ELEC5507: Semester 1, 2021

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Units ELEC5507 Semester 1 2021 [Normal day]

Unit outline_

ELEC5507: Error Control Coding

Semester 1, 2021 [Normal day] - Camperdown/Darlington, Sydney

Overview

This unit deals with the principles of error control coding techniques and their applications in various communication and data storage systems. Its aim is to present the fundamentals of error control coding techniques and develop theoretical and practical skills in the design of error control encoders/decoders. Successful completion of this unit will facilitate progression to advanced study or to work in the fields of telecommunications and computer engineering. It is assumed that the students have some background in communications principles and probability theory. The following topics are covered: Introduction to error control coding, Linear algebra, Linear block codes, Cyclic codes, BCH codes, Reed-Solomon codes, Applications of block codes in communications, Convolutional codes, Viterbi algorithm, Applications of convolutional codes in communications, Soft decision decoding of block and convolutional codes, LDPC codes, Turbo codes, MIMO and rateless codes.

Unit details and rules

Academic unit	School of Electrical and Computer Engineering
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	Fundamental mathematics including probability theory and linear algebra. Basic knowledge on digital communications. Basic MATLAB programming skills is desired.
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Yonghui Li, yonghui.li@sydney.edu.au
Lecturer(s)	Yonghui Li, yonghui.li@sydney.edu.au

Type	Description	Weight	Due	Length
Participation	Tutorial performance	10%	Multiple weeks	n/a
Outcomes assessed:
Assignment	Project individual assignment + group project with final deliverable in week 12	90%	Progressive	n/a
Outcomes assessed:
= group assignment ?

Assessment summary

Detailed information for each assessment can be found on Canvas. The final exam has been replaced by project assignment, which includes an individual assignment and group project.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name	Mark range	Description
High distinction	85 - 100
Distinction	75 - 84
Credit	65 - 74
Pass	50 - 64
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction to error control coding	Online class (2 hr)
Week 02	Binary field, vector space and linear block codes	Online class (2 hr)
Week 03	Decoding of binary linear block codes	Online class (2 hr)
Week 04	Cyclic codes	Online class (2 hr)
Week 05	BCH codes	Online class (2 hr)
Week 06	Decoding of BCH codes and Reed-Solomon codes	Online class (2 hr)
Week 07	Burst error correcting codes and soft decision decoding	Online class (2 hr)
Week 08	Convolutional codes	Online class (2 hr)
Week 09	Viterbi decoding of convolutional codes	Online class (2 hr)
Week 10	Trellis Coded Modulation (TCM)	Online class (2 hr)
Week 11	Low Density Parity Check (LDPC) codes	Online class (2 hr)
Week 12	Review	Online class (2 hr)
Week 13	Project presentation	Online class (2 hr)

Attendance and class requirements

Students need to attend all class sessions. The attendance will be recorded.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

There are no specific prescribed readings for this unit

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. demonstrate proficiency in knowledge development on the specific topic of error control coding using open literature to keep up to date with new advancements
LO2. write reports to communicate complex and often technical information on error control systems, using clear and concise language at a level commensurate with the expected stakeholder knowledge and interest
LO3. work in a team by defining clear responsibilities, drawing on the knowledge and skills of others, and showing initiative by contributing constructively towards the delivery of a solution for a specific engineering problem
LO4. design and evaluate error control coding schemes using probability theory to the extent of the material presented
LO5. demonstrate an understanding of various error control coding techniques and their applications in telecommunication and data storage systems
LO6. analyse error control schemes using principles and techniques developed to identify bottlenecks and optimize performance
LO7. demonstrate an understanding of the theoretical and practical skills in the design of error control encoders and decoders.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

No changes have been made since this unit was last offered

Additional information

There is no other relevant information for this unit

Additional costs

There are no additional costs for this unit

Site visit guidelines

There are no site visit guidelines for this unit

Work, health and safety

There are no specific work health and safety requirements for this unit

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Additional costs

Site visit guidelines

Work, health and safety

Disclaimer

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