# Novel Deep Neural OFDM Receiver Architectures for LLR Estimation

arXiv: https://arxiv.org/abs/2503.20500 · HTML: https://mlsanity.com/paper/2503.20500/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 21 references that are in the map.
- First posted on arXiv 26 Mar 2025.

## Builds on (most cited first, 16 of 16)

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- [DeepRx: Fully Convolutional Deep Learning Receiver](https://mlsanity.com/paper/2005.01494/) (2020)
- [End-to-end Learning for OFDM: From Neural Receivers to Pilotless Communication](https://mlsanity.com/paper/2009.05261/) (2020)
- [Design of a Standard-Compliant Real-Time Neural Receiver for 5G NR](https://mlsanity.com/paper/2409.02912/) (2024)
- [HybridDeepRx: Deep Learning Receiver for High-EVM Signals](https://mlsanity.com/paper/2106.16079/) (2021)
- [Adapting to Reality: Over-the-Air Validation of AI-Based Receivers Trained with Simulated Channels](https://mlsanity.com/paper/2408.04182/) (2024)
- [On Transfer Learning for a Fully Convolutional Deep Neural SIMO Receiver](https://mlsanity.com/paper/2408.16401/) (2024)
- [Interpreting Deep Neural Network-Based Receiver Under Varying Signal-To-Noise Ratios](https://mlsanity.com/paper/2409.16768/) (2024)
- [Belief Information based Deep Channel Estimation for Massive MIMO Systems](https://mlsanity.com/paper/2407.07744/) (2024)
- [TransRx-6G-V2X : Transformer Encoder-Based Deep Neural Receiver For Next Generation of Cellular Vehicular Communications](https://mlsanity.com/paper/2408.01145/) (2024)

## Abstract (arXiv)

Neural receivers have recently become a popular topic, where the received signals can be directly decoded by data driven mechanisms such as machine learning and deep learning. In this paper, we propose two novel neural network based orthogonal frequency division multiplexing (OFDM) receivers performing channel estimation and equalization tasks and directly predicting log likelihood ratios (LLRs) from the received in phase and quadrature phase (IQ) signals. The first network, the Dual Attention Transformer (DAT), employs a state of the art (SOTA) transformer architecture with an attention mechanism. The second network, the Residual Dual Non Local Attention Network (RDNLA), utilizes a parallel residual architecture with a non local attention block. The bit error rate (BER) and block error rate (BLER) performance of various SOTA neural receiver architectures is compared with our proposed methods across different signal to noise ratio (SNR) levels. The simulation results show that DAT and RDNLA outperform both traditional communication systems and existing neural receiver models.