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

TL;DR

This paper introduces an adaptive AI-driven transceiver architecture for NextG wireless systems that eliminates pilots and cyclic prefix, improving spectral efficiency, robustness, and scalability in dynamic environments.

Contribution

It proposes a novel joint-trained AI-based transceiver with a lightweight channel adapter and PAPR constraints, enabling efficient, adaptive, and scalable pilot-free, CP-free wireless communication.

Findings

Superior BER and throughput in simulations

Enhanced robustness to channel variations

Reduced model storage for multiple modulation schemes

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.