# End-to-end Learning for OFDM: From Neural Receivers to Pilotless Communication

arXiv: https://arxiv.org/abs/2009.05261 · HTML: https://mlsanity.com/paper/2009.05261/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2020.

## Built on by (most cited first, 30 of 33)

- [Transformer-Empowered 6G Intelligent Networks: From Massive MIMO Processing to Semantic Communication](https://mlsanity.com/paper/2205.03770/) (2022)
- [Waveform Learning for Next-Generation Wireless Communication Systems](https://mlsanity.com/paper/2109.00998/) (2021)
- [Transformers are Provably Optimal In-context Estimators for Wireless Communications](https://mlsanity.com/paper/2311.00226/) (2023)
- [Trimming the Fat from OFDM: Pilot- and CP-less Communication with End-to-end Learning](https://mlsanity.com/paper/2101.08213/) (2021)
- [Deep Learning in Physical Layer: Review on Data Driven End-to-End Communication Systems and their Enabling Semantic Applications](https://mlsanity.com/paper/2401.12800/) (2024)
- [End-to-End Learning of OFDM Waveforms with PAPR and ACLR Constraints](https://mlsanity.com/paper/2106.16039/) (2021)
- [Waveform Learning for Reduced Out-of-Band Emissions Under a Nonlinear Power Amplifier](https://mlsanity.com/paper/2201.05524/) (2022)
- [Neural Network-based OFDM Receiver for Resource Constrained IoT Devices](https://mlsanity.com/paper/2205.06159/) (2022)
- [HybridDeepRx: Deep Learning Receiver for High-EVM Signals](https://mlsanity.com/paper/2106.16079/) (2021)
- [Learning OFDM Waveforms with PAPR and ACLR Constraints](https://mlsanity.com/paper/2110.10987/) (2021)
- [Autoencoder-based Communications with Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2112.04441/) (2021)
- [Adaptive End-to-End Transceiver Design for NextG Pilot-Free and CP-Free Wireless Systems](https://mlsanity.com/paper/2510.25416/) (2025)
- [Semantic Multi-Resolution Communications](https://mlsanity.com/paper/2308.11604/) (2023)
- [Decision Feedback In-Context Symbol Detection over Block-Fading Channels](https://mlsanity.com/paper/2411.07600/) (2024)
- [PRONTO: Preamble Overhead Reduction with Neural Networks for Coarse Synchronization](https://mlsanity.com/paper/2112.10885/) (2021)
- [Bit-Metric Decoding Rate in Multi-User MIMO Systems: Theory](https://mlsanity.com/paper/2203.06271/) (2022)
- [Deep Learning-Based Pilotless Spatial Multiplexing](https://mlsanity.com/paper/2312.05158/) (2023)
- [Efficient Deep Neural Receiver with Post-Training Quantization](https://mlsanity.com/paper/2508.06275/) (2025)
- [Invariant Transformation and Resampling based Epistemic-Uncertainty Reduction](https://mlsanity.com/paper/2602.23315/) (2026)
- [DeepOFW: Deep Learning-Driven OFDM-Flexible Waveform Modulation for Peak-to-Average Power Ratio Reduction](https://mlsanity.com/paper/2603.23544/) (2026)
- [Dual-Attention Based 3D Channel Estimation](https://mlsanity.com/paper/2604.01769/) (2026)
- [How Critical is Site-Specific RAN Optimization? 5G Open-RAN Uplink Air Interface Performance Test and Optimization from Macro-Cell CIR Data](https://mlsanity.com/paper/2410.19565/) (2024)
- [VERITAS: Verifying the Performance of AI-native Transceiver Actions in Base-Stations](https://mlsanity.com/paper/2501.09761/) (2025)
- [End-to-End Human Pose Reconstruction from Wearable Sensors for 6G Extended Reality Systems](https://mlsanity.com/paper/2503.04860/) (2025)
- [Decision Feedback In-Context Learning for Wireless Symbol Detection](https://mlsanity.com/paper/2503.16594/) (2025)
- [Novel Deep Neural OFDM Receiver Architectures for LLR Estimation](https://mlsanity.com/paper/2503.20500/) (2025)
- [Efficient Quantization-Aware Neural Receivers: Beyond Post-Training Quantization](https://mlsanity.com/paper/2509.13786/) (2025)
- [A Study of Neural Polar Decoders for Communication](https://mlsanity.com/paper/2510.03069/) (2025)
- [Conditional Diffusion Model-Enabled Scenario-Specific Neural Receivers for Superimposed Pilot Schemes](https://mlsanity.com/paper/2511.01173/) (2025)
- [Score-Based Conditional Flow Models for MIMO Receiver Design with Superimposed Pilots](https://mlsanity.com/paper/2602.21654/) (2026)

## Abstract (arXiv)

Previous studies have demonstrated that end-to-end learning enables significant shaping gains over additive white Gaussian noise (AWGN) channels. However, its benefits have not yet been quantified over realistic wireless channel models. This work aims to fill this gap by exploring the gains of end-to-end learning over a frequency- and time-selective fading channel using orthogonal frequency division multiplexing (OFDM). With imperfect channel knowledge at the receiver, the shaping gains observed on AWGN channels vanish. Nonetheless, we identify two other sources of performance improvements. The first comes from a neural network (NN)-based receiver operating over a large number of subcarriers and OFDM symbols which allows to significantly reduce the number of orthogonal pilots without loss of bit error rate (BER). The second comes from entirely eliminating orthognal pilots by jointly learning a neural receiver together with either superimposed pilots (SIPs), linearly combined with conventional quadrature amplitude modulation (QAM), or an optimized constellation geometry. The learned geometry works for a wide range of signal-to-noise ratios (SNRs), Doppler and delay spreads, has zero mean and does hence not contain any form of superimposed pilots. Both schemes achieve the same BER as the pilot-based baseline with around 7% higher throughput. Thus, we believe that a jointly learned transmitter and receiver are a very interesting component for beyond-5G communication systems which could remove the need and associated control overhead for demodulation reference signals (DMRSs).