一种低复杂度的正交时频空系统接收机设计

正交时频空(OTFS)调制可以将时间和频率选择性信道转换为时延-多普勒(DD)域的非选择性信道,这为高速移动场景建立可靠的无线通信提供了解决方案。然而,在车联网等复杂的多散射场景下,信道存在严重的多普勒间干扰(IDI),这给OTFS接收机信号的准确解调带来了极大的挑战。针对上述问题,该文提出一种联合稀疏贝叶斯学习(SBL)和阻尼最小二乘最小残差(d-LSMR)的OTFS接收机设计。首先,根据OTFS时域和DD域的关系,采用基扩展模型(BEM)将信道估计问题转换为基系数恢复问题,精准估计包括多普勒采样点在内的DD域信道。然后,提出一种高效的转换算法将基系数转换为信道等效矩阵。其次,将信道估计中估计得到的噪声,用于d-LSMR均衡器中进行信道均衡,并利用DD域信道矩阵的稀疏性实现快速收敛。系统仿真结果表明,与目前代表性的OTFS接收机相比,该文所提方案实现了更好的误码率性能,同时降低了计算复杂度。

面向车联网通信的OTFS信号检测算法综述

车联网(Vehicle to Everything,V2X)通信被认为是未来无线通信网络最重要的应用之一。然而,车辆在高速移动时引起的高多普勒频移会严重恶化V2X通信链路的性能。正交时频空(Orthogonal Time Frequency Space,OTFS)调制技术可以将时间和频率选择性信道转换为时延-多普勒(Delay-Doppler,DD)域的非选择性信道,从而显著提高无线通信系统在高移动性场景下的性能,在V2X通信中具有重要的应用价值。但OTFS调制技术极大地增加了系统接收端的复杂度,研究低复杂度信号检测算法成为了新一代无线通信系统采用OTFS调制的关键问题之一。为此,综述了面向车联网V2X通信的OTFS信号检测算法。首先介绍了OTFS系统模型,然后概述了现有的低复杂度OTFS信号检测算法,并将其分为线性检测算法、消息传递(Message Passing,MP)检测算法及其改进算法、基于神经网络的检测算法3类,最后探讨了V2X通信中OTFS信号检测目前所面临的技术挑战与未来的发展趋势。

基于模型驱动深度学习的OTFS检测方法

正交时频空(Orthogonal Time Frequency Space, OTFS)调制技术凭借对多普勒频移的优良抗性,保证了高动态场景下的可靠性通信。与大多数OTFS信号检测方案相比,基于深度学习(Deep Learning, DL)的OTFS检测器不需要耗费高额的导频能量,以此获得精确的信道状态信息。基于多维输入的卷积神经网络(Convolutional Neural Networks, CNN)和一维输入的深度神经网络(Deep Neural Networks, DNN),搭建了OTFS信号检测模型,并结合OTFS的输入输出关系,以模型驱动,提出一种部分输入方法。与数据驱动DL相比,该方法沿时延轴截断输入数据,仅向网络输入与待检测信号相关性强的部分接收信号。该方法不仅减小了数据驱动CNN和DNN的训练参数量,降低了训练复杂度,而且检测性能也不弱于传统的线性最小均方误差(Linear Minimum Mean Square Error, LMMSE)算法。

面向6G快时变信道下OTFS系统的信号检测

OTFS调制可以有效解决无线信道的快速时变特性给信号检测带来的巨大挑战,可以为高速移动设备提供可靠的无线通信。针对最大似然算法直接应用于OTFS系统进行信号检测时复杂度过高的问题,提出基于并行干扰消除的最大似然检测算法。在延时多普勒域,信道呈现正交、稀疏、可分离特性,发送信号和信道信息呈现出二维卷积的形式。基于此性质同时利用已检测信号,在检测阶段去除已检测信号形成的干扰,降低最大似然算法的复杂度。在此基础上在发送端插入零值使得接收信号在延时方向实现多径分离,进一步降低算法的复杂度。数值仿真结果表明,所提算法在OTFS系统中能够实现高准确率的信号检测,可以有效对抗高速移动场景下的快时变信道。

UAMP-Based Delay-Doppler Channel Estimation for OTFS Systems

Orthogonal time frequency space(OTFS)technique, which modulates data symbols in the delayDoppler(DD) domain, presents a potential solution for supporting reliable information transmission in highmobility vehicular networks. In this paper, we study the issues of DD channel estimation for OTFS in the presence of fractional Doppler. We first propose a channel estimation algorithm with both low complexity and high accuracy based on the unitary approximate message passing(UAMP), which exploits the structured sparsity of the effective DD domain channel using hidden Markov model(HMM). The empirical state evolution(SE) analysis is then leveraged to predict the performance of our proposed algorithm. To refine the hyperparameters in the proposed algorithm,we derive the update criterion for the hyperparameters through the expectation-maximization(EM) algorithm. Finally, Our simulation results demonstrate that our proposed algorithm can achieve a significant gain over various baseline schemes.

基于OTFS的车联网新型通信与感知一体化技术分析

5G技术的发展为车辆通信感知提供了更多可能性,结合案例探讨正交时频空间(orthogonal time frequency space,OTFS)技术在车辆通信感知中的应用,实现车辆远程驾驶的基本目标。从车联网通信系统调制角度出发,建立系统架构,分析感知一体化技术的实现过程。研究结果表明,将OTFS技术应用到车联网通信中能够应对车辆行驶过程中复杂的环境变化,使得信号传输控制更加精确、平稳。

OTFS调制系统的低复杂度GAMP算法实现

针对正交时频空(Orthogonal Time Frequealy Space,OTFS)调制技术信号检测算法复杂度高的问题,结合OTFS信道矩阵特性构建匹配滤波器,在广义近似消息传递(Generalized Approximate Message Passing,GAMP)算法的基础上,提出了基于期望最大化(Empectation Maxinization,EM)的阻尼广义消息传递算法。相较于传统的消息传递(Message Passing,MP)、近似消息传递(Approximate Message Passing,AMP)和GAMP等检测方式,所提算法有更低复杂度。在500 km/h的高动态场景中对该算法进行仿真,其误码率性能优于最小均方差(Minimum Mean Squared Error,MMSE)和传统的GAMP检测算法,并且具有更好的收敛性。

Joint Multi-Domain Channel Estimation Based on Sparse Bayesian Learning for OTFS System

Since orthogonal time-frequency space(OTFS)can effectively handle the problems caused by Doppler effect in high-mobility environment,it has gradually become a promising candidate for modulation scheme in the next generation of mobile communication.However,the inter-Doppler interference(IDI)problem caused by fractional Doppler poses great challenges to channel estimation.To avoid this problem,this paper proposes a joint time and delayDoppler(DD)domain based on sparse Bayesian learning(SBL)channel estimation algorithm.Firstly,we derive the original channel response(OCR)from the time domain channel impulse response(CIR),which can reflect the channel variation during one OTFS symbol.Compare with the traditional channel model,the OCR can avoid the IDI problem.After that,the dimension of OCR is reduced by using the basis expansion model(BEM)and the relationship between the time and DD domain channel model,so that we have turned the underdetermined problem into an overdetermined problem.Finally,in terms of sparsity of channel in delay domain,SBL algorithm is used to estimate the basis coefficients in the BEM without any priori information of channel.The simulation results show the effectiveness and superiority of the proposed channel estimation algorithm.

Data-Driven Deep Learning for OTFS Detection

Recently,orthogonal time frequency space(OTFS)was presented to alleviate severe Doppler effects in high mobility scenarios.Most of the current OTFS detection schemes rely on perfect channel state information(CSI).However,in real-life systems,the parameters of channels will constantly change,which are often difficult to capture and describe.In this paper,we summarize the existing research on OTFS detection based on data-driven deep learning(DL)and propose three new network structures.The presented three networks include a residual network(ResNet),a dense network(DenseNet),and a residual dense network(RDN)for OTFS detection.The detection schemes based on data-driven paradigms do not require a model that is easy to handle mathematically.Meanwhile,compared with the existing fully connected-deep neural network(FC-DNN)and standard convolutional neural network(CNN),these three new networks can alleviate the problems of gradient explosion and gradient disappearance.Through simulation,it is proved that RDN has the best performance among the three proposed schemes due to the combination of shallow and deep features.RDN can solve the issue of performance loss caused by the traditional network not fully utilizing all the hierarchical information.

Channel Estimation for OTFS System over Doubly Spread Sparse Acoustic Channels

This paper addresses sparse channels estimation problem for the generalized linear models(GLM)in the orthogonal time frequency space(OTFS)underwater acoustic(UWA)system.OTFS works in the delay-Doppler domain,where timevarying channels are characterized as delay-Doppler impulse responses.In fact,a typical doubly spread UWA channel is associated with several resolvable paths,which exhibits a structured sparsity in the delayDoppler domain.To leverage the structured sparsity of the doubly spread UWA channel,we develop a structured sparsity-based generalized approximated message passing(GAMP)algorithm for reliable channel estimation in quantized OTFS systems.The proposed algorithm has a lower computational complexity compared to the conventional Bayesian algorithm.In addition,the expectation maximum algorithm is employed to learn the sparsity ratio and the noise variance.Simulation and experimental results show that the proposed algorithm has superior performance and low computational complexity for quantized OTFS systems.

New Delay Doppler Communication Paradigm in 6G Era:A Survey of Orthogonal Time Frequency Space(OTFS)

In the 6G era,Space-Air-Ground Integrated Network(SAGIN)are anticipated to deliver global coverage,necessitating support for a diverse array of emerging applications in high-mobility,hostile environments.Under such conditions,conventional orthogonal frequency division multiplexing(OFDM)modulation,widely employed in cellular and Wi-Fi communication systems,experiences performance degradation due to significant Doppler shifts.To overcome this obstacle,a novel twodimensional(2D)modulation approach,namely orthogonal time frequency space(OTFS),has emerged as a key enabler for future high-mobility use cases.Distinctively,OTFS modulates information within the delay-Doppler(DD)domain,as opposed to the timefrequency(TF)domain utilized by OFDM.This offers advantages such as Doppler and delay resilience,reduced signaling latency,a lower peak-to-average ratio(PAPR),and a reduced-complexity implementation.Recent studies further indicate that the direct interplay between information and the physical world in the DD domain positions OTFS as a promising waveform for achieving integrated sensing and communications(ISAC).In this article,we present an in-depth review of OTFS technology in the context of the 6G era,encompassing fundamentals,recent advancements,and future directions.Our objective is to provide a helpful resource for researchers engaged in the field of OTFS.

Low PAPR Channel Estimation for OTFS with Scattered Superimposed Pilots

Orthogonal time frequency space(OTFS)modulation has been proven to be superior to traditional orthogonal frequency division multiplexing(OFDM)systems in high-speed communication scenarios.However,the existing channel estimation schemes may results in poor peak to average power ratio(PAPR)performance of OTFS system or low spectrum efficiency.Hence,in this paper,we propose a low PAPR channel estimation scheme with high spectrum efficiency.Specifically,we design a multiple scattered pilot pattern,where multiple low power pilot symbols are superimposed with data symbols in delay-Doppler domain.Furthermore,we propose the placement rules for pilot symbols,which can guarantee the low PAPR.Moreover,the data aided iterative channel estimation was invoked,where joint channel estimation is proposed by exploiting multiple independent received signals instead of only one received signal in the existing scheme,which can mitigate the interference imposed by data symbols for channel estimation.Simulation results shows that the proposed multiple scattered pilot aided channel estimation scheme can significantly reduce the PAPR while keeping the high spectrum efficiency.

基于OTFS技术的新应用研究

为了解基于正交时频空(orthogonal time frequency space,OTFS)技术的新应用现状,研究正交频分复用(orthogonal frequency division multiplexing,OFDM)技术和OTFS技术的应用原理,并分析OTFS技术在水声(underwater acoustic,UWA)通信、车联网等高移动性通信感知一体化场景中的应用情况,包括应用现状、所面临的挑战以及对未来的展望,以期为相关人员提供参考。

BER Performance Analysis of OTFS Systems with Power Allocation

Orthogonal time frequency space(OTFS),as a novel 2-D modulation technique,has been proposed to achieve better BER performances over delayDoppler channels.In this paper,we propose two different power allocation(PA)algorithms in OTFS systems with zero forcing(ZF)or minimum mean square error(MMSE)equalization,where general formulas with PA are derived in advance under the condition of minimum BER(MBER)criterion.On one hand,a suboptimal MBER power allocation method is put forward to achieve better BER performances,and then analytical BER expressions are derived with proposed PA strategy.Considering the case of MMSE equalization,a combined subsymbol allocation(SA)and PA strategy is raised,where some subsymbols may be abandoned due to worse channel conditions,and then it is proven effectively to improve BER performances through theoretical and simulation results.Furthermore,BER performances with proposed joint SA and PA strategy are also investigated in delay-Doppler channels,where an improved message passing(MP)receiver based on equivalent channel matrix with PA is given.

Radar Sensing via OTFS Signaling

By multiplexing information symbols in the delay-Doppler(DD)domain,orthogonal time frequency space(OTFS)is a promising candidate for future wireless communication in high-mobility scenarios.In addition to the superior communication performance,OTFS is also a natural choice for radar sensing since the primary parameters(range and velocity of targets)in radar signal processing can be inferred directly from the delay and Doppler shifts.Though there are several works on OTFS radar sensing,most of them consider the integer parameter estimation only,while the delay and Doppler shifts are usually fractional in the real world.In this paper,we propose a two-step method to estimate the fractional delay and Doppler shifts.We first perform the two-dimensional(2D)correlation between the received and transmitted DD domain symbols to obtain the integer parts of the parameters.Then a difference-based method is implemented to estimate the fractional parts of delay and Doppler indices.Meanwhile,we implement a target detection method based on a generalized likelihood ratio test since the number of potential targets in the sensing scenario is usually unknown.The simulation results show that the proposed method can obtain the delay and Doppler shifts accurately and get the number of sensing targets with a high detection probability.

Secrecy Transmission of NOMA-OTFS Based Multicast-Unicast Streaming

This paper investigates the security performance of a cooperative multicast-unicast system,where the users present the feature of high mobility.Specifically,we develop the non-orthogonal multiple access(NOMA)based orthogonal time frequency space(OTFS)transmission scheme,namely NOMAOTFS,in order to combat Doppler effect as well as to improve the spectral efficiency.Further,we propose a power allocation method addressing the trade-off between the reliability of multicast streaming and the confidentiality of unicast streaming.Based on that,we utilize the relay selection strategy,to improve the security of unicast streaming.In the context of multicastunicast streaming,our simulation findings validate the effectiveness of the NOMA-OTFS based cooperative transmission,which can significantly outperform the existing NOMA-OFDM in terms of both reliability and security.

Trajectory Planning for OTFS-Based UAV Communications

Unmanned aerial vehicles(UAVs)have attracted growing research interests in recent years,which can be used as cost-effective aerial platforms to transmit collected data packets to ground access points(APs).Thus,it is crucial to investigate robust airto-ground(A2G)wireless links for high-speed UAVs.However,the A2G wireless link is unstable as it suffers from large path-loss and severe Doppler effect due to the high mobility of UAVs.In order to meet these challenges,we propose an orthogonal time frequency space(OTFS)-based UAV communication system to relief the Doppler effect.Besides,considering that the energy of UAV is limited,we optimize the trajectory planning of UAV to minimize the energy consumption under the constraints of bit error rate(BER)and transmission rate,where the Doppler compensation is taken into account.Simulation results show that the performance of OTFS-based UAV system is superior to orthogonal frequency division multiplexing(OFDM)-based UAV systems,which can accomplish transmission tasks over shorter distances with lower energy consumption.

OTFS Modulation and PAPR Reduction for IoT-Railways

The internet of things(IoT)has been widely considered to be integrated with high-speed railways to improve safety and service.It is important to achieve reliable communication in IoT for railways(IoT-R)under high mobility scenarios and strict energy constraints.Orthogonal time frequency space(OTFS)modulation is a two-dimensional modulation technique that has the potential to overcome the challenges in high Doppler environments.In addition,OTFS can have lower peak-to-average power ratio(PAPR)compared to orthogonal frequency division multiplexing,which is especially important for the application of IoT-R.Therefore,OTFS modulation for IoT-R is investigated in this paper.In order to decrease PAPR of OTFS and promote the application of OTFS modulation in IoT-R,the peak windowing technique is used in this paper.This technique can reduce the PAPR of OTFS by reducing the peak power and does not require multiple iterations.The impacts of different window functions,window sizes and clipping levels on PAPR and bit error rate of OTFS are simulated and discussed.The simulation results show that the peak windowing technique can efficiently reduce the PAPR of OTFS for IoT-R.

UAMP-Based Delay-Doppler Channel Estimation for OTFS Systems

Orthogonal time frequency space(OTFS)technique,which modulates data symbols in the delay-Doppler(DD)domain,presents a potential solution for supporting reliable information transmission in highmobility vehicular networks.In this paper,we study the issues of DD channel estimation for OTFS in the presence of fractional Doppler.We first propose a channel estimation algorithm with both low complexity and high accuracy based on the unitary approximate message passing(UAMP),which exploits the structured sparsity of the effective DD domain channel using hidden Markov model(HMM).The empirical state evolution(SE)analysis is then leveraged to predict the performance of our proposed algorithm.To refine the hyperparameters in the proposed algorithm,we derive the update criterion for the hyperparameters through the expectation-maximization(EM)algorithm.Finally,Our simulation results demonstrate that our proposed algorithm can achieve a significant gain over various baseline schemes.

OTFS-Based Efficient Handover Authentication Scheme with Privacy-Preserving for High Mobility Scenarios

Handover authentication in high mobility scenarios is characterized by frequent and shortterm parallel execution.Moreover,the penetration loss and Doppler frequency shift caused by high speed also lead to the deterioration of network link quality.Therefore,high mobility scenarios require handover schemes with less handover overhead.However,some existing schemes that meet this requirement cannot provide strong security guarantees,while some schemes that can provide strong security guarantees have large handover overheads.To solve this dilemma,we propose a privacy-preserving handover authentication scheme that can provide strong security guarantees with less computational cost.Based on Orthogonal Time Frequency Space(OTFS)link and Key Encapsulation Mechanism(KEM),we establish the shared key between protocol entities in the initial authentication phase,thereby reducing the overhead in the handover phase.Our proposed scheme can achieve mutual authentication and key agreement among the user equipment,relay node,and authentication server.We demonstrate that our proposed scheme can achieve user anonymity,unlinkability,perfect forward secrecy,and resistance to various attacks through security analysis including the Tamarin.The performance evaluation results show that our scheme has a small computational cost compared with other schemes and can also provide a strong guarantee of security properties.