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Interleaver Design of Turbo Codes

Description

The LTE standard adopted a Turbo Code (TC) as channel code. And TCs could remain promising channel coding candidates for 5G. A typical TC is constructed by parallel concatenating two convolutional codes via an interleaver. The design of the interleaver is critical to the performance of the TC, particularly for short frame sizes. They can in general be separated into two classes: random interleavers and deterministic interleavers.  The task of the student is to give an overview of three kinds of deterministic interleavers: Quadratic Polynomial Permutation (QPP) [1], Almost Regular Permutation (ARP) [2] and Dithered Relative Prime (DRP) lnterleavers [3].   References: [1] yle="color: #3070b3; text-decoration: none; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: gray; padding-bottom: 1px; transition: all 188ms ease;" href="http://ieeexplore.ieee.org/document/1377495/" rel="noopener" target="_blank">http://ieeexplore.ieee.org/document/1377495/ [2] yle="color: #3070b3; text-decoration: none; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: gray; padding-bottom: 1px; transition: all 188ms ease;" href="http://ieeexplore.ieee.org/document/1312507/" rel="noopener">http://ieeexplore.ieee.org/document/1312507/ [3] yle="color: #3070b3; text-decoration: none; border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: gray; padding-bottom: 1px; transition: all 188ms ease;" href="http://read.pudn.com/downloads155/doc/project/686322/DRP.pdf" rel="noopener" target="_blank">https://ieeexplore.ieee.org/document/957178

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Learning Aided SC Flip Decoding for Polar Codes

Description

Polar codes achieve the capacity of binary-input discrete memoryless channels asymptotically in the block length under successive cancellation (SC) decoding. Polar codes have been adopted for the control channel in 5G enhanced mobile broadband (eMBB).

Due to the serial nature of SC decoding, an erroneous bit decision can be caused by the channel noise or previous erroneous bit estimates. The main idea of SC flip decoding is trying to correct the first erroneous bit decision by sequentially flipping the unreliable decisions. 

The optimal flipping strategy is considered difficult due to lack of an analytical solution. Alternatively, (deep) learning aided SC flip algorithm are investigated in this thesis.

[1] O. Afisiadis, A. Balatsoukas-Stimming, and A. Burg, “A low-complexity improved successive cancellation decoder for polar codes,” in Proc. 48th Asilomar Conf. Signals, Systems and Computers, pp. 2116-2120, 2014.

[2] L. Chandesris, V. Savin, and D. Declercq, “Dynamic-SCFlip Decoding of Polar Codes,” IEEE Trans. Commun., vol. 66, no. 6, pp. 2333-2345, Jun., 2018.

[3] X. Wang, et al. "Learning to Flip Successive Cancellation Decoding of Polar Codes with LSTM Networks." arXiv preprint arXiv:1902.08394 (2019).

[4] N. Doan, et al. "Neural Dynamic Successive Cancellation Flip Decoding of Polar Codes." arXiv preprint arXiv:1907.11563 (2019).

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Polar Coding with Non-Binary Kernels

Description

This thesis will focus on polar codes with non-binary kernels on GF(q). Some of the following tasks might be covered: 

• Kernel selection
• Decoder implementation
• Efficient construction
• Comparison of binary and non-binary polar codes

Prerequisites

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Adaptive SC-like Decoding for Polar and RM codes

Description

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Channel Estimation with LDPC codes

Description

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Prerequisites

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Student

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Learning-Aided Channel Estimation for Polar Coding

Description

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Student

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Dynamic Successive Cancellation Flip Decoding of Polar Codes

Description

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Student