ELEC E7210 Communication Theory Lectures autumn 2015 Department of Communications and Networking

General information Lectures, Lectures, weeks ; Tuesday , E 111 Wednesday , E 111 Exersices, Exersices, Thursday I 256

Teachers Responsible teacher – Prof. Olav Tirkkonen Current course is partly based on the DTM course lectured by Prof. Tirkkonen Lecturer – Dr. Natalia Ermolova Reception: Monday , E-207, tel Course assistants: MSc Christopher Boyd and MSc Sergio Lembo

Course grading (5 cr) Exam: ( ); ( ), and ( ). and home assignments At the exam, the use of literature is not allowed. Course grade = 0, if the assignment grade=0; 0, if the exam grade=0 0.8x (exam grade) +0.2x (assignment grade), otherwise. Lecture handouts distributed at the course web page

Literature Recommended literature: Recommended literature: Course book: A. Goldsmith, Wireless comminications, Cambridge University Press, Many other books can be chosen for reading based on own taste, e.g. D. Tse and P. Viswanath, Fundamentals of Wireless Communications, Cambridge University Press, J. G. Proakis, 4th ed., Mc-Grow-Hill, S. Haykin and M. Moher, Modern Wireless Communications, Prentice Hall, 2004.

Course overview (1) A course in the Master’s degree program Is acceptable for post-graduate studies

Course overview (2) " The fundamental problem of communication is that of reproducing at one point either exactly or approximately a message selected at another point.” Claude Shannon ( )

Course overview (3) The goal of the course is to present the basics of modern communication theory including the basics of signal theory as well as transmission techniques used in current and next generation wireless communication systems. We will study modern transmiter (Tx)-receiver (Rx) algorithms applied - for overcoming limitations imposed by wireless communication channels - and for improvement of bandwidth, power, and cost efficiencies of wireless communication systems.

Course overview (4) Issues addressed Issues addressed - Basics of theory of radio signals; functions of transceiver; - Basics of theory of radio signals; functions of transceiver; Limitations Limitations imposed by wireless channels : small-scale (multipath) fading and large-scale (shadowing) fading; path loss; Doppler effect; interference. ways of overcoming

Course overview (5) Communication systems are understood in terms of layers: Lowest layers: 1. Physical layer (PHY): physical transmission of bits between the Tx and Rx; 2. Data-link layer: guarantees reliability of the transmitted information frames (e.g. re-transmission); ensures that frames are transmitted without undue interference with other nodes (Medium Access Control (MAC)); performs link adaptation (rate and power control,..) 3. Network layer: routing, quality of service, flow control (avoiding congestion) Focus of this course – PHY of digital communication systems.

Lecture plan (1) 1. Basics of theory of radio signals; functions of transceiver; source and channel coding Limitations imposed by a wireless channel and ways of overcoming 3. Equalization 3. Equalization 4-6. Performance of digital modulation over wireless channels (SISO); adaptive transmission policies 4-6. Performance of digital modulation over wireless channels (SISO); adaptive transmission policies - Error rate performance; - Error rate performance; - Capacity of wireless channels; adaptive power and rate transmission policies

Lecture plan (2) - Coded modulation; adaptive modulation and coding MIMO systems and space-time communications Multiuser systems Dirty RF issues Course review

Basics of theory of radio signals (1) A radio signal is a function of time x(t) (x(n))

Basics of theory of radio signals (2) FT IFT DFT IDFT Matrix form:

Basics of theory of radio signals (3): Basics of theory of radio signals (3): Parseval’s theorem Signal energy can be evaluated across time or frequency For the DFT this relation becomes

Basics of theory of radio signals (4): Laplace Transform Unilateral LT Bilateral (two-sided) LT

Basics of theory of radio signals (5): Sampling Nyquist theorem

Probability theory A real-valued stochastic process : for a specific t, is a random variable with the first-order CDF and the first-order PDF n th-order CDF is the joint CDF of the RVs n th-order PDF discrete- or continuous-state process discrete- or continuous-time process

Stochastic Processes(2) Complex-valued stochastic processses The complex-valued is specified in terms of the joint distribution of Mean: Autocorrelation:

Stochastic Processes(3) Cross-correlation Cross-covariance If, then and are called mutually orthogonal If then and are called uncorrelated

Stochastic Processes (4) is white noise if Complex-valued is stationary if and are jointly stationary, i.e. the joint statistics of and are the same as those of and for ■ WSS, if is a constant, and

Stochastic Processes(5) Power spectrum: Fourier transform from autocorrelation function cyclostationary,if the CDF for any integer m

Stochastic Processes(6) Gaussian (normal) processes is normal if are jointly Gaussian for and n

Transceiver (1) Transmitter+Receiver =Transceiver

Transceiver (2)

Radio Channels input output A radio channel is characterized by an input (discrete, continuous, single, multiple) and output. Generally, it inserts distortions (noise, interference,…) Dynamical system (DS); linear DS are characterized by impulse/frequency responses Memoryless or memory channels(dispersion in time) Examples: -Binary memoryless channels: inputs/outputs are bits, errors; -Binary erasure channels: some bits may be known to have vanished, -AWGN channels; -Fading channels

Source Entropy

Source and channel coding More details in the course on coding theory Source coding: removes the redundancy from the source signal: e.g. ‘sentence’ ‘sntnc’. Encoder+decoder = (source) codec In natural forms, signals generated by physical sources contain redundant information, which is wasteful from the commun. point of view wastes of transmission power and bandwidth. For an efficient transmission, redundant bits must be removed Number of bits needed for describing a discrete source is given by Shannon’s source theorem: Given a discrete memoryless source characterized by a certain amount of entropy, the fundamental limit of compression is given by the entropy of the source.

Information capacity (channel capacity) - Joint source and channel coding