通过叠加导频与数据提升频谱效率，设计智能接收机实现更优通信。
Optimal Power Allocation and AI Receiver Design for Superimposed DMRS and Data Transmission
- 构建迭代信道估计与检测框架，优化导频与数据功率分配。
- 提出基于Transformer的智能接收机，显著提升系统频谱效率。
- 适合5G/6G高速通信系统研究者参考，尤其关注资源复用技术。
本文研究正交频分复用（OFDM）MIMO系统中导频与数据叠加（SI-DMRS）传输。首先，推导出信道估计（CE）与多输入多输出检测（MD）间均方误差（MSE）迭代行为的解析框架，并用于优化导频与数据符号的功率分配及导频图案。其次，设计一种基于Transformer编码器的人工智能（AI）接收机，融合迭代信道估计与检测（ICED）结构。仿真结果表明，所提AI-ICED接收机结合SI-DMRS，相比传统非重叠导频与数据系统，显著提升频谱效率（SE）。
原文摘要 · Abstract (English)
In this paper, we consider transmissions with superimposed (SI) demodulation-reference-symbol (DMRS) and data in orthogonal frequency-division multiplexing (OFDM) based multiple-input multiple-output (MIMO) systems. First, we derive an analytical framework to characterize the iterative behavior between the mean-square errors (MSEs) of channel estimation (CE) and MIMO detection (MD) within an iterative CE and detection (ICED) process. This framework is subsequently utilized to optimize power allocation and pilot patterns between the DMRS and data symbols for SI-DMRS transmission. Second, we design an artificial intelligence (AI) based receiver built upon Transformer encoders for SI-DMRS transmissions, which incorporates an iterative CE and detection (ICED) structure. Simulation results demonstrate that the proposed AI-ICED receiver, combined with SI-DMRS, effectively increases spectral efficiency (SE) compared to conventional systems using non-overlapped DMRS and data symbols.
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