Institute of Telecommunications

Research

Information on some of our current research activities.

Publications

  1. 2021

    1. M. Geiselhart, A. Elkelesh, M. Ebada, S. Cammerer, and S. Ten Brink, “Automorphism Ensemble Decoding of Reed—Muller Codes,” IEEE Transactions on Communications, pp. 1–1, 2021.
    2. C. Senger, “CRC error detection for CAN XL,” in 17th international CAN Conference (iCC), Baden-Baden, Germany, 2021.
    3. S. Miao and C. Senger, “Dual-containing Alternant Codes for Applications in the Calderbank-Shor-Steane Construction,” in 2021 IEEE International Symposium on Information Theory (ISIT), Melbourne, Australia, 2021, pp. 12–20.
    4. M. Geiselhart, A. Elkelesh, M. Ebada, S. Cammerer, and S. ten Brink, “Iterative Reed-Muller Decoding,” 2021 [Online]. Available: http://arxiv.org/abs/2107.12613
    5. M. Geiselhart, A. Elkelesh, M. Ebada, S. Cammerer, and S. ten Brink, “On the Automorphism Group of Polar Codes,” in 2021 IEEE International Symposium on Information Theory (ISIT), 2021, pp. 1230–1235.
    6. M. Geiselhart, A. Elkelesh, M. Ebada, S. Cammerer, and S. ten Brink, “On the Automorphism Group of Polar Codes,” 2021 [Online]. Available: http://arxiv.org/abs/2101.09679
    7. F. Euchner and C. Senger, “PERIDOT Codes: Replacing Identifiers, Sequence Numbers and Nonces with Permutations,” in 30th Biennial Symposium on Communications (BSC 2021), Saskatoon, SK, Canada, 2021.
    8. J. Clausius, S. Dörner, S. Cammerer, and S. ten Brink, “Serial vs. Parallel Turbo-Autoencoders and Accelerated Training for  Learned Channel Codes.” 2021 [Online]. Available: http://arxiv.org/abs/2104.14234
  2. 2020

    1. M. Geiselhart, A. Elkelesh, M. Ebada, S. Cammerer, and S. ten Brink, “Automorphism Ensemble Decoding of Reed-Muller Codes,” 2020 [Online]. Available: http://arxiv.org/abs/2012.07635
    2. M. Geiselhart, A. Elkelesh, M. Ebada, S. Cammerer, and S. ten Brink, “CRC-Aided Belief Propagation List Decoding of Polar Codes,” in 2020 IEEE International Symposium on Information Theory (ISIT), 2020, pp. 395–400 [Online]. Available: https://ieeexplore.ieee.org/document/9174249
    3. T. Uhlemann, S. Cammerer, A. Span, S. Doerner, and S. ten Brink, “Deep-learning Autoencoder for Coherent and Nonlinear Optical Communication,” in Photonic Networks; 21th ITG-Symposium, 2020, pp. 1–8.
    4. M. Gauger, M. Arnold, and ten B. Stephan, “Drone-Based Spatial MIMO Measurements in Three Dimensions,” 2020.
    5. M. Ebada, S. Cammerer, A. Elkelesh, M. Geiselhart, and S. ten Brink, “Iterative Detection and Decoding of Finite-Length Polar Codes in Gaussian Multiple Access Channels,” in 2020 54th Asilomar Conference on Signals, Systems, and Computers, 2020, pp. 683–688 [Online]. Available: https://ieeexplore.ieee.org/document/9443374
    6. M. Gauger, M. Arnold, and S. ten Brink, “Massive MIMO Channel Measurements and Achievable Rates in a Residential  Area.” 2020 [Online]. Available: http://arxiv.org/abs/2002.09452
    7. D. Verenzuela, E. Björnson, X. Wang, M. Arnold, and S. ten Brink, “Massive-MIMO Iterative Channel Estimation and Decoding (MICED) in the Uplink,” IEEE Transactions on Communications, vol. 68, no. 2, pp. 854–870, Feb. 2020 [Online]. Available: https://ieeexplore.ieee.org/document/8873655
    8. S. Cammerer, F. A. Aoudia, S. Dörner, M. Stark, J. Hoydis, and S. ten Brink, “Trainable Communication Systems: Concepts and Prototype,” IEEE Transactions on Communications, vol. 68, no. 9, pp. 5489–5503, Sep. 2020 [Online]. Available: https://ieeexplore.ieee.org/document/9118963
    9. S. Dörner, M. Henninger, S. Cammerer, and S. ten Brink, “WGAN-based Autoencoder Training Over-the-air.” 2020 [Online]. Available: https://arxiv.org/abs/2003.02744
  3. 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, Jul. 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. M. Arnold, S. Dörner, S. Cammerer, S. Yan, J. Hoydis, and S. ten Brink, “Enabling FDD Massive MIMO through Deep Learning-based Channel Prediction.” 2019 [Online]. Available: http://arxiv.org/abs/1901.03664
    5. 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
    6. C. Senger, “Improved Iterative Decoding of Product Codes Based on Trusted Symbols,” in 2019 IEEE International Symposium on Information Theory (ISIT), 2019, pp. 1342–1346 [Online]. Available: https://ieeexplore.ieee.org/document/8849469
    7. 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, Nov. 2019 [Online]. Available: https://ieeexplore.ieee.org/document/8804231
    8. M. Arnold, J. Hoydis, and S. t. Brink, “Novel Massive MIMO Channel Sounding Data applied to Deep Learning-based Indoor Positioning,” in SCC 2019; 12th International ITG Conference on Systems, Communications and Coding, 2019, pp. 1–6 [Online]. Available: https://ieeexplore.ieee.org/document/8661318
    9. C. Senger, “On List Decoding of Generalized Reed-Solomon Codes Under Partial Codeword Knowledge,” in SCC 2019; 12th International ITG Conference on Systems, Communications and Coding, 2019, pp. 1–5 [Online]. Available: https://ieeexplore.ieee.org/document/8661300
    10. D. Tandler, S. Dörner, S. Cammerer, and S. ten Brink, “On Recurrent Neural Networks for Sequence-based Processing in Communications,” in 2019 53rd Asilomar Conference on Signals, Systems, and Computers, 2019, pp. 537–543 [Online]. Available: https://ieeexplore.ieee.org/document/9048728
    11. M. Arnold and S. t. Brink, “Properties of Measured Massive MIMO Channels using Different Antenna Geometries,” in WSA 2019; 23rd International ITG Workshop on Smart Antennas, 2019, pp. 1–5 [Online]. Available: https://ieeexplore.ieee.org/document/8727203
    12. 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
    13. X. Wang, M. Arnold, and S. t. Brink, “TDD and FDD Massive MIMO: Channel Coherence and Operation Region,” in WSA 2019; 23rd International ITG Workshop on Smart Antennas, 2019, pp. 1–5 [Online]. Available: https://ieeexplore.ieee.org/document/8727218
    14. M. Arnold, S. Dörner, S. Cammerer, J. Hoydis, and S. T. Brink, “Towards Practical FDD Massive MIMO: CSI Extrapolation Driven by Deep Learning and Actual Channel Measurements,” 2019.
    15. M. Arnold, S. Dörner, S. Cammerer, J. Hoydis, and S. ten Brink, “Towards Practical FDD Massive MIMO: CSI Extrapolation Driven by Deep Learning and Actual Channel Measurements,” in 2019 53rd Asilomar Conference on Signals, Systems, and Computers, 2019, pp. 1972–1976 [Online]. Available: https://ieeexplore.ieee.org/document/9048863
    16. M. Widmaier, M. Arnold, S. Dörner, 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
    17. 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
    18. 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
  4. 2018

    1. C. Senger and R. Bohara, “A Linear Algebraic Approach to Subfield Subcodes of GRS Codes,” in 2018 IEEE International Symposium on Information Theory (ISIT), 2018, pp. 6–10 [Online]. Available: https://ieeexplore.ieee.org/document/8437645
    2. M. Arnold, J. Pfeiffer, and S. t. Brink, “Area Rate Evaluation based on Spatial Clustering of massive MIMO Channel Measurements,” in WSA 2018; 22nd International ITG Workshop on Smart Antennas, 2018, pp. 1–6 [Online]. Available: https://ieeexplore.ieee.org/document/8385445
    3. 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
    4. 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, Aug. 2018 [Online]. Available: https://ieeexplore.ieee.org/document/8396299
    5. S. Dörner, S. Cammerer, J. Hoydis, and S. ten Brink, “Deep Learning Based Communication Over the Air,” IEEE Journal of Selected Topics in Signal Processing, vol. 12, no. 1, pp. 132–143, Feb. 2018 [Online]. Available: https://ieeexplore.ieee.org/document/8214233
    6. 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, Feb. 2018 [Online]. Available: https://ieeexplore.ieee.org/document/8214233
    7. 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
    8. 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
    9. M. Arnold, S. Dorner, S. Cammerer, and S. Ten Brink, “On Deep Learning-Based Massive MIMO Indoor User Localization,” 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/8446013
    10. M. Arnold, S. Dörner, S. Cammerer, and S. ten Brink, “On Deep Learning-Based Massive MIMO Indoor User Localization,” 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/8446013
    11. 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
    12. 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
    13. 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
    14. M. Arnold, X. Wang, and S. T. Brink, “Subcarrier-interlaced FDD for faster-than-TDD channel tracking in massive MIMO systems,” in WSA 2018; 22nd International ITG Workshop on Smart Antennas, 2018, pp. 1–7 [Online]. Available: https://ieeexplore.ieee.org/document/8430496
    15. C. Senger and H. K. Shivakumar, “Subfield Subcodes of Tamo–Barg Locally Recoverable Codes,” in 2018 29th Biennial Symposium on Communications (BSC), 2018, pp. 1–5 [Online]. Available: https://ieeexplore.ieee.org/document/8494698
  5. 2017

    1. M. Arnold, M. Gauger, and S. ten Brink, “Evaluating massive MIMO precoding based on 3D-channel measurements with a spider antenna,” in 2017 International Symposium on Wireless Communication Systems (ISWCS), 2017, pp. 134–139 [Online]. Available: https://ieeexplore.ieee.org/document/8108098
    2. 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
    3. 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
    4. 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
    5. S. Dörner, 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
    6. 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
  6. 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. C. Senger and F. R. Kschischang, “Syndrome-based Decoding of Evaluation Codes without Chien Search,” in 28th Biennial Symposium on Communications (BSC 2016), Kelowna, BC, Canada, 2016.
    3. 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
    4. 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
  7. 2014

    1. C. Senger, V. Sidorenko, and A. Chaaban, “On multi-trial Forney-Kovalev decoding of concatenated codes,” Advances in Mathematics of Communications, vol. 8, no. 1, pp. 1--20, Jan. 2014 [Online]. Available: https://doi.org/10.3934%2Famc.2014.8.1
    2. C. Senger, “Sierpinski Prefactors in the Guruwami-Sudan Interpolation Step,” in 2014 International Zurich Seminar on Communications, Zurich, Switzerland, 2014, pp. 83–86 [Online]. Available: http://e-collection.library.ethz.ch/eserv/eth:8192/eth-8192-01.pdf
  8. 2013

    1. D. E. Lazich, C. Senger, and M. Bossert, “A Corrected Disproof of the Strong Simplex Conjecture,” in SCC 2013; 9th International ITG Conference on Systems, Communication and Coding, 2013, pp. 1–5 [Online]. Available: https://ieeexplore.ieee.org/document/6469334
  9. 2012

    1. C. Senger, A. Zeh, Y. Fan, and S. Schober, “Adaptive End-to-End Network Coding based on Algebraic Codes,” in First International Workshop on Performance Evaluation and Modeling in Wireless Networks (PEMWN2012), Tunis, Tunisia, 2012.
    2. J. H. Weber, V. R. Sidorenko, C. Senger, and K. A. S. Abdel-Ghaffar, “Asymptotic single-trial strategies for GMD decoding with arbitrary error-erasure tradeoff,” Problems of Information Transmission, vol. 48, no. 4, pp. 324--333, Oct. 2012 [Online]. Available: https://doi.org/10.1134/S0032946012040023
  10. 2011

    1. V. R. Sidorenko, C. Senger, and M. Bossert, “Decoding in Weighted Combinatorial Metric,” in International Mathematical Conference “50 Years of IPPI,” Moscow, Russia, 2011.
  11. 2010

    1. C. Senger, V. R. Sidorenko, S. Schober, M. Bossert, and V. V. Zyablov, “Adaptive single-trial error/erasure decoding of binary codes,” in 2010 International Symposium On Information Theory  Its Applications, 2010, pp. 267–272 [Online]. Available: https://ieeexplore.ieee.org/document/5648959
    2. C. Senger, V. R. Sidorenko, M. Bossert, and V. V. Zyablov, “Multitrial decoding of concatenated codes using fixed thresholds,” Problems of Information Transmission, vol. 46, no. 2, pp. 127--141, Jun. 2010 [Online]. Available: https://doi.org/10.1134/S0032946010020031
    3. V. Sidorenko, and Christian Senger, M. Bossert, and V. Zyablov, “Single-trial decoding of concatenated codes using fixed or adaptive erasing,” Advances in Mathematics of Communications, vol. 4, no. 1, pp. 49--60, 2010 [Online]. Available: https://doi.org/10.3934%2Famc.2010.4.49
  12. 2009

    1. C. Senger, V. R. Sidorenko, and V. V. Zyablov, “On Generalized Minimum Distance Decoding Thresholds for the AWGN Channel,” in XII International Symposium on Problems of Redundancy in Information and Control Systems, St. Petersburg, Russia, 2009, pp. 155–163.
    2. C. Senger, “Optimized Error/Erasure Bounded Minimum Distance Decoding for the AWGN Channel,” in IEEE Information Theory Winter School 2009, Loen, Norway, 2009.
  13. 2008

    1. C. Senger, V. Sidorenko, M. Bossert, and V. Zyablov, “Decoding generalized concatenated codes using Interleaved Reed-Solomon codes,” in 2008 IEEE International Symposium on Information Theory, 2008, pp. 1808–1812 [Online]. Available: https://ieeexplore.ieee.org/document/4595300
    2. G. Richter, C. Senger, and M. Bossert, “Description and Decoding of q-cyclic Rank Codes,” in 7th International ITG Conference on Source and Channel Coding, 2008, pp. 1–6 [Online]. Available: https://ieeexplore.ieee.org/document/5755959
    3. V. R. Sidorenko, C. Senger, M. Bossert, and V. V. Zyablov, “Single-Trial Adaptive Decoding of Concatenated Codes,” in 11th International Workshop on Algebraic and Combinatorial Coding Theory (ACCT2008), Pamporovo, Bulgaria, 2008, pp. 260–266 [Online]. Available: http://www.moi.math.bas.bg/acct2008/b44.pdf

Machine Learning for Communications

Can we "learn" to communicate?

Inspired by Shannon’s groundbreaking work on the fundamental limits of communications, we seek to solve the task of “reproducing at one point either exactly or approximately a message selected at another point”.  In other words, we seek to reliably transmit a message from a source to a destination over a channel using a transmitter and a receiver.

We propose a thinking-outside-the-box solution where we reinterpret transceiver signal-processing blocks (e.g., quantization, error correcting coding, modulation, detection) as neural networks, enabling data-driven communications systems that perpetually learn and adapt to any environment.

We built the world’s first over-the-air communications system that consists solely of neural networks. In this spirit, we propose one holistic block that does it all using joint signal-processing for detection, synchronization and decoding so that the information is completely transmitted within a single sequence (
waveform).

Current Research

Bit-wise Autoencoder Communication System Training

02:13

This video shows the training process of a bit-wise autoencoder communications system. The system tries to find optimal signals to transmit k=4 bit within n=2 real valued channel uses at the trained SNR of 3.85dB. On the left side one can see the currently used constellation and on the right side one can see the mutual information obtained at receiver side. The bottom heat map plots show the current decision regions of the learned receiver, where pink regions indicate a strong decision towards bit 0 and blue regions indicate a strong decision towards bit 1. It can be seen, that the system finally converges towards a constellation that differs from classical modulation schemes, like the comparable 16-QAM constellation, and is thereby able to transmit more information at this operational point. While these gains are well known in communications research as so called shaping gains, the possible game changer of a neural network based autoencoder system is, that it is able to optimize its signals over every kind of channel, including all unknown insufficiencies using data based training strategies.

For more information see the paper "Trainable Communication Systems: Concepts and Prototype" by S. Cammerer, F. A. Aoudia, S. Dörner, M. Stark, J. Hoydis and S. ten Brink at ieeexplore.ieee.org where these results are based on.

Contact

This image shows Stephan ten Brink
Prof. Dr.-Ing.

Stephan ten Brink

Director

This image shows Christian  Senger
Dr.-Ing.

Christian Senger

Deputy Director

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