Course Title: Digital Signal Processing

Part A: Course Overview

Course Title: Digital Signal Processing

Credit Points: 12.00


Course Code




Learning Mode

Teaching Period(s)


City Campus


125H Electrical & Computer Engineering


Sem 1 2006,
Sem 1 2008,
Sem 1 2009,
Sem 1 2010,
Sem 1 2012,
Sem 1 2013,
Sem 1 2014,
Sem 1 2015,
Sem 1 2016


City Campus


172H School of Engineering


Sem 1 2017,
Sem 1 2018,
Sem 1 2020

Course Coordinator: Professor Kandeepan Sithamparanathan

Course Coordinator Phone: +61 3 9925 2804

Course Coordinator Email:

Course Coordinator Location: 12.08.18

Course Coordinator Availability: Email for appointment

Pre-requisite Courses and Assumed Knowledge and Capabilities

You are expected to have sound knowledge on the following topics: signal description and analysis in time and frequency domains, Fourier/spectrum analysis, analog and digital filters, z-transforms, fundamentals of communication systems, digital modulation techniques, and signal analysis using the software tool Matlab. You are also expected to have basic computer programming skills to work on the lab exercises.  

Course Description

Digital signal processing is an important aspect in the current era of communication engineering especially in wireless and mobile communication. Though deterministic signals are transmitted to communicate information in reality what we receive actually are corrupted signals that are random in nature. In this course therefore we cover random signal theories and its applications to communication engineering in order to understand the behavior of communication signals and improve the performance of communication systems by performing intelligent signal processing.

The topics covered in this course are;

- A quick recap of Discrete Fourier Transform (DFT), spectral analysis, filter design and windowing functions

- Fundamentals of random signal theory and analysis, and LTI system response to random signals

- Modeling communication signals as random processes

- Baseband signal processing, signal synthesis and filter design for communication

- Statistical signal processing in communication for signal detection and synchronization


Objectives/Learning Outcomes/Capability Development

At the postgraduate level this course develops the following Program Learning Outcomes:

  • High levels of technical competence in the field.
  • Be able to apply problem solving approaches to work challenges and make decisions using sound engineering methodologies
  • Be able to apply a systematic engineering design approach and have strong research and design skills

On successful completion of this course, you will be able to:

1. Analyse signals in communication systems

2. Process random signals to meet a particular requirement

3. Simulate, synthesize and process communication signals using software tools

4. Write signal processing algorithms and methods with minimal supervision and communicate the outcomes as a written report

Overview of Learning Activities

Key concepts, theories and their applications will be explained in lectures considering various case studies in communication engineering.

The laboratory exercises will train students to apply the random signal theories in practical scenarios and develop skills to simulate signals and write algorithms to process signals to improve the performance of communication systems.


Overview of Learning Resources

You will be able to access course information and learning materials through RMIT University’s online systems.

Lists of relevant reference texts, resources in the library and freely accessible Internet sites will be provided.

You will also use state-of-the-art laboratory equipment and computer software within the School during project and assignment work.


Overview of Assessment

This course has no hurdle requirements.

The fundamental theories and concepts of random signal processing will be assessed. All the assessment tasks will assess the ability to critically analyse experimental/analytical results and provide arguments to support the requirements. Written feedback where necessary will be provided on all assessment tasks except for the end of semester test. There will be no Final Exam for this course.


Assessment tasks

Assessment Task 1: End of Semester Written Test

Weighting 50%

This assessment task supports CLOs 1, 2 & 4

Assessment Task 2: Lab-Exam 1

Weighting 15%

This assessment task supports CLOs 1, 2, & 4

Assessment Task 3: Lab-Exam 2

Weighting 20%

This assessment task supports CLOs 1, 2, 3 & 4

Assessment Task 4: Mid Semester Assessment

Weighting 15%

This assessment task supports CLOs 1, 2 & 4