跳转至

文章背景与核心概要

在现代无线通信系统中，频谱效率（SE）的提升一直是核心挑战。传统的正交频分复用（OFDM）多输入多输出（MIMO）系统通常将解调参考信号（DMRS）与数据符号在时频资源上进行非重叠隔离传输，这在一定程度上造成了宝贵时频资源的浪费。为了突破这一瓶颈，本文研究了一种将DMRS与数据符号进行叠加（Superimposed, SI）传输的新型无线通信方案。

为了解决叠加传输带来的信道估计（CE）与MIMO检测（MD）之间的强耦合干扰，作者建立了一套严格的分析框架，用于表征迭代信道估计与检测（ICED）过程中均方误差（MSEs）的迭代关系，并在此基础上实现了SI-DMRS传输的最优功率分配与导频图样优化。此外，本文创新性地设计了一种基于Transformer编码器的AI接收机，将ICED结构深度融入其中。仿真结果表明，该AI-ICED接收机与SI-DMRS技术的结合，能够显著提升系统的频谱效率，展现出巨大的应用潜力。

Optimal Power Allocation and AI Receiver Design for Superimposed DMRS and Data Transmission

Summary

This paper investigates wireless transmissions that use superimposed (SI) demodulation-reference-symbols (DMRS) and data within orthogonal frequency-division multiplexing (OFDM) based multiple-input multiple-output (MIMO) systems. The authors establish an analytical framework to characterize the iterative relationship between channel estimation (CE) and MIMO detection (MD) mean-square errors (MSEs) during an iterative CE and detection (ICED) process. This framework is subsequently applied to optimize power allocation and pilot patterns for SI-DMRS transmissions. Furthermore, the study introduces an artificial intelligence (AI)-based receiver built on Transformer encoders, featuring an integrated ICED structure. Simulation results confirm that combining the proposed AI-ICED receiver with SI-DMRS significantly improves spectral efficiency (SE) compared to traditional non-overlapped DMRS and data transmission methods.

Paper Metadata

Field Details
arXiv Identifier arXiv:2608.13809 [cs.IT]
Subjects Information Theory (cs.IT); Artificial Intelligence (cs.AI)
Authors Sha Hu, Zhongwang Fu
Submitted On August 13, 2026
Length & Scope 29 pages, 12 figures, 4 tables
License Creative Commons Attribution-NonCommercial-NoDerivatives 4.0

Abstract

本文研究了在基于正交频分复用（OFDM）的多输入多输出（MIMO）系统中使用叠加（SI）解调参考符号（DMRS）和数据的无线传输。首先，我们推导了一个分析框架，用于表征迭代信道估计与检测（ICED）过程中，信道估计（CE）和MIMO检测（MD）均方误差（MSE）之间的迭代行为。随后，该框架被用于优化SI-DMRS传输中DMRS与数据符号之间的功率分配和导频图样。其次，我们为SI-DMRS传输设计了一种基于Transformer编码器的人工智能（AI）接收机，该接收机融合了迭代信道估计与检测（ICED）结构。仿真结果表明，与使用非重叠DMRS和数据符号的传统系统相比，所提出的AI-ICED接收机结合SI-DMRS有效地提高了频谱效率（SE）。

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.

Associated Media & Resources