Sahay, Rajeev, Zhang, Minjun, Love, David J., and Brinton, Christopher G. Defending Adversarial Attacks on Deep Learning-Based Power Allocation in Massive MIMO Using Denoising Autoencoders. Retrieved from https://par.nsf.gov/biblio/10463221. IEEE Transactions on Cognitive Communications and Networking 9.4 Web. doi:10.1109/TCCN.2023.3261307.

Sahay, Rajeev, Zhang, Minjun, Love, David J., & Brinton, Christopher G. Defending Adversarial Attacks on Deep Learning-Based Power Allocation in Massive MIMO Using Denoising Autoencoders. IEEE Transactions on Cognitive Communications and Networking, 9 (4). Retrieved from https://par.nsf.gov/biblio/10463221. https://doi.org/10.1109/TCCN.2023.3261307

Sahay, Rajeev, Zhang, Minjun, Love, David J., and Brinton, Christopher G. "Defending Adversarial Attacks on Deep Learning-Based Power Allocation in Massive MIMO Using Denoising Autoencoders". IEEE Transactions on Cognitive Communications and Networking 9 (4). Country unknown/Code not available. https://doi.org/10.1109/TCCN.2023.3261307. https://par.nsf.gov/biblio/10463221.

@article{osti_10463221,
place = {Country unknown/Code not available}, title = {Defending Adversarial Attacks on Deep Learning-Based Power Allocation in Massive MIMO Using Denoising Autoencoders}, url = {https://par.nsf.gov/biblio/10463221}, DOI = {10.1109/TCCN.2023.3261307}, abstractNote = {}, journal = {IEEE Transactions on Cognitive Communications and Networking}, volume = {9}, number = {4}, author = {Sahay, Rajeev and Zhang, Minjun and Love, David J. and Brinton, Christopher G.}, }