叠加DMRS与数据传输的最优功率分配及AI接收机设计
Optimal Power Allocation and AI Receiver Design for Superimposed DMRS and Data Transmission
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- Nyquist Research Center（奈奎斯特研究中心）
- Huawei Technologies Sweden AB（华为技术瑞典有限公司）
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中文总结 AI 辅助
针对OFDM-MIMO系统中叠加DMRS与数据的传输问题，推导迭代CE与MD的分析框架优化功率分配，设计带ICED结构的AI-ICED接收机，可提升频谱效率。
中文摘要 AI 辅助
本文研究基于正交频分复用（OFDM）的多输入多输出（MIMO）系统中，叠加（SI）解调参考符号（DMRS）与数据的传输问题。首先，推导分析框架以刻画迭代信道估计（CE）与MIMO检测（MD）过程中，信道估计均方误差（MSE）与MIMO检测均方误差的迭代行为，该框架后续用于优化SI-DMRS传输中DMRS与数据符号间的功率分配和导频图案。其次，设计基于Transformer编码器的AI接收机，该接收机内置迭代信道估计与检测（ICED）结构，适用于SI-DMRS传输。仿真结果表明，所提AI-ICED接收机结合SI-DMRS，与采用非重叠DMRS和数据符号的传统系统相比，可有效提升频谱效率（SE）。
英文摘要
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.
