# Adaptive End-to-End Transceiver Design for NextG Pilot-Free and CP-Free Wireless Systems

arXiv: https://arxiv.org/abs/2510.25416 · HTML: https://mlsanity.com/paper/2510.25416/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 44 references that are in the map.
- First posted on arXiv 29 Oct 2025.

## Builds on (most cited first, 26 of 26)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [An Introduction to Deep Learning for the Physical Layer](https://mlsanity.com/paper/1702.00832/) (2017)
- [Sionna: An Open-Source Library for Next-Generation Physical Layer Research](https://mlsanity.com/paper/2203.11854/) (2022)
- [On the Road to 6G: Visions, Requirements, Key Technologies and Testbeds](https://mlsanity.com/paper/2302.14536/) (2023)
- [Toward a 6G AI-Native Air Interface](https://mlsanity.com/paper/2012.08285/) (2020)
- [DeepRx: Fully Convolutional Deep Learning Receiver](https://mlsanity.com/paper/2005.01494/) (2020)
- [Machine Learning in the Air](https://mlsanity.com/paper/1904.12385/) (2019)
- [End-to-end Learning for OFDM: From Neural Receivers to Pilotless Communication](https://mlsanity.com/paper/2009.05261/) (2020)
- [Transfer Learning for Future Wireless Networks: A Comprehensive Survey](https://mlsanity.com/paper/2102.07572/) (2021)
- [Deep Joint Source-Channel Coding for CSI Feedback: An End-to-End Approach](https://mlsanity.com/paper/2203.16005/) (2022)
- [Waveform Learning for Next-Generation Wireless Communication Systems](https://mlsanity.com/paper/2109.00998/) (2021)
- [Next Generation Advanced Transceiver Technologies for 6G and Beyond](https://mlsanity.com/paper/2403.16458/) (2024)
- [Trimming the Fat from OFDM: Pilot- and CP-less Communication with End-to-end Learning](https://mlsanity.com/paper/2101.08213/) (2021)
- [Towards Sustainability in 6G and beyond: Challenges and Opportunities of Open RAN](https://mlsanity.com/paper/2503.08353/) (2025)
- [Swin Transformer-Based CSI Feedback for Massive MIMO](https://mlsanity.com/paper/2401.06435/) (2024)
- [Learning at the Speed of Wireless: Online Real-Time Learning for AI-Enabled MIMO in NextG](https://mlsanity.com/paper/2403.02651/) (2024)
- [Deep Learning for CSI Feedback: One-Sided Model and Joint Multi-Module Learning Perspectives](https://mlsanity.com/paper/2405.05522/) (2024)
- [Low PAPR MIMO-OFDM Design Based on Convolutional Autoencoder](https://mlsanity.com/paper/2301.05017/) (2023)
- [Signal Processing and Learning for Next Generation Multiple Access in 6G](https://mlsanity.com/paper/2309.00559/) (2023)
- [Interference Cancellation Based Neural Receiver for Superimposed Pilot in Multi-Layer Transmission](https://mlsanity.com/paper/2406.18993/) (2024)
- [On Transfer Learning for a Fully Convolutional Deep Neural SIMO Receiver](https://mlsanity.com/paper/2408.16401/) (2024)
- [Constellation Shaping under Phase Noise Impairment for Sub-THz Communications](https://mlsanity.com/paper/2311.12433/) (2023)
- [Wireless Information and Energy Transfer in the Era of 6G Communications](https://mlsanity.com/paper/2404.18705/) (2024)

## Built on by (most cited first, 3 of 3)

- [EqDeepRx: Learning a Scalable and Interference Mitigating MIMO Receiver](https://mlsanity.com/paper/2602.11834/) (2026)
- [Efficient Transceiver Design for Aerial Image Transmission and Large-scale Scene Reconstruction](https://mlsanity.com/paper/2604.11098/) (2026)
- [CARNet: Channel-Adaptive Receiver Network for Robust NextG Communications](https://mlsanity.com/paper/2608.02172/) (2026)

## Abstract (arXiv)

The advent of artificial intelligence (AI)-native wireless communication is fundamentally reshaping the design paradigm of next-generation (NextG) systems, where intelligent air interfaces are expected to operate adaptively and efficiently in highly dynamic environments. Conventional orthogonal frequency division multiplexing (OFDM) systems rely heavily on pilots and the cyclic prefix (CP), resulting in significant overhead and reduced spectral efficiency. To address these limitations, we propose an adaptive end-to-end (E2E) transceiver architecture tailored for pilot-free and CP-free wireless systems. The architecture combines AI-driven constellation shaping and a neural receiver through joint training. To enhance robustness against mismatched or time-varying channel conditions, we introduce a lightweight channel adapter (CA) module, which enables rapid adaptation with minimal computational overhead by updating only the CA parameters. Additionally, we present a framework that is scalable to multiple modulation orders within a unified model, significantly reducing model storage requirements. Moreover, to tackle the high peak-to-average power ratio (PAPR) inherent to OFDM, we incorporate constrained E2E training, achieving compliance with PAPR targets without additional transmission overhead. Extensive simulations demonstrate that the proposed framework delivers superior bit error rate (BER), throughput, and resilience across diverse channel scenarios, highlighting its potential for AI-native NextG.