Toggle navigation
ComNets
Chair
Staff
Startups
Contact
Route Description
Campus Map and Building Plan
Impressum and Legal Notices
Research
Projects
Publications
Books
Book Chapters
Journals
Conferences
Patents
Teaching
Modules
Lectures
Communication Networks 1
Communication Networks 2
Communication Networks 3
Statistik 1
Statistik 2
Cooperative Communication Systems
Practical Implementations of Network Coding
Advanced Topics on Signal Processing at TUM
Nachrichtenverkehrstheorie – Traffic Theory
ICT for Smart Grids
Problem Based Learning
Oberseminar Kommunikationsnetze
Hauptseminar Kommunikationssysteme
Mikrorechentechnik 1
Mikrorechentechnik 2
PhD seminar Advanced Topics ComNets
Elektronische Medien
Student Theses
Software Defined Networks & Network Functions Virtualization
Software Defined Radio
Network Coding
Wireless Meshed Networks
Distributed Storage and Cloud Solutions
Information-Centric Networking
Artificial Intelligence/Machine Learning (AI/ML)
Compressed Sensing
High-Altitude Platforms
Quantum Communication Networks
Cellular Mobile Communication
Robotics
Post-Shannon Communication
Guesswork
Radio Security
Molecular Communications
Materials and Tools
Activities
Talks
Demonstrations
Tutorials
Panels
Organizer
Summer/Winter School
Girls Day
Standardisation
Awards
Videos
Gallery
Editorial
Open Topics in the Area of Guesswork
Guessing Noise to Decode Messages
(Supervisor:
Juan Cabrera
)
An ideal channel decoder would implement a maximum likelihood decoding technique to guess what message was transmitted. This is guessing which codeword was sent by maximizing the probability of receiving the obtained message. Because this is computationally complex, channel codes are designed backward. I.e., the design of a low-complexity decoder comes first followed by the encoder. This limits the type of codes that can be used because not all codes can be decoded in practical time. However, researchers from MIT and Maynooth University have proven that by guessing the noise in the transmission channel instead of the message you can obtain similar results to a maximum likelihood decoder. The mathematical proof is complicated, yet the principle of operation is quite simple: If you receive a stream of bits that is not a valid codeword, you can flip one bit and ask if the new codeword is a valid one. If it is not, flip a different bit and repeat the process. If the probability of an error bit is low, then with a few flips and questions it is possible to decode. This opens the door to new codes since the decoding process is universal and potentially independent of the code used. We want to implement these novel techniques into our wireless system. To do that, we want to use Software Defined Radio to build the wireless channel and benchmark the novel decoder with state of the art codecs.
Starting time: Immediate
Student and Diploma/Master thesis (with task extension)
Required skills: Python, basic knowledge of digital communication (digital modulation, CDMA, OFDM)
Motivation Links:
https://youtu.be/xQVm-YTKR9s
https://youtu.be/1bgC3AjCnA4