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

2025-10-29

AI Summary

Traditional OFDM systems rely on pilot symbols and cyclic prefixes (CP), resulting in low spectral efficiency and high overhead. To address this, we propose a pilot- and CP-free adaptive end-to-end wireless transceiver architecture. Our method jointly optimizes AI-driven dynamic constellation shaping and a neural receiver, embeds a lightweight channel adaptation module enabling unified modeling of multi-order modulations and rapid online fine-tuning, and directly constrains the peak-to-average power ratio (PAPR) via end-to-end training. Simulation results demonstrate that the proposed framework significantly outperforms conventional OFDM across diverse time-varying channels—achieving lower bit error rates, higher throughput, and enhanced robustness. This work provides a scalable, AI-native solution for pilot-free communications in 6G systems.

Technology Category

• Search and Optimization: Learning to Search
• Machine Learning: Deep Neural Architectures and Foundation Models
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

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.

Problem

• Eliminates pilot and cyclic prefix overhead in wireless systems
• Enables adaptive communication under dynamic channel conditions
• Reduces peak-to-average power ratio without transmission overhead

Innovation

• AI-driven constellation shaping with neural receiver
• Lightweight channel adapter for rapid adaptation
• Constrained training for PAPR compliance