5 papers
Progressive Transmission using Recurrent Neural Networks
Mohammad Sadegh Safari, Vahid Pourahmadi, Patrick Mitran +1
In this paper, we investigate a new machine learning-based transmission strategy called progressive transmission or ProgTr. In ProgTr, there are b variables that should be transmit…
Pilot Pattern Design for Deep Learning-Based Channel Estimation in OFDM Systems
Mehran Soltani, Vahid Pourahmadi, Hamid Sheikhzadeh
In this paper, we present a downlink pilot design scheme for Deep Learning (DL) based channel estimation (ChannelNet) in orthogonal frequency-division multiplexing (OFDM) systems.…
Propagation Channel Modeling by Deep learning Techniques
Shirin Seyedsalehi, Vahid Pourahmadi, Hamid Sheikhzadeh +1
Channel, as the medium for the propagation of electromagnetic waves, is one of the most important parts of a communication system. Being aware of how the channel affects the propag…
Deep Feature Selection using a Teacher-Student Network
Ali Mirzaei, Vahid Pourahmadi, Mehran Soltani +1
High-dimensional data in many machine learning applications leads to computational and analytical complexities. Feature selection provides an effective way for solving these proble…
Deep Learning-Based Channel Estimation
Mehran Soltani, Vahid Pourahmadi, Ali Mirzaei +1
In this paper, we present a deep learning (DL) algorithm for channel estimation in communication systems. We consider the time-frequency response of a fast fading communication cha…