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写在前面:该文写于2023.6.1,整理了IEEE上最新OTFS方面(大多为2023年发表)顶刊论文,主要涉及到分数多普勒问题(一共44篇,每次更新5篇)。写文的初衷是为了理解分数多普勒的问题背景,并了解现有针对分数多普勒问题的一些成熟解决方案,因此在时间有限的前提下只针对摘要核心部分(问题背景、解决方法、简要细节)做出翻译(关于翻译的准确性仅供参考,对于不准确的地方欢迎指正),并对摘要中核心的部分进行标红或划线处理,同时对英文摘要进行拆分,方便大家快速找到所需要的内容,摘要最后写了总结,大多为概括以及本人的一些简单想法。
1
翻译:
总结:论文针对分数多普勒精度不够高的问题,提出了一个通过补零的解决方案,初步来看值得精读。
2
原文:
内容概括:This letter presents an early investigation of reconfigurable intelligent surfaces (RIS)-aided orthogonal time-frequency space (OTFS) in high-Doppler channels. 算法细节:We derive the end-to-end delay-Doppler (DD) domain input-output relation of a RIS-aided OTFS system, considering rectangular pulses and fractional delay-Doppler values. We also consider a Zak receiver for RIS-aided OTFS that converts the received time-domain signal to DD domain in one step using Zak transform, and derive its end-to-end input-output relation. Our simulation results show that i ) RIS-aided OTFS performs better than OTFS without RIS, ii ) Zak receiver performs better than a two-step receiver, and iii ) RIS-aided OTFS achieves superior performance compared to RIS-aided OFDM.
翻译:
3
原文:
问题背景:In this paper, we consider an orthogonal time frequency space (OTFS) modulation |based single input single output (SISO) radar |for integrated sensing and communication (ISAC) |where the transmitter sends OTFS frames |and the radar receiver co-located with the transmitter leverages the transmit frame along with the reflected signal |for the estimation of the target range and velocity. 本文算法:We present a novel radar channel model| that is capable of capturing both integer as well as fractional delays and Doppler shifts accounted for by the targets in the channel. 算法细节:A two-step modified orthogonal matching pursuit with fractional refinement (OMPFR) algorithm is also proposed to estimate the delays and Doppler shifts that can be non-integer multiples of the system delay and Doppler resolutions respectively. 仿真结果:We show that our algorithm has much lower complexity than the traditional orthogonal matching pursuit (OMP) algorithm if used in estimating the fractional delays and Dopplers, as the latter requires a colossal dictionary matrix. Target profiles and the root mean square error (RMSE) plots for range and velocity estimates validate the effectiveness of the proposed OMPFR algorithm.
翻译:
4
原文:
背景:Orthogonal time frequency space (OTFS) waveform is suited for both communication as well as radar sensing. 本文算法:In this letter, we propose an algorithm for efficient channel estimation at the receiver and range/velocity estimation at the transmitter using OTFS. 算法细节:The algorithm processes received pilot frames for channel estimation at the receiver |and data frames echoed from the target/user for range and velocity estimation at the transmitter. A key component in the proposed algorithm is the cancellation of inter-path interference (IPI) in the DD domain. The algorithm works for fractional delay-Doppler which is a source of IPI. 仿真结果:The proposed algorithm outperforms other channel estimation schemes and also achieves good root mean square error performance of range and velocity estimation.
翻译:
16
原文:
本文方法:In this paper, we proposed a deep neural network (DNN) based fractional Doppler channel estimation scheme for orthogonal time frequency space (OTFS) modulation in the air-to-ground communication scenario with high-dynamic Doppler. 算法细节:Based on the zero-padded OTFS structure, the traditional pilot pattern with guard symbols is adopted. The received pilots in the OTFS domain are used as the inputs of the network to estimate the channel parameters which are used in the MRC algorithm to demodulate the signal.仿真结果: In our proposed method, it can achieve the similar performance with 14 dB boost of pilot energy comparing with the ideal channel estimation case, while the conventional method requires 30 dB higher. Both the accuracy and generalization ability of the DNN network are validated.
翻译:
总结:该论文也引入了补零的思想,可以和论文1结合起来阅读,同时,思考DNN在论文中取代了什么算法,其优势是什么?
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