Digital Information Representation and Transmission

The course introduces students to the fundamental principles of digital communications and information theory, covering key concepts such as entropy, channel capacity, data compression, and error correction techniques (Huffman, Shannon–Fano–Elias, Hamming, Reed–Muller), as well as digital modulation methods (PCM, Manchester). It also examines the Fourier Transform, ideal filters, and the impact of noise on channel performance. Furthermore, the course extends to the analysis and evaluation of physical transmission media (cables, optical fibers, radio waves, microwaves), along with the principles and protocols governing wired and wireless networks (Ethernet, IEEE 802.11, PPP).

Upon successful completion of the course, students will be able to:

• Understand the fundamental principles of digital communications, including the concepts of information, entropy, communication channels, noise, and channel capacity.
• Apply the Shannon–Hartley and Nyquist Theorems to calculate and analyze channel performance.
• Apply data compression and coding techniques (Huffman, Shannon–Fano–Elias) as well as error correction codes (Hamming, Reed–Muller).
• Use the Fourier Transform and ideal filters for signal analysis and processing.
• Analyze the performance of digital communication channels and propose solutions that maximize capacity and minimize errors.
• Interpret and apply digital modulation techniques (PCM, Manchester, and others) to improve data transmission.
• Design and develop digital modulation systems for modern communication networks.
• Identify and evaluate physical transmission media (cables, optical fibers, radio waves, microwaves) and select optimal solutions for different communication scenarios.
• Understand the basic operating principles of wireless networks and optical fiber networks.
• Recognize and select appropriate network protocols (Ethernet, IEEE 802.11, PPP) for the efficient operation of communication systems.

1. Introduction and Information Theory: Historical evolution of digital communications and Information Theory. Review of basic knowledge from algebra and probability. Introduction to the Fourier Transform.

2. Entropy and Stochastic Processes: Second law of thermodynamics, entropy, relative entropy, entropy and stochastic processes.

3. Communication Channels and Capacity: Types of communication channels. Noise, rate, loss, channel capacity.

4. Data Compression: Shannon’s Fundamental Theorem, optimal codes, Huffman coding, Shannon–Fano–Elias coding.

5. Error Correction: Error correction codes. Hamming codes.

6. Signal Encoding Techniques: PCM, Manchester.

7. Signals and Noise – Introduction to the Fourier Transform
8. Fourier Transform and Ideal Filters
9. Introduction to Signal Modulation
10. Digital Signal Modulation
11. Sampling and the Nyquist Theorem
12. Physical Transmission Media: Cables, optical fibers, wireless networks (radio waves, microwaves, antennas).

13. Network Protocols: Ethernet, IEEE 802.11, PPP.

[1] William Stallings. Επικοινωνίες Υπολογιστών & Δεδομένων. Θεσσαλονίκη, Ελλάδα: Εκδόσεις Τζιόλα & Υιοί, 2018. ISBN: 978-960-418-814-7

[2] Νικόλαος Αλεξανδρής, Βασίλειος Χρυσικόπουλος, Κωνσταντίνος Πατσάκης. Εισαγωγή στη Θεωρία Πληροφοριών, Κωδίκων και Κρυπτογραφίας. Αθήνα, Ελλάδα: Εκδόσεις Βαρβαρήγου, 2015. ISBN: 978-960-7996-39-8.

[3] David G. Luenberger. Θεωρία της Πληροφορίας. Αθήνα, Ελλάδα: Εκδόσεις Παπασωτηρίου, 2011. ISBN: 978-960-491-020-5.

Mode of Delivery: Face-to-face

Teaching Organization:

• Slides for the teaching of the theoretical part, available through the e-class platform.
• Laboratory guides, available through the e-class platform.

• Final examination at the end of the teaching period: written or oral.
• Laboratory assignment.