# 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**]

**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**]