Simulation Code

Sharing is caring. In science, sharing is the way to enable research reproducibility and swift improvements of the state-of-the-art. This page lists papers from the research library for which the authors have made the simulation code openly available.

Signal Detection, Classification, and Compression

N. Samuel, T. Diskin and A. Wiesel, “Deep MIMO detection,” in Proc. IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), July 2017. [Simulation code]
N. Samuel, T. Diskin and A. Wiesel, “Learning to Detect,” preprint arXiv:1805.07631, 2018. [Simulation code]
C.-K. Wen, W.-T. Shih and S. Jin, “Deep learning for massive MIMO CSI feedback,” IEEE Wireless Communications Letters, vol. 7, no. 5, pp. 748-751, October 2018. [Simulation code]
S. Ramjee, S. Ju, D. Yang, X. Liu, A. El Gamal, and Y. C. Eldar, “Fast Deep Learning for Automatic Modulation Classification,” preprint arXiv:1901.05850, 2019. [Simulation code]
Z. Zhao, M. C. Vuran, F. Guo, and S. Scott, “Deep-Waveform: A Learned OFDM Receiver Based on Deep Complex Convolutional Networks,” preprint arXiv:1810.07181, 2018. [Simulation code]
F. Meng, P. Chen, L. Wu, and X. Wang, “Automatic Modulation Classification: A Deep Learning Enabled Approach,” IEEE Transactions on Vehicular Technology, vol. 67, no. 11, pp. 10760-10772, 2018. [Simulation code]
G. Cerar, M. Mohorčič, T. Gale and C. Fortuna, “Analysis of machine learning for link quality estimation,” preprint arXiv:1812.08856, 2018. [Simulation code]

Channel Encoding and Decoding

T. Gruber, S. Cammerer, J. Hoydis and S. ten Brink, “On deep learning-based channel decoding,” in Proc. Information Sciences and Systems (CISS), March 2017. [Simulation code]
F. Liang, C. Shen and F. Wu, “An iterative BP-CNN architecture for channel decoding,” in IEEE Journal of Selected Topics in Signal Processing, vol. 12, no. 1, pp. 144-159, February 2018. [Simulation code]
E. Nachmani, E. Marciano, L. Lugosch, W. J. Gross, D. Burshtein and Y. Be’ery, “Deep learning methods for improved decoding of linear codes,” IEEE Journal of Selected Topics in Signal Processing, vol. 12, no. 1, pp.119-131, February 2018. [Simulation code]
L. Lugosch and W. J. Gross, “Learning from the syndrome,” in Proc. IEEE Asilomar Conference on Signal, System, Computers, October 2018. [Simulation code]
L. Lugosch and W. J. Gross, “Neural offset min-sum decoding,” in Proc. IEEE International Symposium on Information Theory (ISIT), June 2017. [Simulation code]
H. Kim, Y. Jiang, S. Kannan, S. Oh, and P. Viswanath, “Deepcode: Feedback Codes via Deep Learning,” preprint arXiv:1807.00801, 2018. [Simulation code]
C. Qing, B. Cai, Q. Yang, J. Wang and C. Huang, “Deep learning for CSI feedback based on superimposed coding,” in IEEE Access, 2019. [Simulation code]

Positioning, Sensing, and Localization

M. Soltani, V. Pourahmadi, A. Mirzaei and H. Sheikhzadeh, “Deep learning-based channel estimation,” preprint arXiv:1810.05893, 2018. [Simulation code]
S. Abeywickrama, L. Jayasinghe, H. Fu, S. Nissanka, and C. Yuen, “RF-based Direction Finding of UAVs Using DNN,” in Proc. IEEE International Conference on Communication Systems (ICCS), 2018. [Simulation code]

Security and Robustness

M. Sadeghi and E. G. Larsson, “Adversarial attacks on deep-learning based radio signal classification,” IEEE Wireless Communications Letters, vol. 8, no. 1, pp. 213–216, Feb. 2019. [Simulation code]
M. Sadeghi and E. G. Larsson, “Physical adversarial attacks against end-to-end autoencoder communication systems,” IEEE Communications Letters, 2019. [Simulation code]
F. B. Mismar and B. L. Evans, “Deep Q-Learning for self-organizing networks fault management and radio performance improvement,” in Proc. Asilomar Conference on Signals, Systems, and Computers, October 2018. [Simulation code]

mmWave Communications

H. He, C.-K. Wen, S. Jin and G. Y. Li, “Deep learning-based channel estimation for beamspace mmWave massive MIMO systems,” IEEE Wireless Communications Letters, vol. 7, no. 5, pp. 852-855, October 2018. [Simulation code]
T. Lin and Y. Zhu, “Beamforming design for large-scale antenna arrays using deep learning,” preprint arXiv:1904.03657, 2019. [Simulation code]
R. Li, C. Zhang, P. Patras, P. Cao, and J. S. Thompson, “DELMU: A Deep Learning Approach to Maximising the Utility of Virtualised Millimetre-Wave Backhauls,” preprint arXiv:1810.00356, 2018. [Simulation code]
A. Klautau, P. Batista, N. Gonzalez-Prelcic, Y. Wang, and R. W. Heath Jr., “MIMO Data for Machine Learning: Application to Beam-Selection using Deep Learning,” in Information Theory and Applications Workshop, 2018. [Simulation code]
A. Alkhateeb, S. Alex, P. Varkey, Y. Li, Q. Qu and D. Tujkovic, “Deep learning coordinated beamforming for highly-mobile millimeter wave systems,” in IEEE Access, vol. 6, pp. 37328-37348, 2018. [Simulation code]
M. Alrabeiah and A. Alkhateeb, “Deep learning for mmWave beam and blockage prediction using sub-6GHz channels,” preprint arXiv:1910.02900, 2019. [Simulation code]

Resource Allocation

L. Sanguinetti, A. Zappone and M. Debbah, “Deep learning power allocation in massive MIMO,” Proc. Asilomar Conference on Signals, Systems, and Computers, 2018. [Simulation code]
Y. Zhang, C. Kang, T. Ma, Y. Teng, D. Guo, “Power Allocation in Multi-cell Networks Using Deep Reinforcement Learning,” in Proc. IEEE 88th Vehicular Technology Conference (VTC-Fall), 2018. [Simulation code]
H. Ye, G. Y. Li, “Deep Reinforcement Learning for Resource Allocation in V2V Communications,” in Proc. IEEE International Conference on Communications (ICC), 2018. [Simulation code]
F. B. Mismar and B. L. Evans, “Q-Learning algorithm for VoLTE closed-loop power control in indoor small cells,” in Proc. Asilomar Conference on Signals, Systems, and Computers, October 2018. [Simulation code]
F. B. Mismar and B. L. Evans, “Deep learning in downlink coordinated multipoint in new radio heterogeneous networks,” in IEEE Wireless Communications Letters, 2019. [Simulation code]
C. Saha and H. S. Dhillon, “Machine learning meets stochastic geometry: Determinantal subset selection for wireless networks,” preprint arXiv:1905.00504, 2019. [Simulation code]
G. Cao, Z. Lu, X. Wen, T. Lei, and Z. Hu, “AIF: An Artificial Intelligence Framework for Smart Wireless Network Management,” IEEE Communications Letters, vol. 22, no. 2, pp. 400-403, 2018. [Simulation code]
W. Lee, M. Kim, and D.-H. Cho, “Transmit Power Control Using Deep Neural Network for Underlay Device-to-Device Communication,” IEEE Wireless Communications Letters, vol. 8, no. 1, pp. 141-144, 2019. [Simulation code]
W. Cui, K. Shen, and W. Yu, “Spatial Deep Learning for Wireless Scheduling,” preprint arXiv:1808.01486, 2018. [Simulation code]
B. Matthiesen, A. Zappone, E. A. Jorswieck, and M. Debbah, “Deep Learning for Optimal Energy-Efficient Power Control in Wireless Interference Networks,” preprint arXiv:1808.01486, 2018. [Simulation code]
B. Yan, Y. Zhao, Y. Li, X. Yu, J. Zhang, Y. Wang, L. Yan, and S. Rahman, “Actor-Critic-Based Resource Allocation for Multi-Modal Optical Networks,” in IEEE Globecom Workshops, 2018. [Simulation code]
M. Kozlowski, R. McConville, R. Santos-Rodriguez, and R. Piechocki, “Energy Efficiency in Reinforcement Learning for Wireless Sensor Networks,” preprint arXiv:1812.02538, 2018. [Simulation code]
F. Wilhelmi, B. Bellalta, C. Cano, A. Jonsson, “Implications of Decentralized Q-learning Resource Allocation in Wireless Networks,” in IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), 2017. [Simulation code]

Quantization

Y. Zhang, M. Alrabeiah, and A. Alkhateeb, “Deep learning for massive MIMO with 1-bit ADCs: When more antennas need fewer pilots,” preprint arXiv:1910.06960, 2019. [Simulation code]

Machine Learning For Communications Emerging Technologies Initiative