The course aims to provide students with specific knowledge about advanced signal processing methodologies, both in telecommunication and radar, including software radio techniques. In particular, during the course the student will learn how to connect the different functional blocks within a complex system of analysis and processing in a single framework of integrated and interdependent processes. Moreover, students will have the opportunity to learn how to use some Software Defined Radio (SDR) devices for the reception and transmission of signals and software tools for their management. The course will provide an overview of some typical systems for processing and transmission of telecommunications signals, also with reference to radar technologies, briefly describing both basic operational concepts and typical application examples.
teacher profile teaching materials
Basic concepts, software requirements and reconfigurability.
Benefits, ideal architecture and definitions.
2. RF implementation issues:
Receiver front-end, characteristics and topologies, transmitter architecture.
Noise and distorsions in the RF chain.
Link-budget analysis and high-level requirements.
3. Analog to digital conversion and related issues:
Quantization noise, jitter and aperture uncertainty.
Performance requirements of the hardware devices, DSP and FPGA.
4. Spectral analysis:
Continuous-time signal sampling, DFT and FFT.
Pass-band sampling, Hilbert transform, envelope and analytic signal.
Spectral analysis and non-parametric estimation of the power spectrum (periodogram, Bartlett method, Welch method, ...).
Use of FFT in spectrum estimation.
Time-frequency signals analysis.
Introduction to Matlab.
Excercises in Matlab (spectral estimation).
Excercises in Matlab (time-frequency signals analysis).
5. Signals modulation:
Overview on analog and digital modulations.
Excercises in Matlab (modulations).
6. SDR devices:
Architecture and operating scheme of the devices RTL-SDR and Analog-Device Adalm-Pluto.
Introduction to the use of Matlab, Simulink and GNURadio for SDR applications.
SDR laboratory (use of the devices RTL-SDR and Analog-Device Adalm-Pluto for signals acquisition).
7. Cognitive Radios and cognitive radio networks:
Basic definitions and concepts, cognitive cycle, spectrum sensing, spectrum mobility, spectrum management, spectrum decision.
Excercises in Matlab (application of spectrum sensing techniques).
8. Cognitive radar:
Radar working principles and basic concepts; introduction to the cognitive radar.
[2] Tuttlebee, Walter HW, ed. Software Defined Radio: Enabling Technologies, John Wiley & Sons, 2003.
[3] John Bard, Vincent J. Kovarik Jr., Software Defined Radio: the Software Communications Architecture, Vol. 6. John Wiley & Sons, 2007.
[4] Stewart, Robert W., et al. Software Defined Radio using MATLAB & Simulink and the RTL-SDR, Strathclyde Academic Media, 2015.
[5] Fabio Rocca, Elaborazione Numerica dei Segnali, Cusl, 2009.
[6] Petre Stoica and Randolph L. Moses, Spectral Analysis of Signals, 2005.
[7] Francois Auger, Patrick Flandrin, Paulo Goncalves, and Olivier Lemoine, Time-Frequency Toolbox for use with MATLAB, 1995-1996.
It will also provided the slides of the lectures and the codes of the exercises carried out during the course.
Programme
1. Introduction to SDR technology:Basic concepts, software requirements and reconfigurability.
Benefits, ideal architecture and definitions.
2. RF implementation issues:
Receiver front-end, characteristics and topologies, transmitter architecture.
Noise and distorsions in the RF chain.
Link-budget analysis and high-level requirements.
3. Analog to digital conversion and related issues:
Quantization noise, jitter and aperture uncertainty.
Performance requirements of the hardware devices, DSP and FPGA.
4. Spectral analysis:
Continuous-time signal sampling, DFT and FFT.
Pass-band sampling, Hilbert transform, envelope and analytic signal.
Spectral analysis and non-parametric estimation of the power spectrum (periodogram, Bartlett method, Welch method, ...).
Use of FFT in spectrum estimation.
Time-frequency signals analysis.
Introduction to Matlab.
Excercises in Matlab (spectral estimation).
Excercises in Matlab (time-frequency signals analysis).
5. Signals modulation:
Overview on analog and digital modulations.
Excercises in Matlab (modulations).
6. SDR devices:
Architecture and operating scheme of the devices RTL-SDR and Analog-Device Adalm-Pluto.
Introduction to the use of Matlab, Simulink and GNURadio for SDR applications.
SDR laboratory (use of the devices RTL-SDR and Analog-Device Adalm-Pluto for signals acquisition).
7. Cognitive Radios and cognitive radio networks:
Basic definitions and concepts, cognitive cycle, spectrum sensing, spectrum mobility, spectrum management, spectrum decision.
Excercises in Matlab (application of spectrum sensing techniques).
8. Cognitive radar:
Radar working principles and basic concepts; introduction to the cognitive radar.
Core Documentation
[1] Paul Burns, Software Defined Radio for 3G, Artech House, 2003.[2] Tuttlebee, Walter HW, ed. Software Defined Radio: Enabling Technologies, John Wiley & Sons, 2003.
[3] John Bard, Vincent J. Kovarik Jr., Software Defined Radio: the Software Communications Architecture, Vol. 6. John Wiley & Sons, 2007.
[4] Stewart, Robert W., et al. Software Defined Radio using MATLAB & Simulink and the RTL-SDR, Strathclyde Academic Media, 2015.
[5] Fabio Rocca, Elaborazione Numerica dei Segnali, Cusl, 2009.
[6] Petre Stoica and Randolph L. Moses, Spectral Analysis of Signals, 2005.
[7] Francois Auger, Patrick Flandrin, Paulo Goncalves, and Olivier Lemoine, Time-Frequency Toolbox for use with MATLAB, 1995-1996.
It will also provided the slides of the lectures and the codes of the exercises carried out during the course.
Reference Bibliography
[1] Slides of the lectures. [2] Matlab codes of the excercises carried out during the course. [3] Paul Burns, Software Defined Radio for 3G, Artech House, 2003. [4] Tuttlebee, Walter HW, ed. Software Defined Radio: Enabling Technologies, John Wiley & Sons, 2003. [5] John Bard, Vincent J. Kovarik Jr., Software Defined Radio: the Software Communications Architecture, Vol. 6. John Wiley & Sons, 2007. [6] Stewart, Robert W., et al. Software Defined Radio using MATLAB & Simulink and the RTL-SDR, Strathclyde Academic Media, 2015. [7] Dispense del Prof. Benedetto (http://host.uniroma3.it/laboratori/sp4te/teaching/sdr.html). [8] Fabio Rocca, Elaborazione Numerica dei Segnali, Cusl, 2009. [9] Petre Stoica and Randolph L. Moses, Spectral Analysis of Signals, 2005. [10] Francois Auger, Patrick Flandrin, Paulo Goncalves, and Olivier Lemoine, Time-Frequency Toolbox for use with MATLAB, 1995-1996. [11] Leon Cohen, Time-frequency analysis, Upper Saddle River, N.J.: Prentice Hall, 1995. [12] F. Hlawatsch and G.F. Boudreaux-Bartels, Linear and Quadratic Time-Frequency Signal Representations, IEEE Signal Processing Magazine, pp. 21-67, april 1992. [13] Proakis, John G., and Masoud Salehi, Digital Communications, Vol. 4. New York: McGraw-Hill, 2001. [14] https://wiki.analog.com/university/tools/pluto [15] https://www.sdr-radio.com/Radios/Pluto [16] M. Lopez-Benitez, A. Umbertand F. Casadevall, Evaluation of Spectrum Occupancy in Spain for Cognitive Radio Applications, IEEE 69th conference on Vehicular Technology (VTC), pp. 1-5, Spring 2009. [17] V. Valenta, R. Marsálek, G. Baudoin, M. Villegas, M. Suarez, and F. Robert, Survey on Spectrum Utilization in Europe: Measurements, Analyses and Observations, Proc. of the 5th Int. Conf. on Cognitive Radio Oriented Wireless Networks Communications (CROWNCOM), pp. 1-5, June 2010. [18] Tanim M. Taher, Roger B. Bacchus, Kenneth J. Zdunek, R. Marsálek, Dennis A. Roberson, Long-Term Spectral Occupancy Findings in Chicago, IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN), 2011. [19] L. Ling et al., A Two Stage Sensing Technique for Dynamic Spectrum Access, IEEE Trans. on Wireless Communications, Vol. 8, No. 6, June 2009. [20] S. Chaudhari et al., Autocorrelation-Based Decentralized Sequential Detection of OFDM Signals in Cognitive Radios, IEEE Trans. on Signal Processing, Vol. 47, pp. 2690-2700. July 2009. [21] Mark A. Richards, James A. Scheer, William A. Holm, Principles of Modern Radar: Basic Principles, SciTech Publishing, 2010. [22] Griffiths, H. D., Christopher Baker, and David Adamy, Stimson’s Introduction to Airborne Radar, Scitech Pub Incorporated, 2014. [23] S. Haykin, Cognitive Dynamic Systems: Perception-Action Cycle, Radar and Radio, Cambridge University Press, 2012. [24] J. R. Guerci, Cognitive Radar: The Knowledge-Aided Fully Adaptive Approach, Artech House, 2010. [25] C. Baker, H. Griffiths, A. Balleri, Biologically Inspired Waveform Diversity”, chapter 6 of the book. [26] F. Gini, A. De Maio, L. Patton, Waveform Design and Diversity for Advanced Radar Systems, IET 2012. [27] L. A. Miller and A. Surlykkej, How Some Insects Detect and Avoid Being Eaten by Bats: Tactics and Countertactics of Prey and Predator, BioScience, Vol. 51 No. 7, July 2001. [28] J. S. Bergin and P. M. Techau, High-Fidelity Site-Specific Radar Simulation: KASSPER ’02 Workshop Datacube, ISL Technical Note, Vienna, May 2002.Type of delivery of the course
Traditional frontal lessons, excercises with the use of softwares such as Mathworks Matlab and GnuRadio, practical excercises with RTL-SDR and Analog-Device Adalm-Pluto devices in the teaching laboratory.Type of evaluation
Evaluation of a project work and an oral discussion. Due to the sanitary emergency caused by the spread of COVID-19, the exam will be oral according to art.1 of DR n°. 703 (5 May 2020).