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Syllabus for

Academic year
ESS140 - Digital communications
 
Owner: EMAST
6,0 Credits (ECTS 9)
Grading: TH - Five, Four, Three, Not passed
Level: C
Department: 32 - ELECTRICAL ENGINEERING


Teaching language: English

Course module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 No Sp
0101 Examination 6,0 c Grading: TH   2,5 c 3,5 c   19 Dec 2006 pm V,  Contact examiner

In programs

TDATA COMPUTER SCIENCE AND ENGINEERING - Communications System, Year 4 (elective)
TTFYA ENGINEERING PHYSICS, Year 4 (elective)
TITEA SOFTWARE ENGINEERING, Year 4 (elective)
TITEA SOFTWARE ENGINEERING, Year 3 (elective)
TELTA ELECTRICAL ENGINEERING, Year 4 (elective)
EMAST MSc PROGR. IN DIGITAL COMMUNICATION SYSTEMS AND TECHNOLOGY, Year 1 (compulsory)

Examiner:

Professor  Erik Ström



Eligibility:

For single subject courses within Chalmers programmes the same eligibility requirements apply, as to the programme(s) that the course is part of.

Course specific prerequisites

The course presumes that the participants have knowledge of probability theory, random processes, and signal and system theory. Furthermore, knowledge of communication on a system level is useful. This knowledge can, for instance, be obtained by passing the following courses: TMA421 Stokastiska processer E, ESS010 Signaler och system, and ESS165 Kommunikationssystem.

Aim

In this course, we will be concerned with the design of a system that transfers information from point A over a physical channel to point B. Of course, we would like to do this at the lowest possible cost, but at the same time we must ensure that the quality of the information transfer is acceptable.

Several questions immediately come to mind when reading the above paragraph. What is meant by information? How is the cost calculated? How is quality defined and measured? What design trade-offs can be made?

The objective of this course is to answer these questions.

After the course, the students should be able to
--explain the purpose of each of the main blocks (source encoder/decoder, channel encoder/decoder, modulator/demodulator) in the Shannon communication model
--elaborate on the trade-off between transmitted signals' power and bandwidth in order to reach a certain error performance of the communication link (spectral and power efficiency are key concepts here)
--to compute or estimate the symbol and bit error probability for transmission over linear AWGN channels using several modulation methods (e.g., PAM, PSK, DPSK, QAM, coherent and noncoherent FSK)
--design the signal waveforms and receiver filter for ISI-free transmission over (known) linear channels
--be aware of the concept of channel equalization
--explain the advantages and disadvantages of block and convolutional channel coding, define and compare some major decoding methods (syndrome, bounded-distance, Viterbi decoding), and estimate the error performance for channel-coded systems.

Content

In short, this course will cover the Shannon communication model, signal space representation of deterministic and random signals, ML and MAP detection, bit and symbol error probability computations, union bound techniques, matched filtering, additive white Gaussian noise channels, linear channels, baseband modulation, intersymbol interference, Nyquist pulses, bandpass modulation, coherent and noncoherent detection, and block and convolutional error control coding.

These concepts will be introduced in more detailed below.

The communication system is a called a digital communication system if it transfers digital information (bits), as opposed to analog signals. However, the bit stream may very well represent information from analog sources, e.g., speech or music. The procedure by which the analog waveforms are represented with bit streams is called source coding. Source coding will not be covered here. Instead, this important topic is deferred to the course ESS155 Data Compression.

The channel is often outside the control of the communication engineer. The channel is basically the medium that nature has provided us to communicate over. Typical media could be copper wires, coaxial cables, optical fibers, or space in which radio waves can propagate. Common to most channels is that they can only convey analog signals. We must therefore replace the bit streams with analog waveforms prior to transmission, a procedure called modulation. We will study several modulation methods, e.g., pulse-amplitude modulation, phase-shift keying, frequency-shift keying, and quadrature amplitude modulation.

To keep our design and analysis general, we will quickly replace the actual physical channels with models. The channels will distort the transmitted signal and add noise and other interfering signals to it. The channel model must capture these effects, and there exists several channel models with varying accuracy and complexity. We will mainly be using the additive white Gaussian noise and linear channel models.

The task of the receiver is to decide which bits that were most probably sent by the transmitter and to deliver those bits to the user. Naturally, a good receiver should minimize the bit error probability, and the best receiver in this respect is the maximum a posteriori (MAP) receiver. In many situations, the MAP receiver and the maximum likelihood (ML) receivers are equivalent and are easily interpreted in a signal space setting. For additive white Gaussian noise channels MAP and ML receivers can be implemented by processing the received signal with a matched filter.

Signal space techniques are very useful here since they permit the designer to view the communication signals and noise as vectors in a linear space. As an example, error probability calculations are greatly facilitated by this. Nevertheless, the error probability may be very difficult to compute, and we have to resort to bounding the error probability with, e.g., union bound techniques.

For bandlimited channels, the channel filter will distort the transmitted signal, and this results in that consecutive symbols will interfere with each other. This is called intersymbol interference (ISI) and may be a very serious problem in some applications. The ISI can be completely eliminated if the convolution of the transmitter, channel, and receiver impulse responses is a so-called Nyquist pulse. When it is impossible or impractical to remove the ISI by the selection of transmitter and receiver filter, we can still combat the ISI with a channel equalizer.

Prior to detection, we must synchronize the receiver to the clock and oscillator used by the transmitter. Depending on the modulation format, it may or may not be necessary to estimate the phase of the received signal. A coherent receiver is a receiver that requires an estimate of the phase, and a noncoherent receiver does not.

The cost involved in the communication can be quantified in terms of the amount of hardware and software required, and by the resources consumed by the system. Of course, we would like to use as simple hardware and software as possible. Moreover, we may also want to minimize the power and bandwidth of the transmitted signal. We want low transmitter power to enhance battery life and reduce interference to other systems and potential biological and health effects. All wireless systems use a shared medium: the frequency spectrum that is suitable for radio transmission. Hence, it is important to conserve this natural resource. Many more details on how to design efficient digital radio systems are covered in the course ESS036 Wireless Communications.

To reduce the power or bandwidth requirements, it is often necessary to protect the transmitted data with error-control codes. There are mainly two forms of error-control codes: block codes (e.g., Hamming, BCH, and Reed-Solomon codes) and convolutional codes. Convolutional codes are usually decoded with a Viterbi algorithm, which also can be used for other purposes, e.g., channel equalization.

Organisation

The course contains lectures, exercises (problem solving sessions), and projects.

The main objective with the lectures is to present, motivate and interpret the theory of digital communications. Exercises are useful develop hands-on problem solving skills. To transform the theory into actual design skills, the course will contain several small design projects.

Literature

John G. Proakis and Masoud Salehi. Communication Systems Engineering, Second Edition. Prentice-Hall, Upper Saddle River, NJ, USA, 2001.

Examination

Approved projects is necessary for a passing grade. The actual grades (3, 4, or 5) are set by adding the project and written exam results. The written exam is optional.


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