mlsanityData to 14 Oct 2026

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

arXiv 2510.25416 ↗First posted 29 Oct 2025eess.SP cs.AIcited by 3

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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.

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

26 of 44 references.

References · 26

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  11. 2020End-to-end Learning for OFDM: From Neural Receivers to Pilotless Communicationcited by 33
  12. 2021Transfer Learning for Future Wireless Networks: A Comprehensive Surveycited by 18
14 more
  1. 2022Deep Joint Source-Channel Coding for CSI Feedback: An End-to-End Approachcited by 14
  2. 2021Waveform Learning for Next-Generation Wireless Communication Systemscited by 10
  3. 2024Next Generation Advanced Transceiver Technologies for 6G and Beyondcited by 8
  4. 2021Trimming the Fat from OFDM: Pilot- and CP-less Communication with End-to-end Learningcited by 6
  5. 2025Towards Sustainability in 6G and beyond: Challenges and Opportunities of Open RANcited by 5
  6. 2024Swin Transformer-Based CSI Feedback for Massive MIMOcited by 4
  7. 2024Learning at the Speed of Wireless: Online Real-Time Learning for AI-Enabled MIMO in NextGcited by 3
  8. 2024Deep Learning for CSI Feedback: One-Sided Model and Joint Multi-Module Learning Perspectivescited by 3
  9. 2023Low PAPR MIMO-OFDM Design Based on Convolutional Autoencodercited by 2
  10. 2023Signal Processing and Learning for Next Generation Multiple Access in 6Gcited by 2
  11. 2024Interference Cancellation Based Neural Receiver for Superimposed Pilot in Multi-Layer Transmissioncited by 2
  12. 2024On Transfer Learning for a Fully Convolutional Deep Neural SIMO Receivercited by 2
  13. 2023Constellation Shaping under Phase Noise Impairment for Sub-THz Communicationscited by 1
  14. 2024Wireless Information and Energy Transfer in the Era of 6G Communicationscited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026EqDeepRx: Learning a Scalable and Interference Mitigating MIMO Receivercited by 1
  2. 2026Efficient Transceiver Design for Aerial Image Transmission and Large-scale Scene Reconstructioncited by 0
  3. 2026CARNet: Channel-Adaptive Receiver Network for Robust NextG Communicationscited by 0