Code to accompany my upcoming book "Deep learning with PyTorch Book " from Packt
Source Code for the book: Machine Learning in Action published by Manning
Jupyter notebooks for the code samples of the book "Deep Learning with Python"
Code for my publication: Deep Learning in Downlink Coordinated Multipoint in New Radio Heterogeneous Networks. Paper is accepted to IEEE Wireless Communications Letters, 2019.
Simulation scripts used to produce the results presented in our paper R. LI et al. " DELMU: A Deep Learning Approach to Maximising the Utility of Virtualised Millimetre-Wave Backhauls"
This code is for the following paper: H. He, C. Wen, S. Jin, and G. Y. Li, “Deep learning-based channel estimation for beamspace mmwave massive MIMO systems,” IEEE Wireless Commun. Lett., vol. 7, no. 5, pp. 852–855, Oct. 2018.
Code samples and datasets that are related to link quality estimation.
Source code for "A Globally Optimal Energy-Efficient Power Control Framework and its Efficient Implementation in Wireless Interference Networks" by Bho Matthiesen, Alessio Zappone, Karl-L. Besser, Eduard A. Jorswieck, and Merouane Debbah, submitted to IEEE Transactions on Signal Processing.
Deep-Waveform: A Learned OFDM Receiver Based on Deep Complex Convolutional Networks
A research oriented repository on the Security and Robustness of Deep Learning for Wireless Communication Systems
