# Deep Energy Autoencoder for Noncoherent Multicarrier MU-SIMO Systems

arXiv: https://arxiv.org/abs/2002.08710 · HTML: https://mlsanity.com/paper/2002.08710/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2020.

## Built on by (most cited first, 7 of 7)

- [Real-time Outdoor Localization Using Radio Maps: A Deep Learning Approach](https://mlsanity.com/paper/2106.12556/) (2021)
- [A Weighted Autoencoder-Based Approach to Downlink NOMA Constellation Design](https://mlsanity.com/paper/2306.13423/) (2023)
- [Diffusion Models for Accurate Channel Distribution Generation](https://mlsanity.com/paper/2309.10505/) (2023)
- [Model-Driven Deep Learning for Non-Coherent Massive Machine-Type Communications](https://mlsanity.com/paper/2301.00516/) (2023)
- [End-to-End Learning for Uplink MU-SIMO Joint Transmitter and Non-Coherent Receiver Design in Fading Channels](https://mlsanity.com/paper/2105.01260/) (2021)
- [Differential Deep Detection in Massive MIMO With One-Bit ADC](https://mlsanity.com/paper/2110.14534/) (2021)
- [Variational Autoencoders for Precoding Matrices with High Spectral Efficiency](https://mlsanity.com/paper/2111.15626/) (2021)

## Abstract (arXiv)

We propose a novel deep energy autoencoder (EA) for noncoherent multicarrier multiuser single-input multipleoutput (MU-SIMO) systems under fading channels. In particular, a single-user noncoherent EA-based (NC-EA) system, based on the multicarrier SIMO framework, is first proposed, where both the transmitter and receiver are represented by deep neural networks (DNNs), known as the encoder and decoder of an EA. Unlike existing systems, the decoder of the NC-EA is fed only with the energy combined from all receive antennas, while its encoder outputs a real-valued vector whose elements stand for the subcarrier power levels. Using the NC-EA, we then develop two novel DNN structures for both uplink and downlink NC-EA multiple access (NC-EAMA) schemes, based on the multicarrier MUSIMO framework. Note that NC-EAMA allows multiple users to share the same sub-carriers, thus enables to achieve higher performance gains than noncoherent orthogonal counterparts. By properly training, the proposed NC-EA and NC-EAMA can efficiently recover the transmitted data without any channel state information estimation. Simulation results clearly show the superiority of our schemes in terms of reliability, flexibility and complexity over baseline schemes.