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]

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]

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]

Other Selected Topics

• M. Alrabeiah and A. Alkhateeb, “Deep learning for TDD and FDD massive MIMO: Mapping channels in space and frequency,” preprint arXiv:1905.03761, 2019. [Simulation code]
• Z. Chen and X. Wang, “Decentralized Computation Offloading for Multi-User Mobile Edge Computing: A Deep Reinforcement Learning Approach,” preprint arXiv:1812.07394, 2018. [Simulation code]
• J. Liu, B. Krishnamachari, S. Zhou, and Z. Niu, “DeepNap: Data-Driven Base Station Sleeping Operations Through Deep Reinforcement Learning,” IEEE Internet of Things Journal, vol. 5, no. 6, pp. 4273-4282, 2018. [Simulation code]
• A. Taha, M. Alrabeiah and A. Alkhateeb, “Enabling large intelligent surfaces with compressive sensing and deep learning,” preprint arXiv:1904.10136, 2019. [Simulation code]