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arXiv 2412.04751eess.SP
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ISAC-OTFS空地传输的预均衡设计:一种深度学习方法

Pre-equalization Design for ISAC-OTFS Air-Ground Transmission: A Deep Learning Approach

School of Automation and Intelligent Manufacturing, Southern University of Science and Technology(南方科技大学自动化与智能制造学院)

Weihao Wang, Jing Guo, Siqiang Wang, Xinyi Wang, Weijie Yuan, Zesong Fei

AI总结:

本文针对OTFS在空地传输中接收端均衡复杂度高的问题,提出基于深度学习信道预测的地面预均衡ISAC-OTFS框架,使无人机无需均衡即可检测符号,在降低复杂度和导频开销的同时接近完美CSI下的检测性能并提升感知精度。

摘要:

尽管正交时频空(OTFS)调制具有很强的多普勒鲁棒性,但它在接收端存在较高的信道估计与均衡复杂度,这阻碍了其在空地传输中的应用。本文提出了一种基于预均衡的感知通信一体化-OTFS下行传输框架,其中地面接入点利用预测的信道状态信息(CSI)执行预均衡,从而使无人机无需信道均衡即可进行直接符号检测。特别地,考虑了OTFS符号解调的均方误差和感知参数估计的克拉美罗下界,并以二者的加权和作为优化预均衡矩阵的指标。为了应对时变CSI,我们开发了一个由信道预测和预均衡组成的基于深度学习的框架。具体而言,利用参数级信道预测模块对OTFS信道参数进行解耦,并利用低维预测网络对过时的CSI进行校正,然后将其用于初始化预均衡模块的输入。最后,级联一个双分支残差结构深度神经网络来执行预均衡。仿真结果表明,所提出的基于信道预测的预均衡框架显著降低了接收机复杂度和导频开销,同时在高移动性下实现了接近具有完美CSI的最小均方误差均衡的符号检测性能,并大幅提升了感知精度。

英文摘要:

Despite the strong Doppler resilience capability, orthogonal time-frequency space (OTFS) modulation suffers from high channel estimation and equalization complexity at the receiver, hindering its applicability in air-ground transmission. In this paper, we propose a pre-equalization-based integrated sensing and communications-OTFS downlink transmission framework in which the terrestrial access point executes pre-equalization using the predicted channel state information (CSI), so that the unmanned aerial vehicle can perform direct symbol detection without channel equalization. In particular, the mean square error of OTFS symbol demodulation and Cramer-Rao lower bound of sensing parameter estimation are considered, with their weighted sum utilized as the metric for optimizing the pre-equalization matrix. To address the time-varying CSI, we develop a deep learning based framework composed of channel prediction and pre-equalization. In particular, a parameter-level channel prediction module is utilized to decouple OTFS channel parameters, and a low-dimensional prediction network is leveraged to correct outdated CSI, which is then used to initialize the input of the pre-equalization module. Finally, a dual-branch residual-structured deep neural network is cascaded to execute pre-equalization. Simulation results show that the proposed channel prediction-based pre-equalization framework significantly reduces receiver complexity and pilot overhead while achieving symbol detection performance close to minimum mean square error equalization with perfect CSI under high mobility, as well as substantially improving sensing accuracy.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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