Communications in highly dynamic channels relying on training-based channel estimation experience a trade-off between increasing channel measurement accuracy by sending more frequent training sequences and increasing data rate by sending fewer training sequences. Simultaneously, most communication systems use forward error correction to enable error detection and correction at the receiver. This paper presents decoder-provided pilots for time-varying channels by using decoded codewords as training sequences to update the channel estimate at the receiver. In contrast to approaches such as data-aided channel estimation, decision-feedback equalization, joint channel estimation and error correction, and turbo equalization, the decoder-provided pilots approach is non-iterative, which is ideal for low-latency requirements in highly dynamic scenarios. Furthermore, it is modulation-, code-, and decoder-agnostic, meaning it can be implemented on top of virtually any communication system that uses forward error correction. From an information-theoretic perspective, we derive the fundamental limits of decoder-provided pilots' ability to simultaneously sense the channel and transmit data. Simulation results demonstrate that decoder-provided pilots significantly improve performance, that when coding across frequency, soft-output can further enhance performance, and that when coding across time, short codes can outperform long codes of the same rate in fast-fading channels.

翻译：在高动态信道中，基于训练的信道估计通信面临一个权衡：发送更频繁的训练序列以提高信道测量精度，与发送更少的训练序列以提升数据速率。同时，大多数通信系统采用前向纠错实现接收端的错误检测与纠正。本文提出一种适用于时变信道的译码器辅助导频方法，通过将已译码的码字作为训练序列来更新接收端的信道估计。与数据辅助信道估计、判决反馈均衡、联合信道估计与纠错以及Turbo均衡等方法不同，译码器辅助导频方法无需迭代，非常适用于高动态场景的低时延需求。此外，该方法与调制方式、编码方案及译码器类型无关，可集成于任何采用前向纠错的通信系统之上。从信息论角度出发，我们推导了译码器辅助导频在同时感知信道和传输数据时的基本性能极限。仿真结果表明：译码器辅助导频能显著提升性能；当编码跨频域时，软输出可进一步增强性能；而在编码跨时域时，快速衰落信道中短码的性能可能优于相同码率的长码。