Search Results for author:
Found 5 papers, 0 papers with code
Wideband Channel Estimation with A Generative Adversarial Network
Communication at high carrier frequencies such as millimeter wave (mmWave) and terahertz (THz) requires channel estimation for very large bandwidths at low SNR.
High Dimensional Channel Estimation Using Deep Generative Networks
This paper presents a novel compressed sensing (CS) approach to high dimensional wireless channel estimation by optimizing the input to a deep generative network.
Autoencoder-Based Error Correction Coding for One-Bit Quantization
It is empirically shown that this design gives nearly the same performance as to the hypothetically perfectly trained autoencoder, and we also provide a theoretical proof of why that is so.
Online Antenna Tuning in Heterogeneous Cellular Networks with Deep Reinforcement Learning
Our results illustrate that the proposed algorithm approaches the performance of the multi-agent RL, which requires millions of trials, with hundreds of online trials, assuming relatively low environmental dynamics, and performs much better than a single agent RL.
One-Bit OFDM Receivers via Deep Learning
For channel estimation (using pilots), we design a novel generative supervised deep neural network (DNN) that can be trained with a reasonable number of pilots.
