This course provides the fundamental theory of the basic building blocks that exist in all communication systems. Textbook: B. P Lathi and Z. Ding, Modern Digital and Analog Communication Systems, 4th Edition, Oxford University Press, 2009.

• Review of signals and systems

• Modulation/Demodulation

• Review of probability theory and random processes

• Analog Communication in the presence of noise

• Digital signals

• Digital Communication in the presence of noise

   The principal objective of this course is to develop in each student an understanding of the fundamental principles behind mobile wireless communications. Textbook:T. S. Rappaport, Wireless Communications --- Principles and Practice, Prentice Hall, 2nd edition, 2001.

• Wireless channel characterization
      o Large scale fading – Path loss and shadowing
      o Small scale fading – Multipath and Doppler shift

• Techniques to combat fading
      o The effects of fading on digital communications
      o Modulation, coding and diversity techniques
      o Equalization

• Modulation techniques
      o Analog modulation
      o Digital modulation
      o Spread spectrum modulation
      o Modulation performance in fading channels

• Advanced topics
      o CDMA
      o OFDM
      o MIMO
      o Cognitive Radio

   Information fusion involves combining information from multiple sources or sensors to achieve inferences not possible using a single sensor or source. This course provides an introductory treatment of the fundamentals of statistical inference in a distributed framework. Applications range from military applications such as battle management, target tracking, automated threat assessment, to nonmilitary applications including environmental monitoring, medical diagnosis, monitoring of complex machines and robotics. In these applications, observations are processed in a distributed manner and, either inferences are made at the distributed processors or processed data (compressed observations) are conveyed to a fusion center that makes a global decision. Topics to be covered include:

• Introduction of the JDL data fusion model

• Review of detection and estimation theory

• Distributed signal processing and information fusion

• Fusion architectures

• Distributed detection

• Distributed estimation of a parameter

• Distributed estimation in dynamic systems

• Management in distributed systems

   This course introduces basic principles of probability, random variables and random processes to deal with stochastic systems involving random processes and noise through mathematical analysis and computer simulations. Textbook: H. Stark and J. W. Woods, Probability and Random Processes with Applications to Signal Processing, 3rd Edition, Prentice Hall, 2002.

• Probability and Random Variable

• Functions of Random Variables

• Expectation and Estimation

• Multiple Random Variables or Random Vectors

• Random sequences

• Random processes

• Applications to Statistical Signal Processing