This picture showsSebastian Cammerer

M. Sc.

Sebastian Cammerer

Research staff
Institute of Telecommunications

Contact

+49 711 685-67941

Pfaffenwaldring 47
70569 Stuttgart
Germany
Room: 2.344

Subject

Channel Coding: Spatially Coupled LDPC Codes

Nowadays, block LDPC codes are widely used as forward error correction (FEC) in several important standards such as the DVB-S2, the WiMAX and the IEEE 802.11n standard. LDPC codes are usually decoded by the low-complexity belief-propagation algorithm (BP). In general this decoder is very powerful and simple to construct for every arbitrary LDPC code. It allows to build a soft-in/soft-out (SOSI) decoder with a highly parallel architecture. However, the practical codes with respect to hardware complexity, number of decoding iterations and the finite-length performance suffer from a gap between the BP performance and the maximum a posteriori (MAP) performance, which results in a gap to the channel capacity.
 
Spatially coupled low-density parity-check (SC-LDPC) codes can achieve the channel capacity under low-complexity belief propagation (BP) decoding, which means they reach the MAP performance under low-complexity BP decoding. These codes usually show a very low error floor, which makes them a promising candidate for modern communication standards. For practical finite coupling lengths however, there is a non-negligible rate-loss because of termination effects.

In order to simulate such low error probabilities, we currently develop a simulation cluster based on graphic cards (GPU) and the Nvidia CUDA programming language. This setup allows to simulate an arbitrary LDPC code with bit error rates (BER) up to 10 -9 - 10 - 10 within a feasible period of time.

 
One goal of my research is to investigate and understand the effects of spatial coupling and use this knowledge to design codes close to the capacity with very good properties and a low decoding complexity.

  

  1. 2019

    1. A. Elkelesh, M. Ebada, S. Cammerer, L. Schmalen, and S. ten Brink, “Decoder-in-the-Loop: Genetic Optimization-Based LDPC Code Design,” IEEE Access, vol. 7, pp. 141161–141170, 2019 [Online]. Available: https://ieeexplore.ieee.org/document/8846017
    2. A. Elkelesh, M. Ebada, S. Cammerer, and S. t. Brink, “Decoder-Tailored Polar Code Design Using the Genetic Algorithm,” IEEE Transactions on Communications, vol. 67, no. 7, pp. 4521–4534, 2019 [Online]. Available: https://ieeexplore.ieee.org/document/8680016
    3. M. Ebada, S. Cammerer, A. Elkelesh, and S. ten Brink, “Deep Learning-Based Polar Code Design,” in 2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2019, pp. 177–183 [Online]. Available: https://ieeexplore.ieee.org/document/8919804
    4. A. Elkelesh, M. Ebada, S. Cammerer, and S. t. Brink, “Genetic Algorithm-based Polar Code Construction for the AWGN Channel,” in SCC 2019; 12th International ITG Conference on Systems, Communications and Coding, 2019, pp. 1–6 [Online]. Available: https://ieeexplore.ieee.org/document/8661304
    5. X. Wang, S. Cammerer, and S. Ten Brink, “Near-Capacity Detection and Decoding: Code Design for Dynamic User Loads in Gaussian Multiple Access Channels,” IEEE Transactions on Communications, vol. 67, no. 11, pp. 7417–7430, 2019 [Online]. Available: https://ieeexplore.ieee.org/document/8804231
    6. S. Cammerer, X. Wang, Y. Ma, and S. t. Brink, “Spatially Coupled LDPC Codes and the Multiple Access Channel,” in 2019 53rd Annual Conference on Information Sciences and Systems (CISS), 2019, pp. 1–6 [Online]. Available: https://ieeexplore.ieee.org/document/8692899
    7. M. Widmaier, M. Arnold, S. Dorner, S. Cammerer, and S. ten Brink, “Towards Practical Indoor Positioning Based on Massive MIMO Systems,” in 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall), 2019, pp. 1–6 [Online]. Available: https://ieeexplore.ieee.org/document/8891273
  2. 2018

    1. K. Klaiber, S. Cammerer, L. Schmalen, and S. t. Brink, “Avoiding Burst-like Error Patterns in Windowed Decoding of Spatially Coupled LDPC Codes,” in 2018 IEEE 10th International Symposium on Turbo Codes  Iterative Information Processing (ISTC), 2018, pp. 1–5 [Online]. Available: https://ieeexplore.ieee.org/document/8625312
    2. A. Elkelesh, M. Ebada, S. Cammerer, and S. ten Brink, “Belief Propagation List Decoding of Polar Codes,” IEEE Communications Letters, vol. 22, no. 8, pp. 1536–1539, 2018 [Online]. Available: https://ieeexplore.ieee.org/document/8396299
    3. S. Dörner, S. Cammerer, J. Hoydis, and S. t. Brink, “Deep Learning Based Communication Over the Air,” IEEE Journal of Selected Topics in Signal Processing, vol. 12, no. 1, pp. 132–143, 2018 [Online]. Available: https://ieeexplore.ieee.org/document/8214233
    4. A. Felix, S. Cammerer, S. Dörner, J. Hoydis, and S. Ten Brink, “OFDM-Autoencoder for End-to-End Learning of Communications Systems,” in 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2018, pp. 1–5 [Online]. Available: https://ieeexplore.ieee.org/document/8445920
    5. S. Schibisch, S. Cammerer, S. Dörner, J. Hoydis, and S. ten Brink, “Online Label Recovery for Deep Learning-based Communication through Error Correcting Codes,” in 2018 15th International Symposium on Wireless Communication Systems (ISWCS), 2018, pp. 1–5 [Online]. Available: https://ieeexplore.ieee.org/document/8491189
    6. S. Cammerer, M. Ebada, A. Elkelesh, and S. ten Brink, “Sparse Graphs for Belief Propagation Decoding of Polar Codes,” in 2018 IEEE International Symposium on Information Theory (ISIT), 2018, pp. 1465–1469 [Online]. Available: https://ieeexplore.ieee.org/document/8437581
  3. 2017

    1. A. Elkelesh, S. Cammerer, M. Ebada, and S. ten Brink, “Mitigating clipping effects on error floors under belief propagation decoding of polar codes,” in 2017 International Symposium on Wireless Communication Systems (ISWCS), 2017, pp. 384–389 [Online]. Available: https://ieeexplore.ieee.org/document/8108145
    2. T. Gruber, S. Cammerer, J. Hoydis, and S. t. Brink, “On deep learning-based channel decoding,” in 2017 51st Annual Conference on Information Sciences and Systems (CISS), 2017, pp. 1–6 [Online]. Available: https://ieeexplore.ieee.org/document/7926071
    3. S. Dorner, S. Cammerer, J. Hoydis, and S. ten Brink, “On deep learning-based communication over the air,” in 2017 51st Asilomar Conference on Signals, Systems, and Computers, 2017, pp. 1791–1795 [Online]. Available: https://ieeexplore.ieee.org/document/8335670
    4. S. Cammerer, T. Gruber, J. Hoydis, and S. ten Brink, “Scaling Deep Learning-Based Decoding of Polar Codes via Partitioning,” in GLOBECOM 2017 - 2017 IEEE Global Communications Conference, 2017, pp. 1–6 [Online]. Available: https://ieeexplore.ieee.org/document/8254811
  4. 2016

    1. A. Elkelesh, M. Ebada, S. Cammerer, and S. ten Brink, “Improving Belief Propagation decoding of polar codes using scattered EXIT charts,” in 2016 IEEE Information Theory Workshop (ITW), 2016, pp. 91–95 [Online]. Available: https://ieeexplore.ieee.org/document/7606802
    2. S. Cammerer, V. Aref, L. Schmalen, and S. ten Brink, “Triggering wave-like convergence of tail-biting spatially coupled LDPC codes,” in 2016 Annual Conference on Information Science and Systems (CISS), 2016, pp. 93–98 [Online]. Available: https://ieeexplore.ieee.org/document/7460483
    3. S. Cammerer, L. Schmalen, V. Aref, and S. ten Brink, “Wave-like decoding of tail-biting spatially coupled LDPC codes through iterative demapping,” in 2016 9th International Symposium on Turbo Codes and Iterative Information Processing (ISTC), 2016, pp. 121–125 [Online]. Available: https://ieeexplore.ieee.org/document/7593089

Winter term 2015/2016: Exercises Modern Error Correction

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