AI驱动的无线收发器，无导频无循环前缀，性能更优。
Adaptive End-to-End Transceiver Design for NextG Pilot-Free and CP-Free Wireless Systems
- 用神经网络联合训练调制星座与接收机，实现端到端自适应。
- 在多种信道下误码率更低，吞吐量更高，支持多阶调制统一建模。
- 轻量适配模块快速应对信道变化，且满足高功耗比限制。
人工智能原生无线通信正重塑下一代（NextG）系统的设计范式，智能空口需在高度动态环境中自适应高效运行。传统正交频分复用（OFDM）系统依赖导频和循环前缀（CP），造成显著开销，降低频谱效率。为此，我们提出一种面向无导频、无循环前缀系统的自适应端到端（E2E）收发架构，结合AI驱动的星座整形与神经接收机，通过联合训练优化性能。为增强对信道失配或时变条件的鲁棒性，引入轻量级信道适配（CA）模块，仅更新其参数即可实现快速适应，计算开销极低。此外，框架支持统一模型下多调制阶数扩展，显著降低模型存储需求。针对OFDM固有的高峰均功率比（PAPR）问题，采用约束式端到端训练，在不增加传输开销的前提下达成PAPR目标。大量仿真表明，该框架在各类信道场景中均展现出更优的误码率（BER）、吞吐量及鲁棒性，凸显其在AI原生NextG中的应用潜力。
原文摘要 · Abstract (English)
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.
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