Statistical Theory of Communication

Pre-Requisite: MA206
Contact Hours and Credits: ( 3 -0- 0 ) 3


The subject aims to make the students to understand the statistical theory of telecommunication, which are the basics to learn analog and digital tele-communication.

Topics Covered:

Information measure. Discrete entropy. Joint and conditional entropies. Uniquely decipherable and instantaneous codes. Kraft-Mcmillan inequality. Noiseless coding  theorem. Construction of optimal codes.

DMC. Mutual information and channel capacity. Shannon’s fundamental theorem. Entropy in the continuous case. Shannon-Hartley law.

Binary hypothesis testing. Baye’s, minimax and Neyman-Pearson tests. Random parameter estimation-MMSE,MMAE and MAP estimates. Nonrandom parameters – ML estimation.

Coherent signal detection in the presence of additive white and non-white Gaussian noise.Matched filter.

Discrete optimum linear filtering. Orthogonality principle. Spectral factorization. FIR and IIR Wiener filters.

Course Outcomes:

Students are able to

  • CO1: Show how the information is measured and able to use it for effective coding.
  • CO2: Summarize how the channel capacity is computed for various channels.
  • CO3: Use various  techniques involved in basic detection and estimation theory  to solve the  problem.
  • CO4: Summarize the applications of detection theory in telecommunication.
  • CO5: Summarize the application of estimation theory in telecommunication.

Text Books:

  • R.B. Ash, Information Theory, Wiley,1965.
  • M.D. Srinath, P.K. Rajasekaran & R. Viswanathan, Statistical Signal Processing with Applications, PHI 1999.

Reference Books:

  • H.V. Poor : An Introduction to Signal Detection and  Estimation,(2/e),  Spring Verlag.1994.
  • M. Mansuripur :  Introduction to Information  Theory,  Prentice  Hall.1987.
  • J.G. Proakis et al : Digital Signal Processing, (4/e), Pearson Education, 2007.