Low resolution OFDM receivers via deep learning
US11575544B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Oct 29, 2019 |
| Grant date | Feb 7, 2023 |
| Priority date | — |
| Expiry date | Oct 29, 2039 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04L5/0007
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Various embodiments provide for deep learning-based architectures and design methodologies for an orthogonal frequency division multiplexing (OFDM) receiver under the constraint of one-bit complex quantization. Single bit quantization greatly reduces complexity and power consumption in the receivers, but makes accurate channel estimation and data detection difficult. This is particularly true for OFDM waveforms, which have high peak-to average (signal power) ratio in the time domain and fragile subcarrier orthogonality in the frequency domain. The severe distortion for one-bit quantization typically results in an error floor even at moderately low signal-to-noise-ratio (SNR) such as 5 dB. For channel estimation (using pilots), various embodiments use novel generative supervised deep neural networks (DNNs) that can be trained with a reasonable number of pilots. After channel estimation, a neural network-based receiver specifically, an autoencoder jointly learns a precoder and decoder for data symbol detection.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.