- Datum:Startar 25 maj 2023, 13:00Slutar 25 maj 2023, 14:00
- Språk:Svenska och engelska
Opponent: Charlie Dahlberg
Examinator: Alexandre Graell I Amat
In the ever-evolving landscape of communication systems, the primary objective is to ensure efficient and reliable transmission of information across a physical medium. commonly referred to as the channel. The pioneering work of Claude E. Shannon showed that channel coding can harness the full potential of information transfer, enabling the attainment of channel capacity. At a drawback of added redundancy, channel coding enables correction of errors that occurs during transmission due to the conditions of wireless transmission and non-ideal hardware.
A coding scheme extensively used in digital communication is low-density parity-check (LDPC) codes that provides near-optimal error-correction at low complexity. The most common decoding algorithm for LDPC codes is the iterative belief propagation algorithm, also known as sum-product algorithm This iterative process involves exchanging messages between the variable nodes (representing the received bits) and the check nodes (representing parity-check constraints) until convergence. These messages usually in the form of log-likelihood ratios (LLR), e.g. information about the probability of a the decoded bit being either a 0 or a 1.
Typically, the LLR calculation assumes an additive white Gaussian noise (AWGN) channel. However, due to non-ideal hardware there may be non-Gaussian impairments affecting the received signal, such as residual phase noise due to imperfect estimation. This thesis investigates the performance of LDPC codes if the LLR calculation is extended with the information of phase noise over a single-input single-output (SISO) and a $2\times2$ multiple-input multiple-output (MIMO) channel. The result showed an increase in performance compared to the conventional LLR calculation. The implementation was also realised in hardware to estimation the cost of the extension.
Theodor, Simon and Alexandre