# Neural Network-based OFDM Receiver for Resource Constrained IoT Devices

arXiv: https://arxiv.org/abs/2205.06159 · HTML: https://mlsanity.com/paper/2205.06159/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 21 references that are in the map.
- First posted on arXiv 12 May 2022.

## Builds on (most cited first, 5 of 5)

- [Communication Algorithms via Deep Learning](https://mlsanity.com/paper/1805.09317/) (2018)
- [ADMM-NN: An Algorithm-Hardware Co-Design Framework of DNNs Using Alternating Direction Method of Multipliers](https://mlsanity.com/paper/1812.11677/) (2018)
- [End-to-end Learning for OFDM: From Neural Receivers to Pilotless Communication](https://mlsanity.com/paper/2009.05261/) (2020)
- [Learn-Prune-Share for Lifelong Learning](https://mlsanity.com/paper/2012.06956/) (2020)
- [ComNet: Combination of Deep Learning and Expert Knowledge in OFDM Receivers](https://mlsanity.com/paper/1810.09082/) (2018)

## Built on by (most cited first, 5 of 5)

- [Learning from the Best: Active Learning for Wireless Communications](https://mlsanity.com/paper/2402.04896/) (2024)
- [NN-Defined Modulator: Reconfigurable and Portable Software Modulator on IoT Gateways](https://mlsanity.com/paper/2403.09861/) (2024)
- [ATLAS: AI-Native Receiver Test-and-Measurement by Leveraging AI-Guided Search](https://mlsanity.com/paper/2508.12204/) (2025)
- [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)

## Abstract (arXiv)

Orthogonal Frequency Division Multiplexing (OFDM)-based waveforms are used for communication links in many current and emerging Internet of Things (IoT) applications, including the latest WiFi standards. For such OFDM-based transceivers, many core physical layer functions related to channel estimation, demapping, and decoding are implemented for specific choices of channel types and modulation schemes, among others. To decouple hard-wired choices from the receiver chain and thereby enhance the flexibility of IoT deployment in many novel scenarios without changing the underlying hardware, we explore a novel, modular Machine Learning (ML)-based receiver chain design. Here, ML blocks replace the individual processing blocks of an OFDM receiver, and we specifically describe this swapping for the legacy channel estimation, symbol demapping, and decoding blocks with Neural Networks (NNs). A unique aspect of this modular design is providing flexible allocation of processing functions to the legacy or ML blocks, allowing them to interchangeably coexist. Furthermore, we study the implementation cost-benefits of the proposed NNs in resource-constrained IoT devices through pruning and quantization, as well as emulation of these compressed NNs within Field Programmable Gate Arrays (FPGAs). Our evaluations demonstrate that the proposed modular NN-based receiver improves bit error rate of the traditional non-ML receiver by averagely 61% and 10% for the simulated and over-the-air datasets, respectively. We further show complexity-performance tradeoffs by presenting computational complexity comparisons between the traditional algorithms and the proposed compressed NNs.