arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2305.04414eess.SP
收藏

基于未训练神经网络的OTFS调制系统贝叶斯检测器

Untrained Neural Network based Bayesian Detector for OTFS Modulation Systems

Hao Chang, Alva Kosasih, Wibowo Hardjawana, Xinwei Qu, Branka Vucetic

AI总结:

本文针对OTFS高移动性符号检测问题,提出基于深度图像先验的未训练去噪器D-DIP,并将其与贝叶斯并行干扰消除检测器结合为D-DIP-BPIC,在无需训练且保持低复杂度的情况下实现优于现有检测器0.5 dB的符号错误率性能。

摘要:

针对高移动性通信场景的正交时频空(OTFS)符号检测器设计近来受到广泛关注。当前最先进的OTFS检测器主要可分为两类:迭代检测器和基于训练的深度神经网络(DNN)检测器。许多实用的迭代检测器依赖最小均方误差(MMSE)去噪器来获得初始符号估计,然而其计算复杂度随检测符号数量呈指数增长。基于训练的DNN检测器通常受制于对大量计算资源的依赖以及训练阶段合成数据集的保真度,而这两者成本都很高。本文提出一种基于深度图像先验(DIP)和解码器架构的未训练DNN,称为D-DIP,用于替代迭代检测器中的MMSE去噪器。DIP是一种无需训练的DNN类型,这使其在OTFS检测器设计中具有优势。随后,我们提出将D-DIP去噪器与贝叶斯并行干扰消除(BPIC)检测器相结合以执行迭代符号检测,称为D-DIP-BPIC。仿真结果表明,所提出的D-DIP-BPIC检测器的符号错误率(SER)性能优于实用的最先进检测器0.5 dB,同时保持较低的计算复杂度。

英文摘要:

The orthogonal time frequency space (OTFS) symbol detector design for high mobility communication scenarios has received numerous attention lately. Current state-of-the-art OTFS detectors mainly can be divided into two categories; iterative and training-based deep neural network (DNN) detectors. Many practical iterative detectors rely on minimum-mean-square-error (MMSE) denoiser to get the initial symbol estimates. However, their computational complexity increases exponentially with the number of detected symbols. Training-based DNN detectors typically suffer from dependency on the availability of large computation resources and the fidelity of synthetic datasets for the training phase, which are both costly. In this paper, we propose an untrained DNN based on the deep image prior (DIP) and decoder architecture, referred to as D-DIP that replaces the MMSE denoiser in the iterative detector. DIP is a type of DNN that requires no training, which makes it beneficial in OTFS detector design. Then we propose to combine the D-DIP denoiser with the Bayesian parallel interference cancellation (BPIC) detector to perform iterative symbol detection, referred to as D-DIP-BPIC. Our simulation results show that the symbol error rate (SER) performance of the proposed D-DIP-BPIC detector outperforms practical state-of-the-art detectors by 0.5 dB and retains low computational complexity.

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。

↑