Signal Processing for communications

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    In brief

  • Number of hours : 4 lectures, 4 sessions of practical work
  • Teaching language : English
  • Teaching method : En présence
  • Code : N9MS01A


Two parts in this course: 1) Introduce theoretical tools for signal processing, 2) Digital signal processing (implementation).


Objectives for the first part (theoretical tools) :

- Understand the different classes of deterministic and random signals with the definitions of the autocorrelation function and the power spectrum density

- Understand the concept of linear filtering and the Wiener Lee relationships

- Understand the principles of sampling and the Shannon theorem

- Understand the interest of non-linear transformations applied to deterministic and random signals and how to apply Price’s theorem


Objectives for the second part (digital signal processing) :

- To be able to correctly sample a signal and to generate simple digital signals.

- To be able to estimate digitally the aucorrelation function and to perform a frequency representation (Fourier transform, Power Spectral Density) of a signal.

- To be able to determine impulse responses for simple filters (Finite Impulse Response, or FIR, filters) and to synthesize them, meaning to choose their parameters to meet some requirements.

- To be able to filter a signal and to analyze the obtained result.


For the first part (theoretical tools) :

- Autocorrelation and power spectral density

- Sampling

- Linear Filtering

- Non-linear transformations and Price’s theorem


For the second part (digital signal processing) :

- Sampling and quantization.

- From theoretical to digital tools for the autocorrelation function and the Fourier transform : what are the approximations to be done ? what are their consequences ?

- Digital filters (FIR and IIR) and FIR synthesis.


- Athanasios Papoulis and S. Unnikrishna Pillai, Probability, Random Variable and Stochastic Processes, McGraw Hill Higher Education, 4th edition, 2002.

 - Simon Haykin and Barry Van Veen, Signal and Systems, Wiley Alan V.  Oppenheim and Ronald W. Schafer, Digital Signal Processing, Prentice-Hall.


Bases on deterministic signals (energy, power, periodicity) 

Random variables and vectors


THOMAS Nathalie

Phone : 2236

Email : Nathalie.Thomas @



  • Toulouse


The National Institute of Electrical engineering, Electronics, Computer science,Fluid mechanics & Telecommunications and Networks

2, rue Charles Camichel - BP 7122
31071 Toulouse Cedex 7, France

+33 (0)5 34 32 20 00


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