency bands that are not used by the Primary User (PU) at a given time and geographical location [82, 51, 27]. These white spaces / spectrum holes are op-portunities for a secondary user (SU), and can be used either temporally, spectrally,

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Figure 2.3: Spectrum utilization with temporal variations for 0−2.5 GHz in downtown Berkeley [12]. Licensed user inactivity is shown as green color.

or spatially. Through CRs, the goal is to greatly increase the spectrum utilization and possibly eliminate the false spectrum scarcity problem caused by static spectrum allocation.

Figure 2.5 presents a cognitive cycle which shows how unique features of a CR conceptually interact with the environment. Namely, Sensing functionality requires contentiously monitoring the RF spectrum. Analysis is the characterization / classi-fication of the spectrum. Reasoning needs to find the best response strategy based on the Analysis and Adaption is the transition of the device or system to new operating parameters.

One of the key aspects for a CR terminal is to avoid imposing any harmful inter-ference to the PU while sharing the licensed spectrum. CRs are divided into three broad categories on bases of their ability to handle this problem. These catagories are overlay, underlay and spectrum-sensing ( interweave) CRs. In overlay CRs, PUs share their signal information with SUs such that SUs may enhance and aid the

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li-Figure 2.4: Spectrum utilization from 30 MHz to 6 GHz in Singapore [52]. censed transmission rather than competing for spectrum. In underlay approach, SUs transmissions are allowed with PUs as long as the interference level at PU remain below a tolerance threshold [57]. In spectrum sensing, SUs continuously monitor the radio environment to sense the spectrum holes and consequently use them for their transmissions. Therefore, the interweave mode allow SUs to efficiently utilize the unused white spaces , while avoiding, or limiting, collisions with PU transmissions. This dissertation is focused on interweave cognitive radios and its application, there-fore, spectrum sensing will be the focus of the dissertation. Throughout this thesis, interweave CR is simply referred to as CR.

2.3 Spectrum Sensing and Signal Classification

Overview

2.3.1 Spectrum Sensing

To perform its tasks properly, a CR should be aware of surrounding environment and identify all types of on going RF activities. Thus, spectrum sensing is the primary task needed to be performed by CR terminal, therefore it was identified as a major part in CRs [97, 59, 65, 14, 64, 95]. Spectrum sensing is known as the ability of a CR terminal to measure the spectrum activities due to ongoing transmissions over different frequency bands and to capture the related parameters.

Various spectrum sensing techniques have been proposed over the last decade, such as matched filtering, cyclostationay feature detection and energy detection [118, 120, 58, 63, 121, 54, 7, 62]. Matched filtering detectors are known be the best due

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Sensing Spectrum monitoring Reasoning Response Strategy Analysis Environment characterization Adaptation Transition to new parameters Radio Environment

Figure 2.5: Cognitive cycle

to their detection performance even at very low SNRs but require a dedicated re-ceiver structure for each type of signal, which may not be possible in practical CR environment. Energy detectors are blind and simplest to use but perform poorly at low SNRs. On the other hand, cyclostationary feature detectors [45, 26] use the inherent cyclic properties of the received signals. Cyclostationary feature detectors do not suffers from the noise uncertainty as they are able separate signal from noise by exploiting cyclic features of the modulated signals. Therefore, this detector is more robust than the energy detectors in this regard. The choice of detector to be considered in a CR depends on the required performance versus complexity matrix. In addition, cooperative spectrum sensing was introduced as a means of increasing the sensing accuracy by addressing the hidden terminal problems faced by wireless networks in [39, 40, 58, 121, 38, 105, 19]. In the following subsection, these spectrum sensing methods are briefly discussed.

2.3.1.1 Energy Detection

Energy detection, also known as radiometry or priodogram, is the most popular tech-nique to detect signals due to its lowest computational cast and simplest implemen-tation. Energy detector is used when the amplitudes of the received PU’s signals

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Figure 2.6: Energy detection block diagram

are unknown. The implementation steps of the energy detector is depicted in Fig. 2.6. First, the received signal is passed through a noise pre-filter and after that, it is squared and integrated to measure the energy contents. Then measured energy is compared against a predefined threshold to take the decision about the presence of the signal in particular band. The problem of the energy detection of unknown deter-ministic signals was explored in [4] by Urkowitz. Detailed mathematical analysis of the energy detector was presented in [4] to evaluate the probability of detection and probability of false alarm for noisy signals.Few years later, Kostylev [55] revisited the problem of energy detection and presented the analyses for noisy signals effected by different kinds of fading, such as Ricean, Rayleigh and Nakagami. The disadvantage of this spectrum sensing technique is it cannot distinguish between different types of signals, therefore, it can not be used for spectrum characterization. Moreover, energy detectors have poor performance under low SNR and can not detect the weak signals.

2.3.1.2 Matched Filtering Detection

The matched filter detector is the optimal method to detect any signal [87, 46, 53, 122, 106] because it maximizes the received SNR at receiver. Matched filter requires to demodulate the PU’s signal in a CR network which would require for a CR to have prior signal information of the PU’s at both PHY and MAC layers [11]. This informa-tion may include, such as modulainforma-tion technique, pilots, spreading codes, preambles and packet format. The matched filter based sensing is performed by correlating the observed signal with the known sample to detect the presence of the PU. The main advantage of this detection technique is, it requires less time and few samples of the received PU’s signal. However, this technique requires a dedicated receiver struc-ture for PU which may not be possible in a CR networks. The matched filter based detection technique is depicted in Fig. 2.7.

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Figure 2.7: Matched filter detection block diagram

2.3.1.3 Cyclostationary Feature Detection

Communication signals usually imposed on carrier sine waves, spreading code or hoping sequences which causes built-in periodicity. Such signals are known as cyclo-stationary, because their mean and autocorrelation exhibits periodicity [41, 42, 44]. Cyclostationary feature detection used this periodicity to detect and differentiate the modulated signals in noise and interference. The cyclostationary based detection al-gorithms utilize the cyclostationary of the modulated signals by detecting spectral peaks in spectral correlation function [44]. Unlike energy detectors which relay on power spectral density, a real valued, one dimensional transform, the spectral correla-tion funccorrela-tion is two dimensional transform which is complex valued. Cyclostacorrela-tionary detector block diagram is depicted in Fig. 2.8.

Figure 2.8: Cyclostationary feature detection block diagram

The received signal is passed first through a noise pre-filter than an N-point dis-crete Fourier transform (DFT) is performed to decompose time domain signal into its frequency contents. After DFT, spectral correlation is taken and averaged over the symbol time T to spectral correlation function. Finally, features are extracted from spectral correlation function in order to detect and classify different type of signals. Cyclostationary feature detectors have been used by several researchers in context of CR due to their good performance at low SNRs, such as, [98, 62, 28] to name a few important ones. In [98], authors used cyclostaionary signatures for detection, network identification and rendezvous in the context of dynamic spectrum access and CR. Authors in [62], proposed an energy efficient collaborative cyclostationary spec-trum sensing technique for CR systems. In [28], a fast and simple implementation scheme is introduced using single-cycle cyclostationary spectrum sensing. Further-more, cooperation and coordination among SUs was studied to improve the detection and classification performance of the proposed detector.

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2.3.2 Signal Classification

After detection and features extraction of active signals, a CR can construct an RF mapping of the on-going activities in order to learn certain characteristics about its RF domain. This RF mapping is considered to accumulate the obtained knowledge about the RF environment over time, which may assist a CR terminal in its future decision-making. Such RF mapping is based on the feature vectors that are extracted from the sensed signals. By using the suitable probabilistic models, the extracted

features can be used to infer certain properties about the RF environment. For

example, if the features are considered to be obtained from a mixture model, then classification methods can be used to identify the RF signals belong to certain wireless

communication system. On the basis of a properly designed signal classification

process, the CR terminal can identify whether a detected signal belongs to a known system (legitimate signals) or to an interferer of jammer (illegitimate signals) [110, 94, 124], which is also the objective of this thesis. This knowledge can used by a CR to decide whether to access or avoid a certain spectral band or channel. Moreover, this information can be used for designing an anti-jamming system in electronic warfare systems.

Signal classification algorithms can be divided into two categories. The Maximum likelihood (ML) algorithms [108, 86, 50, 8] are the optimal classifiers in Bayesian sense. They need the distribution of the incoming samples, and minimize the proba-bility of false classification given a finite number of samples. However, the classifica-tion performance of the ML algorithms depend on uncertainties in the received sig-nal. Further, ML classifiers are complex to implement, because they require to store multi-dimensional probability distribution functions. On the other hand, feature-based (FB) [99, 75, 114] classification algorithms exploit the statistical features of the incoming signal instead of relaying on the complete probability distribution function. FB based algorithms are less sensitive to uncertainties in the received signal model, are less complex to implement, but they are sub-optimal classifiers. However, the loss in classification performance is usually compensated by increasing the sample set.

Designing a FB algorithm, to differentiate the communication signals, relies on the choice of the features selected for classification. In particular, FB classifiers form a higher dimensional space in which signals of distinct features can be separated by a hyperplane. One example of such classifiers is the cumulants classifier [99] where a hierarchical tree is presented to distinguish between one or more types at each stage of classification. Another FB based classifier is Goodness of Fit (GoF) classifiers which use the probability distribution function of a given feature of the received samples.

Wideband cyclostationary spectrum sensing and characterization for cognitive radios