# Deep Learning Based on Orthogonal Approximate Message Passing for CP-Free OFDM

## TL;DR

This paper introduces a deep learning-based receiver for CP-free OFDM systems that combines neural networks with approximate message passing, achieving improved channel estimation and signal detection with low complexity.

## Contribution

It proposes a novel DL-OAMP receiver integrating neural networks with OAMP, capable of estimating time-varying channels with a single training and outperforming existing algorithms.

## Key findings

- Lower bit-error rate compared to competitive algorithms.
- Effective for high-order modulation schemes.
- Capable of estimating time-varying channels with minimal training.

## Abstract

Channel estimation and signal detection are very challenging for an orthogonal frequency division multiplexing (OFDM) system without cyclic prefix (CP). In this article, deep learning based on orthogonal approximate message passing (DL-OAMP) is used to address these problems. The DL-OAMP receiver includes a channel estimation neural network (CE-Net) and a signal detection neural network based on OAMP, called OAMP-Net. The CE-Net is initialized by the least square channel estimation algorithm and refined by minimum mean-squared error (MMSE) neural network. The OAMP-Net is established by unfolding the iterative OAMP algorithm and adding some trainable parameters to improve the detection performance. The DL-OAMP receiver is with low complexity and can estimate time-varying channels with only a single training. Simulation results demonstrate that the bit-error rate (BER) of the proposed scheme is lower than those of competitive algorithms for high-order modulation.