簡易檢索 / 詳目顯示

研究生: 李家葦
Lee, Jia-Wei
論文名稱: 正交時頻空間系統之時頻域等化方法設計與效能分析
Design and Performance Analysis of TF-Domain Equalization for Orthogonal Time Frequency Space Systems
指導教授: 陳曉華
Chen, Hsiao-Hwa
學位類別: 碩士
Master
系所名稱: 工學院 - 工程科學系
Department of Engineering Science
論文出版年: 2026
畢業學年度: 114
語文別: 英文
論文頁數: 406
中文關鍵詞: 正交時頻空間 、時頻域等化 、延遲都卜勒域 、零強迫等化 、位元錯誤率 、循環字首 、通道模型
外文關鍵詞: OTFS, Orthogonal Time Frequency Space, TF-domain equalization, delay-Doppler domain, zero-forcing equalization, bit error rate, cyclic prefix, inter-frame interference, 3GPP TDL channel
相關次數: 點閱:85  下載:3 
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
  • 正交時頻空間(Orthogonal Time Frequency Space, OTFS)調變技術將資訊符號配置於延遲都卜勒(delay-Doppler, DD)域,藉由二維轉換在時頻域與延遲都卜勒域之間建立訊號表示,使其適合應用於高速移動與時變多路徑通道環境。然而,在實際 OTFS 系統中,多路徑延遲、都卜勒效應、通道估測誤差,皆會影響接收端訊號偵測與位元錯誤率效能。因此,本論文針對 OTFS 系統之通道估測、跨訊框干擾,以及時頻域(time-frequency, TF)等化方法進行分析與研究。

    本論文首先由 OTFS 系統經 Wigner 轉換後之時頻域輸入輸出關係出發。在第三章的初始推導中,將時頻域輸入輸出關係假設為通道響應與傳送訊號之理想二維循環卷積關係,並據此利用二維快速傅立葉轉換將卷積關係轉換為乘法形式,以建立時頻域等化架構。然而,進一步檢視完整推導後發現,實際時頻域通道響應中仍包含與路徑延遲、都卜勒頻移以及時頻索引相互耦合之通道相關指數相位項,使通道核並不具有理想二維循環卷積所要求的平移不變性。因此,第三章所採用的二維循環卷積輸入輸出模型在一般時變多路徑通道下並不成立,而第四章建立於該模型上的殘留干擾功率、雜訊功率、有效訊雜干擾比及理論位元錯誤率等解析結果亦不具嚴格有效性。

    為解決上述模型不符問題,本論文於第五章重新由連續時間接收訊號及 Wigner 轉換出發,在不忽略任何通道相關指數相位項的情況下,重新推導 OTFS 系統實際的時頻域輸入輸出關係。推導結果顯示,時頻域接收訊號應由一個同時依賴輸入與輸出時頻索引的通道核描述,而非簡化為僅與索引差值相關的二維循環卷積形式。基於此精確輸入輸出模型,本論文進一步提出精準時頻域等化(precise TF-domain equalization)方法,直接依據完整通道響應補償各時頻資源單元間由多路徑延遲與都卜勒效應所造成的干擾。值得注意的是,本研究所提出之精準時頻域等化系統並未配置循環字首(cyclic prefix, CP),而是利用所推導之完整時頻域通道模型與等化機制直接補償多路徑傳播所造成的訊號干擾,因此可避免循環字首所帶來的額外傳輸開銷與頻寬使用效率損失。不同於建立於理想卷積假設上的盲時頻域等化(blind TF-domain equalization)方法,精準時頻域等化保留完整的路徑相依相位資訊,並在無循環字首的系統架構下進行通道補償,以降低因通道模型近似及多路徑效應所造成的等化失配與殘留干擾。

    模擬結果進一步比較精準時頻域等化、盲時頻域等化、傳統延遲都卜勒域零強迫(zero-forcing, ZF)等化,以及未等化 OTFS 系統在多種 3GPP TDL 通道模型下的位元錯誤率效能。結果顯示,採用完整時頻域輸入輸出模型之精準時頻域等化能夠更有效補償多路徑與都卜勒效應所造成的干擾,並在所考慮的通道與系統參數下獲得較佳的位元錯誤率效能。本論文亦進一步分析不同 OTFS 訊框大小、調變階數與子載波間距對精準與盲時頻域等化效能的影響,以評估不同系統參數設定下的傳輸可靠度。

    除了時頻域等化之外,本論文亦探討數個與實際 OTFS 傳輸相關的重要議題。首先,針對延遲都卜勒域中的導頻與保護區配置進行通道估測分析,以取得接收端等化所需之通道狀態資訊。最後,為同時評估傳輸可靠度與頻寬資源使用情形,本論文引入聯合頻寬與能量效率(joint bandwidth--energy efficiency, JBEE)指標,將有效資料速率、頻寬使用效率與位元錯誤率共同納入考量,並比較具有循環字首之盲時頻域等化與無循環字首之精準時頻域等化在不同系統參數下的整體效能。

    綜合而言,本論文首先指出將 OTFS 時頻域輸入輸出關係簡化為理想二維循環卷積所造成的模型限制,並進一步重新建立完整且精確的時頻域訊號模型。在此基礎上所提出之精準時頻域等化方法,可在不依賴理想循環卷積假設的情況下,直接處理多路徑通道所造成的干擾。模擬結果顯示,此方法在不同 OTFS 系統參數與 3GPP TDL 通道模型下具有良好的位元錯誤率與整體傳輸效率表現,顯示精準時頻域等化可作為高速移動 OTFS 通訊系統接收機設計的一種可行方法。

    Orthogonal Time Frequency Space (OTFS) modulation maps information symbols onto the delay-Doppler (DD) domain and establishes signal representations between the time-frequency (TF) and DD domains through two-dimensional transforms, making it well suited for high-mobility communications over time-varying multipath channels. However, in practical OTFS systems, multipath delay, Doppler effects, channel estimation errors, and bit error rate (BER) performance. Therefore, this thesis investigates channel estimation, and TF-domain equalization for OTFS systems.

    This thesis first investigates the TF-domain input-output relationship of an OTFS system after the Wigner transform. In the initial derivation presented in Chapter 3, the TF-domain input-output relationship was assumed to form an ideal two-dimensional circular convolution between the channel response and the transmitted signal. Based on this assumption, a two-dimensional fast Fourier transform (2D FFT) was employed to convert the convolution relationship into a multiplicative form, thereby establishing a TF-domain equalization framework. However, a further examination of the complete derivation reveals that the actual TF-domain channel response contains channel-dependent exponential phase terms that are jointly coupled with the path delays, Doppler shifts. Consequently, the resulting channel response and transmit signal in TF-domain does not valid for an ideal two-dimensional circular convolution. Therefore, the two-dimensional circular-convolution-based input-output model adopted in Chapter 3 does not hold in general multipath channels. As a consequence, the analytical results derived in Chapter 4 based on this model, including the residual interference power, noise power, effective signal-to-interference-plus-noise ratio (SINR), and theoretical BER, are not strictly valid.

    To address this model mismatch, Chapter 5 re-derives the actual TF-domain input-output relationship of the OTFS system starting from the continuous-time received signal and the Wigner transform, while retaining all channel-dependent exponential phase terms throughout the derivation. The resulting expression shows that the received TF-domain signal must be characterized by a channel that depends jointly on both the input and output TF indices, rather than by a two-dimensional circular convolution that depends only on their index differences. Based on this precise input-output model, a precise TF-domain equalization scheme is further developed to compensate for the interference caused by multipath delay and Doppler effects among TF resource elements using the complete channel response. It is worth noting that the proposed precise TF-domain equalization system operates without a cyclic prefix. Instead, the derived complete TF-domain channel model and the corresponding equalization mechanism are employed to directly compensate for interference caused by multipath propagation, thereby avoiding the additional transmission overhead and loss of bandwidth utilization efficiency associated with CP insertion. In contrast to the blind TF-domain equalization scheme established under the ideal convolution assumption, the precise TF-domain equalization scheme retains the complete path-dependent phase information and performs channel compensation in a CP-free system, thereby reducing equalization mismatch and residual interference caused by channel-model approximation and multipath propagation.

    Simulation results further compare the BER performance of the precise TF-domain equalization scheme, the blind TF-domain equalization scheme, conventional DD-domain zero-forcing (ZF) equalization, and the OTFS system without equalization under various 3GPP tapped delay line (TDL) channel models. The results demonstrate that the precise TF-domain equalization scheme based on the complete TF-domain input-output model can more effectively compensate for interference caused by multipath and Doppler effects and achieves improved BER performance under the considered channel conditions and system configurations. The effects of different OTFS frame sizes, modulation orders, and subcarrier spacings on the performance of the precise and blind TF-domain equalization schemes are also investigated to evaluate transmission reliability under different system parameter settings.

    In addition to TF-domain equalization, this thesis investigates several important issues related to practical OTFS transmission. First, channel estimation based on pilot and guard-region allocation in the DD domain is analyzed to obtain the channel state information required for receiver equalization. Finally, to jointly evaluate transmission reliability and bandwidth resource utilization, a joint bandwidth--energy efficiency (JBEE) metric is introduced by jointly considering the effective data rate, bandwidth utilization efficiency, and BER. The overall performance of the CP-based blind TF-domain equalization scheme and the CP-free precise TF-domain equalization scheme is then compared under different system parameter configurations.

    In summary, this thesis first identifies the model limitation associated with simplifying the OTFS TF-domain input-output relationship as an ideal two-dimensional circular convolution and subsequently establishes a complete and precise TF-domain signal model. Based on this model, the proposed precise TF-domain equalization scheme can directly mitigate interference caused by multipath propagation without relying on the ideal circular-convolution assumption. Simulation results demonstrate that the proposed scheme provides favorable BER and overall transmission-efficiency performance under different OTFS system configurations and 3GPP TDL channel models. These results indicate that precise TF-domain equalization provides a feasible receiver design approach for OTFS communication systems operating in high-mobility environments.

    摘要 vii Abstract ix Acknowledgements xiii Table of Contents xv List of Tables xix List of Figures xxv List of Abbreviations xliii List of Symbols xlvii Dedication lv 1 Introduction 1 1.1 Background and Motivations 1 1.2 A Brief Survey on Related Works 4 2 OTFS System 7 2.1 Overview of OTFS 7 2.1.1 Channel Fading and Wireless Channel Model In OTFS 7 2.1.2 Definition of the Delay-Doppler Grid 18 2.1.3 OTFS Signal Generation and Modulation Process In Transmitter 20 2.1.4 Input-Output Relation In Time-Frequency Domain And Delay-Doppler Domain 24 2.2 PAPR Problem In OTFS 38 2.2.1 Literature Review of PAPR Reduction Techniques in OTFS 40 2.3 CFO Problem In OTFS 44 2.4 A Novel Interference Cancellation Approach 45 2.4.1 Ambiguity Function under Doppler-Induced and Multipath-Induced Interference 46 2.4.2 Interference Cancellation Signal Processing in OTFS 47 2.4.3 Issues and Challenges of the Interference Cancellation Approach 49 2.5 The Cross-Ambiguity Function in Multipath LTV channel 50 2.6 OTFS System Channel Estimation 54 2.6.1 OTFS Channel Estimation Simulation 59 2.6.2 OTFS Channel Estimation Summary 81 3 System Model 83 3.1 Transmitter Model of the OTFS system 83 3.2 Receiver Model of the Compensated Ambiguity Function in OTFS Systems 86 3.3 Error-Rate Analysis of the Proposed 2D-FFT TF-Domain Equalization 102 3.3.1 SINR derivation after (imperfect) ZF equalization 103 3.3.2 BER of Square Q-QAM by Substituting the Derived SNR 106 4 Performance Analysis and Advantage of the Proposed TF-Domain Equalization Scheme for OTFS Systems 109 4.1 Residual Interference and Noise Enhancement Comparison With DD-Domain ZF Equalization 109 4.1.1 Residual-Interference Analysis of DD- and TF-Domain Equalization 110 4.1.2 Comparison of Noise Enhancement for DD- and TF-Domain Equalization 114 4.1.3 Why the Proposed TF-Domain Equalizer Achieves Better Performance 116 4.2 Performance Comparison Under Different System Parameters 119 4.2.1 Theoretical Performance Comparison Under Different OTFS Frame Sizes 120 4.2.2 Theoretical BER Performance Comparison Under Different Subcarrier Spacings 124 5 A more precise analysis on TF domain equalization in OTFS systems 139 5.1 Derivation of the Time-Frequency Input-Output Relation 139 5.2 Statistical Characteristics of the Residual Phase-Induced Interference 168 5.2.1 Second Moment of the Residual Phase-Induced Interference 171 5.3 Statistical Characteristics of the Equalized DD-Domain Noise 179 5.3.1 Mean of the Equalized DD-Domain Noise 189 5.3.2 Second Moment of the Equalized DD-Domain Noise 189 5.4 Second Moment of the Useful Signal Component and Effective SINR 190 6 Conclusions and Future Works 219 6.1 Conclusions 219 6.2 Future Works 225 References 231 A BER Calculation with Q-QAM over AWGN 255 A.1 Uncoded Error-Rate Analysis of Square Q-QAM over AWGN 255 A.1.1 Derivation of dmin from constellation geometry and definition of Eb,avg 256 A.1.2 Minimum distance and PAM decomposition 258 A.1.3 SER of square Q-QAM in terms of Eb,avg/N0 259 A.2 Modulation Constellations Used in Chapter 5 260 B Relation between the OTFS System Received Signal and the Delay-Doppler Domain Impulse Response 263 B.1 OTFS System Received Signal 263 C Cyclic Prefix Added on OTFS System 267 C.1 CP-OTFS 267 C.1.1 TX On CP-OTFS 267 C.1.2 RX On CP-OTFS 271 C.2 Simulation and Comparison of Cyclic-Prefix-Aided OTFS Systems 273 C.2.1 simulation parameter setting 277 C.2.2 Simulation result 279 C.2.3 Bandwidth Utilization Efficiency 287 C.3 Summary of Appendix C 290 D Useful Fraction Decompositions and Algebraic Manipulations and proof residual-interference power 293 D.1 A useful fraction decomposition 293 D.2 Average Residual-Interference Power Due to Imperfect Channel Estimation 294 D.3 Pointwise Simplification of the Integer-Valued Phase Term within a Two-Dimensional Summation 299 E Error-Rate Analysis of OTFS Systems Without Equalizer 305 E.1 SFFT-Based Derivation of the DD-Domain Input-Output Relation 305 E.2 Relationship Between the Doppler-Domain Grid Index and The Corresponding Physical Doppler Frequency 311 F Error-Rate Analysis of ZF Equalization in DD-Domain 315 F.1 Vector I/O Model and ZF Equalization Under Imperfect CSI 315 G Execution Time Comparison of CPU- and GPU-Based Matrix Operations in MATLAB 323 G.1 Execution Time Comparison of Matrix Operations Using CPU and GPU in MATLAB 323 G.2 Parallel Computing Using Multi-Core CPU in MATLAB 328 H Bandwidth-Power Utilization Efficiency Analysis of OTFS Systems under Different Frame Sizes 331 H.1 Bandwidth Utilization Efficiency under Different OTFS Frame Sizes 331 H.2 Objective Function for Fair Comparison of Different OTFS Equalization Schemes 333 I Inter-Frame Interference in OTFS system 341 I.1 Inter-Frame Interference After the Heisenberg Transform 341

    [1] R. Hadani, S. Rakib, M. Tsatsanis, A. Monk, A. J. Goldsmith, and A. F. Molisch, “Orthogonal Time Frequency Space (OTFS) Modulation,”in 2017 IEEE Wireless Communications and Networking Conference (WCNC), San Francisco, CA, USA, Mar. 2017.
    [2] A. F. Molisch, ”Delay-Doppler Communications: Principles and Applications,” in IEEE Communications Magazine, vol. 61, no. 3, pp. 10-10, March 2023, doi: 10.1109/MCOM.2023.10080900.
    [3] Zegrar, Salah Eddine, and Hüseyin Arslan. ”A novel cyclic prefix configuration for enhanced reliability and spectral efficiency in OTFS systems.” IEEE Wireless Communications Letters 12.5 (2023): 888-892.
    [4] Raviteja, Patchava, et al. ”Interference cancellation and iterative detection for orthogonal time frequency space modulation.” IEEE transactions on wireless communications 17.10 (2018): 6501-6515.
    [5] Gentleman, W. M., Sande, G. (1966). Fast Fourier transforms—for fun and profit. In Proceedings of the AFIPS Fall Joint Computer Conference (Vol. 29, pp. 563–578). Spartan Books / ACM.
    [6] Hadani, Ronny, et al. ”Orthogonal time frequency space modulation.” 2017 IEEE wireless communications and networking conference (WCNC). IEEE, 2017.
    [7] Rodriguez, Domingo, et al. ”Intelligent Waveform Design and Channel Modeling Using Group Theoretic Weyl-Heisenberg Frames.” 2024 IEEE Latin-American Conference on Communications (LATINCOM). IEEE, 2024.
    [8] D. Zhang and C. Jiang, ”Fast Calculation of Cross Ambiguity Function for Passive Radar Based on SMGO,” 2021 China Automation Congress (CAC), Beijing, China, 2021, pp. 6684-6689, doi: 10.1109/CAC53003.2021.9727353. keywords: Passive radar;Time-frequency analysis;Correlation;Delay effects;Simulation;Signal processing algorithms;Optimization methods;Cross Ambiguity Function;Passive Radar;Set Membership Global Optimization,
    [9] Liu, Haoyan, et al. ”On the characterizations of OTFS modulation over multipath rapid fading channel.” IEEE transactions on wireless communications 22.3 (2022): 2008-2021.
    [10] Surabhi, G. D., Rose Mary Augustine, and Ananthanarayanan Chockalingam. ”Peak-to-average power ratio of OTFS modulation.” IEEE Communications Letters 23.6 (2019): 999-1002.
    [11] Wang, Yu, et al. ”An Improved OTFS Transmission Frame Structure Design for PAPR Reduction.” 2024 20th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob). IEEE, 2024.
    [12] Wang, Yu, et al. ”An Improved OTFS Transmission Frame Structure Design for PAPR Reduction.” 2024 20th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob). IEEE, 2024.
    [13] Tek, Yusuf Islam, and Ertugrul Basar. ”PAPR reduction precoding for orthogonal time frequency space modulation.” 2023 46th International Conference on Telecommunications and Signal Processing (TSP). IEEE, 2023.
    [14] Gao, Shuang, and Jianping Zheng. ”Peak-to-average power ratio reduction in pilot-embedded OTFS modulation through iterative clipping and filtering.” IEEE Communications Letters 24.9 (2020): 2055-2059.
    [15] Naveen, Cheemala, and V Sudha. ”Peak-to-average power ratio reduction in OTFS modulation using companding technique.” 2020 5th international conference on devices, circuits and systems (ICDCS). IEEE, 2020.
    [16] Sümer, Ahmet Sacid, et al. ”Exploiting OTFS frame structure for PAPR reduction.” 2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall). IEEE, 2022.
    [17] Liu, Xuan, et al. ”Low complexity design of peak-to-average power ration (papr) reduction scheme for otfs modulation.” 2022 10th International Conference on Information Systems and Computing Technology (ISCTech). IEEE, 2022.
    [18] Sreekumar, Shyama, and Manoj Kumar. ”A Non-Data-Aided Carrier Frequency Offset Estimation Technique for High Mobility OTFS Systems.” 2024 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT). IEEE, 2024.
    [19] Kumar, Manoj, and Shyama Sreeekumar. ”Joint CFO and Channel Estimation for OTFS Based High Mobility Wireless Communication Systems.” 2024 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT). IEEE, 2024.
    [20] Chen, Wenxin, et al. ”Blind CFO estimator for OTFS systems in vehicle-to-infrastructure communications.” IEEE Wireless Communications Letters 12.12 (2023): 2048-2052.
    [21] Wei, Liangdong, et al. ”A Novel Blind CFO Estimator for RCP-OTFS Systems.” 2024 IEEE 24th International Conference on Communication Technology (ICCT). IEEE, 2024.
    [22] Weiss, Lora G. ”Wavelets and wideband correlation processing.” IEEE signal processing magazine 11.1 (1994): 13-32.
    [23] D. Vandenberg, “Mathematical Survey and Application of the Cross-Ambiguity Function”, M.S. thesis, Indiana Univ., 2012.
    [24] Benedetto, John J., Ioannis Konstantinidis, and Muralidhar Rangaswamy. ”The role of the ambiguity function in waveform design and phase coded waveforms.” 2008,
    [25] Jin, Chenxi, et al. ”A simple two-stage equalizer for OTFS with rectangular windows.” IEEE communications letters 25.4 (2020): 1158-1162.
    [26] Jing, Lianyou, et al. ”Two dimensional adaptive multichannel decision feedback equalization for OTFS system.” IEEE communications letters 25.3 (2020): 840-844.
    [27] Raviteja, Patchava, Khoa T. Phan, and Yi Hong. ”Embedded pilot-aided channel estimation for OTFS in delay–Doppler channels.” IEEE transactions on vehicular technology 68.5 (2019): 4906-4917.
    [28] Shen, Wenqian, et al. ”Channel estimation for orthogonal time frequency space (OTFS) massive MIMO.” IEEE Transactions on Signal Processing 67.16 (2019): 4204-4217.
    [29] Kazemzadeh, Bentolhoda, et al. ”Enhancing Channel Estimation in High Mobility OTFS Systems: A Novel Pilot-Based Method Exploiting Doppler Axis Diversity in a TransPod Transportation System.” GLOBECOM 2023-2023 IEEE Global Communications Conference. IEEE, 2023.
    [30] Y. Shen, Y. Yang, Y. Liu and Y. Wang, ”Pilot-Aided Channel Estimation Based on MDCFT for OTFS in High Dynamic Environment,” in IEEE Wireless Communications Letters, vol. 14, no. 8, pp. 2311-2315, Aug. 2025, doi: 10.1109/LWC.2025.3568254.
    [31] X. Li, C. Shan, H. Zhao, W. Yuan and R. Zhang, ”A Modified Structured SAMP Channel Estimation Method for FDD MIMO-OTFS Systems,” in IEEE Wireless Communications Letters, vol. 13, no. 11, pp. 3005-3009, Nov. 2024, doi: 10.1109/LWC.2024.3435077
    [32] A. Pfadler, T. Szollmann, P. Jung and S. Stanczak, ”Leakage Suppression in Pulse-Shaped OTFS Delay-Doppler-Pilot Channel Estimation,” in IEEE Wireless Communications Letters, vol. 11, no. 6, pp. 1181-1185, June 2022, doi: 10.1109/LWC.2022.3160657
    [33] L. Jing, Q. Wang, C. He and X. Zhang, ”A Learned Denoising-Based Sparse Adaptive Channel Estimation for OTFS Underwater Acoustic Communications,” in IEEE Wireless Communications Letters, vol. 13, no. 4, pp. 969-973, April 2024, doi: 10.1109/LWC.2024.3354280.
    [34] Q. Guo, H. Jiang, J. Xiang and Y. Zhong, ”A CS-BEM OTFS Channel Estimation Approach for Sparse Continuous Doppler-Spread Channels,” in IEEE Wireless Communications Letters, vol. 13, no. 11, pp. 2985-2989, Nov. 2024, doi: 10.1109/LWC.2024.3432090.
    [35] X. Huang, A. Farhang and R. -R. Chen, ”Channel Estimation and Turbo Equalization for Coded OTFS and OFDM: A Comparison,” in IEEE Wireless Communications Letters, vol. 12, no. 9, pp. 1613-1617, Sept. 2023, doi: 10.1109/LWC.2023.3284778.
    [36] B. Li, W. Yuan, F. Liu, N. Wu and S. Jin, ”OTFS-Based ISAC: How Delay Doppler Channel Estimation Assists Environment Sensing?,” in IEEE Wireless Communications Letters, vol. 13, no. 12, pp. 3563-3567, Dec. 2024, doi: 10.1109/LWC.2024.3478744.
    [37] S. G. Neelam and P. R. Sahu, ”Joint Estimation and Compensation of CFO, IQ Imbalance and Channel Parameters for Zero Padded OTSM Systems,” in IEEE Wireless Communications Letters, vol. 12, no. 11, pp. 1871-1875, Nov. 2023, doi: 10.1109/LWC.2023.3297133.
    [38] W. Yuan, S. Li, Z. Wei, J. Yuan and D. W. K. Ng, ”Data-Aided Channel Estimation for OTFS Systems With a Superimposed Pilot and Data Transmission Scheme,” in IEEE Wireless Communications Letters, vol. 10, no. 9, pp. 1954-1958, Sept. 2021, doi: 10.1109/LWC.2021.3088836.
    [39] I. A. Khan and S. K. Mohammed, ”A Low-Complexity OTFS Channel Estimation Method for Fractional Delay-Doppler Scenarios,” in IEEE Wireless Communications Letters, vol. 12, no. 9, pp. 1484-1488, Sept. 2023, doi: 10.1109/LWC.2023.3274936
    [40] M. Ma, J. Dai and X. -Q. Jiang, ”Fast Burst-Sparsity Learning Approach for Massive MIMO-OTFS Channel Estimation,” in IEEE Wireless Communications Letters, vol. 14, no. 5, pp. 1546-1550, May 2025, doi: 10.1109/LWC.2025.3548685.
    [41] Y. Zhou, P. Fan, Q. Wang and X. He, ”Off-Grid OTFS Channel Estimation Based on Equally Distributed Information Quantity Grid Evolution,” in IEEE Wireless Communications Letters, vol. 13, no. 11, pp. 3182-3186, Nov. 2024, doi: 10.1109/LWC.2024.3457845.
    [42] S. Kumari, M. K. Dikkala, S. Mukhopadhyay and H. B. Mishra, ”Two Choice Hard Thresholding Pursuit (TCHTP) for Delay-Doppler Channel Estimation in OTFS,” in IEEE Wireless Communications Letters, vol. 12, no. 6, pp. 1032-1036, June 2023, doi: 10.1109/LWC.2023.3257998.
    [43] S. R. Mattu and A. Chockalingam, ”Learning in Time-Frequency Domain for Fractional Delay-Doppler Channel Estimation in OTFS,” in IEEE Wireless Communications Letters, vol. 13, no. 5, pp. 1245-1249, May 2024, doi: 10.1109/LWC.2024.3367112.
    [44] J. Sun et al., ”Pilot-Aided Joint Time Synchronization and Channel Estimation for OTFS,” in IEEE Wireless Communications Letters, vol. 14, no. 1, pp. 143-147, Jan. 2025, doi: 10.1109/LWC.2024.3490839.
    [45] Y. Yang, P. Fan, X. He and X. Li, ”An OTFS Channel Estimation Scheme Based on Iterative Path Peak Search and Inter-Path Interference Mitigation,” in IEEE Wireless Communications Letters, vol. 13, no. 7, pp. 1828-1832, July 2024, doi: 10.1109/LWC.2024.3391931.
    [46] G. Lei, Y. Qiao, T. Liang, W. Yuan and T. Zhang, ”Low-Complexity Channel Estimation in OTFS Systems With Fractional Effects,” in IEEE Wireless Communications Letters, doi: 10.1109/LWC.2025.3585553.
    [47] M. K. AbuFoul, D. A. Tubail, M. Zourob and S. Ikki, ”Parameter-Based Estimation of Block Time-Varying Channels in OTFS Modulation Systems,” in IEEE Wireless Communications Letters, vol. 14, no. 4, pp. 1189-1193, April 2025, doi: 10.1109/LWC.2025.3538822.
    [48] Y. Liang, P. Fan, Q. Wang and X. He, ”Two-Dimensional Delay-Doppler Pilots and Channel Estimation for Multi-Antenna OTFS in Doubly Dispersive Channels,” in IEEE Transactions on Wireless Communications, vol. 23, no. 7, pp. 7612-7623, July 2024, doi: 10.1109/TWC.2023.3342877.
    [49] X. He, W. Yuan and P. Fan, ”On the Pilot-Aided Channel Estimation for Windowed OTFS With Data Interference in Rapidly Time-Varying Channels,” in IEEE Transactions on Wireless Communications, vol. 23, no. 11, pp. 16359-16374, Nov. 2024, doi: 10.1109/TWC.2024.3440586.
    [50] P. Priya, Y. Hong and E. Viterbo, ”OTFS Channel Estimation and Detection for Channels With Very Large Delay Spread,” in IEEE Transactions on Wireless Communications, vol. 23, no. 9, pp. 11920-11930, Sept. 2024, doi: 10.1109/TWC.2024.3386160.
    [51] H. B. Mishra, P. Singh, A. K. Prasad and R. Budhiraja, ”OTFS Channel Estimation and Data Detection Designs With Superimposed Pilots,” in IEEE Transactions on Wireless Communications, vol. 21, no. 4, pp. 2258-2274, April 2022, doi: 10.1109/TWC.2021.3110659.
    [52] D. Shi et al., ”Deterministic Pilot Design and Channel Estimation for Downlink Massive MIMO-OTFS Systems in Presence of the Fractional Doppler,” in IEEE Transactions on Wireless Communications, vol. 20, no. 11, pp. 7151-7165, Nov. 2021, doi: 10.1109/TWC.2021.3081164.
    [53] X. Zhou et al., ”Active Terminal Identification, Channel Estimation, and Signal Detection for Grant-Free NOMA-OTFS in LEO Satellite Internet-of-Things,” in IEEE Transactions on Wireless Communications, vol. 22, no. 4, pp. 2847-2866, April 2023, doi: 10.1109/TWC.2022.3214862.
    [54] Q. Wang, Y. Liang, Z. Zhang and P. Fan, ”2D Off-Grid Decomposition and SBL Combination for OTFS Channel Estimation,” in IEEE Transactions on Wireless Communications, vol. 22, no. 5, pp. 3084-3098, May 2023, doi: 10.1109/TWC.2022.3215991.
    [55] M. Tang, H. Wang, Z. Yuan and J. Yuan, ”A Novel Off-Grid Channel Estimation With Fast BCS Using LSM Prior for OTFS Modulation,” in IEEE Transactions on Wireless Communications, vol. 23, no. 9, pp. 12157-12171, Sept. 2024, doi: 10.1109/TWC.2024.3388449.
    [56] Z. Wei, W. Yuan, S. Li, J. Yuan and D. W. K. Ng, ”Off-Grid Channel Estimation With Sparse Bayesian Learning for OTFS Systems,” in IEEE Transactions on Wireless Communications, vol. 21, no. 9, pp. 7407-7426, Sept. 2022, doi: 10.1109/TWC.2022.3158616.
    [57] H. -T. Sheng and W. -R. Wu, ”Time-Frequency Domain Channel Estimation for OTFS Systems,” in IEEE Transactions on Wireless Communications, vol. 23, no. 2, pp. 937-948, Feb. 2024, doi: 10.1109/TWC.2023.3283578.
    [58] K. R. R. Ranasinghe, H. Seok Rou, G. Thadeu Freitas de Abreu, T. Takahashi and K. Ito, ”Joint Channel, Data, and Radar Parameter Estimation for AFDM Systems in Doubly-Dispersive Channels,” in IEEE Transactions on Wireless Communications, vol. 24, no. 2, pp. 1602-1619, Feb. 2025, doi: 10.1109/TWC.2024.3510935.
    [59] F. Liu, Z. Yuan, Q. Guo, Z. Wang and P. Sun, ”Message Passing-Based Structured Sparse Signal Recovery for Estimation of OTFS Channels With Fractional Doppler Shifts,” in IEEE Transactions on Wireless Communications, vol. 20, no. 12, pp. 7773-7785, Dec. 2021, doi: 10.1109/TWC.2021.3087501.
    [60] Y. Shan, F. Wang, Y. Hao, J. Yuan, J. Hua and Y. Xin, ”Off-Grid Channel Estimation Using Grid Evolution for OTFS Systems,” in IEEE Transactions on Wireless Communications, vol. 23, no. 8, pp. 9549-9565, Aug. 2024, doi: 10.1109/TWC.2024.3363696.
    [61] Q. Tao, T. Xie, X. Hu, S. Zhang and D. Ding, ”Channel Estimation and Detection for Intelligent Reflecting Surface-Assisted Orthogonal Time Frequency Space Systems,” in IEEE Transactions on Wireless Communications, vol. 23, no. 8, pp. 8419-8431, Aug. 2024, doi: 10.1109/TWC.2024.3349707.
    [62] A. Pfadler, T. Szollmann, P. Jung and S. Stańczak, ”Estimation of Doubly-Dispersive Channels in Linearly Precoded Multicarrier Systems Using Smoothness Regularization,” in IEEE Transactions on Wireless Communications, vol. 23, no. 2, pp. 1293-1307, Feb. 2024, doi: 10.1109/TWC.2023.3287948.
    [63] H. Yin, X. Wei, Y. Tang and K. Yang, ”Diagonally Reconstructed Channel Estimation for MIMO-AFDM With Inter-Doppler Interference in Doubly Selective Channels,” in IEEE Transactions on Wireless Communications, vol. 23, no. 10, pp. 14066-14079, Oct. 2024, doi: 10.1109/TWC.2024.3408458.
    [64] Y. Liang, W. Yuan, Y. Wu and P. Fan, ”Matrix Pencil-Based Channel Estimation Algorithm for OTFS in Fractional Doppler Channels,” in IEEE Communications Letters, vol. 28, no. 9, pp. 2191-2195, Sept. 2024, doi: 10.1109/LCOMM.2024.3422285.
    [65] S. P. Muppaneni, S. R. Mattu and A. Chockalingam, ”Channel and Radar Parameter Estimation With Fractional Delay-Doppler Using OTFS,” in IEEE Communications Letters, vol. 27, no. 5, pp. 1392-1396, May 2023, doi: 10.1109/LCOMM.2023.3251578.
    [66] J. Hu, Z. Bai, H. Xu, H. Liu, Y. Wang and K. Kwak, ”Cross-Domain Channel Estimation Based Serial Interference Cancellation in NOMA-OTFS System,” in IEEE Communications Letters, vol. 28, no. 7, pp. 1668-1672, July 2024, doi: 10.1109/LCOMM.2024.3406432.
    [67] M. Zhou, F. Chen, H. Yu and J. Lu, ”Iterative Channel Estimation for OTFS Systems Based on Low-PAPR Hybrid Superimposed Pilots,” in IEEE Communications Letters, vol. 28, no. 8, pp. 1939-1943, Aug. 2024, doi: 10.1109/LCOMM.2024.3410268.
    [68] Y. Yue, J. Shi, Z. Li, J. Hu and Z. Tie, ”Model-Driven Deep Learning Assisted Detector for OTFS With Channel Estimation Error,” in IEEE Communications Letters, vol. 28, no. 4, pp. 842-846, April 2024, doi: 10.1109/LCOMM.2024.3362970.
    [69] X. He, P. Fan and Q. Wang, ”A Two-Stage Channel Estimation Algorithm for OTFS in Fractional Doppler Channels,” in IEEE Communications Letters, vol. 27, no. 7, pp. 1839-1843, July 2023, doi: 10.1109/LCOMM.2023.3270296.
    [70] X. Wang, C. Zheng, P. Hu, J. Yang and C. G. Kang, ”A Sparsity-Agnostic SL0 Channel Estimation Approach for OTFS Systems,” in IEEE Communications Letters, vol. 29, no. 5, pp. 1097-1101, May 2025, doi: 10.1109/LCOMM.2025.3554379.
    [71] Y. Yan, C. Shan, J. Zhang and H. Zhao, ”Off-Grid Channel Estimation for OTFS-Based mmWave Hybrid Beamforming Systems,” in IEEE Communications Letters, vol. 27, no. 8, pp. 2167-2171, Aug. 2023, doi: 10.1109/LCOMM.2023.3290035.
    [72] Q. Guo, H. Jiang, J. Xiang and Y. Zhong, ”Low Complexity Iterative Channel Estimation and Detection Based on Pilot-Assisted for ZP-OTFS,” in IEEE Communications Letters, vol. 29, no. 4, pp. 724-728, April 2025, doi: 10.1109/LCOMM.2025.3542020.
    [73] H. Wu, H. Chen, Q. Peng, Q. Luo and J. Ou, ”Performance Analysis of BEM-Based Channel Estimation for OTFS With Hardware Impairments,” in IEEE Communications Letters, vol. 29, no. 7, pp. 1719-1723, July 2025, doi: 10.1109/LCOMM.2025.3572905.
    [74] S. G. Neelam and P. R. Sahu, ”Iterative Channel Estimation and Data Detection of OTSM With Superimposed Pilot Scheme and PAPR Analysis,” in IEEE Communications Letters, vol. 27, no. 8, pp. 2147-2151, Aug. 2023, doi: 10.1109/LCOMM.2023.3281575.
    [75] V. Yogesh, S. R. Mattu and A. Chockalingam, ”Low-Complexity Delay-Doppler Channel Estimation in Discrete Zak Transform Based OTFS,” in IEEE Communications Letters, vol. 28, no. 3, pp. 672-676, March 2024, doi: 10.1109/LCOMM.2024.3351685.
    [76] L. Zhao, J. Yang, Y. Liu and W. Guo, ”Block Sparse Bayesian Learning-Based Channel Estimation for MIMO-OTFS Systems,” in IEEE Communications Letters, vol. 26, no. 4, pp. 892-896, April 2022, doi: 10.1109/LCOMM.2022.3144674.
    [77] X. Li, Y. Liang, Z. Zhou and P. Fan, ”Fractional Delay-Doppler Channel Estimation for OTFS Systems Based on Segmentation Technique and Sparse Bayesian Learning,” in IEEE Communications Letters, vol. 29, no. 5, pp. 1067-1071, May 2025, doi: 10.1109/LCOMM.2025.3553831.
    [78] S. Habibi, J. Chen, F. Fang and X. Wang, ”User-Specific Channel Estimation Overhead Optimization and Resource Allocation for Multi-User OTFS Systems,” in IEEE Communications Letters, vol. 28, no. 9, pp. 2126-2130, Sept. 2024, doi: 10.1109/LCOMM.2024.3424666.
    [79] P. Raviteja, K. T. Phan and Y. Hong, ”Embedded Pilot-Aided Channel Estimation for OTFS in Delay–Doppler Channels,” in IEEE Transactions on Vehicular Technology, vol. 68, no. 5, pp. 4906-4917, May 2019, doi: 10.1109/TVT.2019.2906357.
    [80] H. Zhang, X. Huang and J. A. Zhang, ”Low-Overhead OTFS Transmission With Frequency or Time Domain Channel Estimation,” in IEEE Transactions on Vehicular Technology, vol. 73, no. 1, pp. 799-811, Jan. 2024, doi: 10.1109/TVT.2023.3305921.
    [81] O. A. Aghda, M. J. Omidi and H. Saeedi-Sourck, ”Low-PAPR Joint Channel Estimation and Data Detection in ZP-OTFS System,” in IEEE Transactions on Vehicular Technology, vol. 73, no. 12, pp. 19821-19825, Dec. 2024, doi: 10.1109/TVT.2024.3435934.
    [82] C. Qing, Z. Liu, G. Ling, W. Hu and P. Du, ”Channel Estimation in OTFS Systems by Leveraging Differential Modulation,” in IEEE Transactions on Vehicular Technology, vol. 74, no. 5, pp. 6907-6918, May 2025, doi: 10.1109/TVT.2024.3522940.
    [83] A. F. d. Reis, B. S. Chang, Y. Medjahdi, G. Brante and F. Bader, ”LSTM-Based Time-Frequency Domain Channel Estimation for OTFS Modulation,” in IEEE Transactions on Vehicular Technology, vol. 73, no. 10, pp. 15049-15060, Oct. 2024, doi: 10.1109/TVT.2024.3406192.
    [84] H. Wen, W. Yuan, C. Yuen and Y. Li, ”MF-OAMP-Based Joint Channel Estimation and Data Detection for OTFS Systems,” in IEEE Transactions on Vehicular Technology, vol. 73, no. 2, pp. 2948-2953, Feb. 2024, doi: 10.1109/TVT.2023.3319562.
    [85] Y. Zhang, Q. Zhang, C. He, L. Jing, T. Zheng and C. Yuen, ”Sparse Bayesian Learning Approach for OTFS Channel Estimation With Fractional Doppler,” in IEEE Transactions on Vehicular Technology, vol. 73, no. 11, pp. 16846-16860, Nov. 2024, doi: 10.1109/TVT.2024.3420136.
    [86] D. Ying and F. Ye, ”Deep Learning Supported Path Prediction and Channel Estimation for MIMO-OTFS System With High Delay Resolution,” in IEEE Transactions on Vehicular Technology, vol. 74, no. 3, pp. 3584-3597, March 2025, doi: 10.1109/TVT.2024.3493921.
    [87] C. Liu, W. Xiang, H. Long and Y. Jia, ”Spectral-Efficient Reference Signal Design and Iterative Channel Estimation for OTFS Modulation,” in IEEE Transactions on Vehicular Technology, vol. 73, no. 10, pp. 15286-15301, Oct. 2024, doi: 10.1109/TVT.2024.3414468.
    [88] C. Shi, L. Zhao, Y. Cui, Y. Chu, W. Guo and W. Wang, ”Joint Detection and Channel Estimation for MIMO-OTFS Systems,” in IEEE Transactions on Vehicular Technology, vol. 73, no. 8, pp. 11568-11579, Aug. 2024, doi: 10.1109/TVT.2024.3379507.
    [89] M. Zhou, F. Chen, M. Xia, X. Zhang and H. Yu, ”Iterative Channel Estimation for Multi-User OTFS Uplink Systems With Superimposed Full Pilots,” in IEEE Transactions on Vehicular Technology, vol. 74, no. 3, pp. 4485-4497, March 2025, doi: 10.1109/TVT.2024.3497176.
    [90] S. Qi, Q. Wang and Z. Ma, ”Deep Residual Attention Network for OTFS Channel Estimation,” in IEEE Transactions on Vehicular Technology, vol. 74, no. 6, pp. 9834-9839, June 2025, doi: 10.1109/TVT.2025.3534796.
    [91] X. Yang, H. Li, Q. Guo, J. A. Zhang, X. Huang and Z. Cheng, ”Sensing Aided Uplink Transmission in OTFS ISAC With Joint Parameter Association, Channel Estimation and Signal Detection,” in IEEE Transactions on Vehicular Technology, vol. 73, no. 6, pp. 9109-9114, June 2024, doi: 10.1109/TVT.2024.3355981.
    [92] X. Zhang, C. Liu, W. Yuan, J. A. Zhang and D. W. K. Ng, ”Sparse Prior-Guided Deep Learning for OTFS Channel Estimation,” in IEEE Transactions on Vehicular Technology, vol. 73, no. 12, pp. 19913-19918, Dec. 2024, doi: 10.1109/TVT.2024.3450012.
    [93] K. Meng et al., ”Joint Sparsity Pattern Learning Based Channel Estimation for Massive MIMO-OTFS Systems,” in IEEE Transactions on Vehicular Technology, vol. 73, no. 8, pp. 12189-12194, Aug. 2024, doi: 10.1109/TVT.2024.3375027.
    [94] Z. Gui, Y. Li, C. Zhou, Q. Xiong and X. Xia, ”3D-ESP: An Efficient Subspace Pursuit Algorithm for MIMO-OTFS Channel Estimation,” in IEEE Transactions on Vehicular Technology, vol. 73, no. 11, pp. 17714-17719, Nov. 2024, doi: 10.1109/TVT.2024.3421934.
    [95] X. Li, P. Fan, Q. Wang and Z. Liu, ”Grid Evolution for Doubly Fractional Channel Estimation in OTFS Systems,” in IEEE Transactions on Vehicular Technology, vol. 74, no. 2, pp. 3486-3490, Feb. 2025, doi: 10.1109/TVT.2024.3472695.
    [96] L. Guo, P. Gu, J. Zou, G. Liu and F. Shu, ”DNN-Based Fractional Doppler Channel Estimation for OTFS Modulation,” in IEEE Transactions on Vehicular Technology, vol. 72, no. 11, pp. 15062-15067, Nov. 2023, doi: 10.1109/TVT.2023.3280901.
    [97] J. Pan, ”Cramer-Rao Low Bound of Channel Estimation for Orthogonal Time Frequency Space Modulation System,” in IEEE Transactions on Vehicular Technology, vol. 70, no. 10, pp. 9646-9658, Oct. 2021, doi: 10.1109/TVT.2021.3107917.
    [98] F. Huang, Q. Guo, Y. Zhang and Y. V. Zakharov, ”Message Passing-Based Joint Channel Estimation and Signal Detection for OTFS With Superimposed Pilots,” in IEEE Transactions on Vehicular Technology, vol. 73, no. 8, pp. 11531-11542, Aug. 2024, doi: 10.1109/TVT.2024.3378833.
    [99] S. Srivastava, R. K. Singh, A. K. Jagannatham and L. Hanzo, ”Bayesian Learning Aided Sparse Channel Estimation for Orthogonal Time Frequency Space Modulated Systems,” in IEEE Transactions on Vehicular Technology, vol. 70, no. 8, pp. 8343-8348, Aug. 2021, doi: 10.1109/TVT.2021.3096432.
    [100] Q. Wang, X. Chen, Q. Tao, L. P. Qian, P. -Y. Kam and Y. Wu, ”Model-Driven Channel Estimation Network for Orthogonal Time-Frequency Space Systems,” in IEEE Transactions on Vehicular Technology, vol. 74, no. 7, pp. 11552-11556, July 2025, doi: 10.1109/TVT.2025.3548639.
    [101] A. Thomas, K. Deka, P. Raviteja and S. Sharma, ”Convolutional Sparse Coding Based Channel Estimation for OTFS-SCMA in Uplink,” in IEEE Transactions on Communications, vol. 70, no. 8, pp. 5241-5257, Aug. 2022, doi: 10.1109/TCOMM.2022.3182402.
    [102] H. Qu, G. Liu, L. Zhang, M. A. Imran and S. Wen, ”Low-Dimensional Subspace Estimation of Continuous-Doppler-Spread Channel in OTFS Systems,” in IEEE Transactions on Communications, vol. 69, no. 7, pp. 4717-4731, July 2021, doi: 10.1109/TCOMM.2021.3072744.
    [103] A. Mehrotra, S. Srivastava, N. Shanmughanadha Reddy, A. Jagannatham and L. Hanzo, ”Sparse Channel Estimation for MIMO OTFS/OTSM Systems Using Finite-Resolution ADCs,” in IEEE Transactions on Communications, vol. 73, no. 6, pp. 3971-3987, June 2025, doi: 10.1109/TCOMM.2024.3502682.
    [104] M. Li, S. Zhang, Y. Ge, F. Gao and P. Fan, ”Joint Channel Estimation and Data Detection for Hybrid RIS Aided Millimeter Wave OTFS Systems,” in IEEE Transactions on Communications, vol. 70, no. 10, pp. 6832-6848, Oct. 2022, doi: 10.1109/TCOMM.2022.3199019.
    [105] X. Wang, W. Shen, C. Xing, J. An and L. Hanzo, ”Joint Bayesian Channel Estimation and Data Detection for OTFS Systems in LEO Satellite Communications,” in IEEE Transactions on Communications, vol. 70, no. 7, pp. 4386-4399, July 2022, doi: 10.1109/TCOMM.2022.3179389.
    [106] H. -G. Lee, J. Kim, J. Zhao and J. Joung, ”Single-Tone-Based Channel Estimation Method for OTFS Systems,” in IEEE Transactions on Communications, vol. 73, no. 3, pp. 1638-1651, March 2025, doi: 10.1109/TCOMM.2024.3455240.
    [107] M. Jafri, S. Srivastava and A. K. Jagannatham, ”Sparse Target Parameter and Channel Estimation in mmWave MIMO OTFS-Aided Integrated Sensing and Communication Systems,” in IEEE Transactions on Communications, vol. 73, no. 2, pp. 1320-1335, Feb. 2025, doi: 10.1109/TCOMM.2024.3440874.
    [108] S. Srivastava, R. K. Singh, A. K. Jagannatham and L. Hanzo, ”Bayesian Learning Aided Simultaneous Row and Group Sparse Channel Estimation in Orthogonal Time Frequency Space Modulated MIMO Systems,” in IEEE Transactions on Communications, vol. 70, no. 1, pp. 635-648, Jan. 2022, doi: 10.1109/TCOMM.2021.3123354.
    [109] K. Huang, M. Qiu, J. Tong, J. Yuan and H. Lin, ”Performance of Orthogonal Delay-Doppler Division Multiplexing Modulation With Imperfect Channel Estimation,” in IEEE Transactions on Communications, vol. 73, no. 5, pp. 3637-3654, May 2025, doi: 10.1109/TCOMM.2024.3487797.
    [110] Y. Liu, S. Zhang, F. Gao, J. Ma and X. Wang, ”Uplink-Aided High Mobility Downlink Channel Estimation Over Massive MIMO-OTFS System,” in IEEE Journal on Selected Areas in Communications, vol. 38, no. 9, pp. 1994-2009, Sept. 2020, doi: 10.1109/JSAC.2020.3000884.
    [111] X. Li, C. Shan, Y. Ma, H. Zhao, S. Jia and D. Zhang, ”A Variable Step-Size Backtracking SAMP Channel Estimation Method for OTFS System,” GLOBECOM 2023 - 2023 IEEE Global Communications Conference, Kuala Lumpur, Malaysia, 2023, pp. 2838-2842, doi: 10.1109/GLOBECOM54140.2023.10437824.
    [112] M. Kollengode Ramachandran and A. Chockalingam, ”MIMO-OTFS in High-Doppler Fading Channels: Signal Detection and Channel Estimation,” 2018 IEEE Global Communications Conference (GLOBECOM), Abu Dhabi, United Arab Emirates, 2018, pp. 206-212, doi: 10.1109/GLOCOM.2018.8647394.
    [113] X. Li, B. Chang and Z. Chen, ”Tensor Decomposition Based THz Channel Estimation in OTFS for Integrated Sensing and Communications,” GLOBECOM 2023 - 2023 IEEE Global Communications Conference, Kuala Lumpur, Malaysia, 2023, pp. 3996-4001, doi: 10.1109/GLOBECOM54140.2023.10437761.
    [114] M. A. Sheikh, A. Rajoriya, P. Singh and R. Budhiraja, ”Coupled Prior-Based Sparse Bayesian Channel Estimation for Superimposed Pilot OTFS Systems,” GLOBECOM 2023 - 2023 IEEE Global Communications Conference, Kuala Lumpur, Malaysia, 2023, pp. 4044-4049, doi: 10.1109/GLOBECOM54140.2023.10437812.
    [115] B. Kazemzadeh, R. Janzen, V. Meghdadi, H. Meghdadi and A. Bradai, ”Enhancing Channel Estimation in High Mobility OTFS Systems: A Novel Pilot-Based Method Exploiting Doppler Axis Diversity in a TransPod Transportation System,” GLOBECOM 2023 - 2023 IEEE Global Communications Conference, Kuala Lumpur, Malaysia, 2023, pp. 4958-4963, doi: 10.1109/GLOBECOM54140.2023.10436957.
    [116] S. Zeng, X. Kuai and Y. -C. Liang, ”A Novel Transceiver Design with Low-Overhead Pilot Pattern and Low-Complexity Channel Estimation in MIMO-OTFS Systems,” GLOBECOM 2023 - 2023 IEEE Global Communications Conference, Kuala Lumpur, Malaysia, 2023, pp. 3246-3251, doi: 10.1109/GLOBECOM54140.2023.10437509.
    [117] F. Gómez-Cuba, ”Compressed Sensing Channel Estimation for OTFS Modulation in Non-Integer Delay-Doppler Domain,” 2021 IEEE Global Communications Conference (GLOBECOM), Madrid, Spain, 2021, pp. 1-6, doi: 10.1109/GLOBECOM46510.2021.9685856.
    [118] F. Long, K. Niu and J. Lin, ”Joint Channel Estimation and Equalization for OTFS Based on EP,” 2021 IEEE Global Communications Conference (GLOBECOM), Madrid, Spain, 2021, pp. 01-06, doi: 10.1109/GLOBECOM46510.2021.9685527.
    [119] H. Zhang, J. Li, T. Zhang and X. Zhu, ”An Efficient Channel Estimation Scheme for Short Frame OTFS Using Impulse-Train Pilots,” GLOBECOM 2022 - 2022 IEEE Global Communications Conference, Rio de Janeiro, Brazil, 2022, pp. 5929-5934, doi: 10.1109/GLOBECOM48099.2022.10001284.
    [120] H. Shi, W. Xing, Y. Zhou, Y. Zhang, S. Zhou and J. Shi, ”Iterative Channel Estimation for OTFS-based LEO-Sat Communication Using Comb-type ZC Sequences,” GLOBECOM 2024 - 2024 IEEE Global Communications Conference, Cape Town, South Africa, 2024, pp. 4902-4907, doi: 10.1109/GLOBECOM52923.2024.10901266.
    [121] D. Ying, F. Ye, R. Q. Hu and Y. Qian, ”Uplink-Aided Downlink Channel Estimation for a High-Mobility Massive MIMO-OTFS System,” GLOBECOM 2022 - 2022 IEEE Global Communications Conference, Rio de Janeiro, Brazil, 2022, pp. 347-352, doi: 10.1109/GLOBECOM48099.2022.10001420.
    [122] Z. Wei, W. Yuan, S. Lit, J. Yuant and D. W. Kwan Ngt, ”A New Off-grid Channel Estimation Method with Sparse Bayesian Learning for OTFS Systems,” 2021 IEEE Global Communications Conference (GLOBECOM), Madrid, Spain, 2021, pp. 01-07, doi: 10.1109/GLOBECOM46510.2021.9685329.
    [123] X. Wei, W. Yuan, F. Gao and G. Han, ”Parameter-Inherited Delay Doppler Channel Estimation Based on Unitary AMP,” GLOBECOM 2023 - 2023 IEEE Global Communications Conference, Kuala Lumpur, Malaysia, 2023, pp. 4182-4187, doi: 10.1109/GLOBECOM54140.2023.10436978.
    [124] B. Shen, Y. Wu, W. Zhang, S. Chatzinotas and B. Ottersten, ”Joint Device Identification, Channel Estimation, and Signal Detection for LEO Satellite-Enabled Random Access,” GLOBECOM 2023 - 2023 IEEE Global Communications Conference, Kuala Lumpur, Malaysia, 2023, pp. 679-684, doi: 10.1109/GLOBECOM54140.2023.10437523.
    [125] T. Ma, Y. Xu, X. Ou, Y. Huang, D. He and W. Zhang, ”Iterative Channel Estimation for OTFS Using ZC Sequence with Low Peak-to-Average Power Ratio,” ICC 2023 - IEEE International Conference on Communications, Rome, Italy, 2023, pp. 2276-2281, doi: 10.1109/ICC45041.2023.10279154.
    [126] W. Shen, L. Dai, S. Han, I. Chih-Lin and R. W. Heath, ”Channel Estimation for Orthogonal Time Frequency Space (OTFS) Massive MIMO,” ICC 2019 - 2019 IEEE International Conference on Communications (ICC), Shanghai, China, 2019, pp. 1-6, doi: 10.1109/ICC.2019.8761362.
    [127] Z. Wei, W. Yuan, S. Li, J. Yuan and D. W. K. Ng, ”Performance Analysis and Window Design for Channel Estimation of OTFS Modulation,” ICC 2021 - IEEE International Conference on Communications, Montreal, QC, Canada, 2021, pp. 1-7, doi: 10.1109/ICC42927.2021.9500798.
    [128] S. P. S. and A. Farhang, ”A Practical Pilot for Channel Estimation of OTFS,” ICC 2023 - IEEE International Conference on Communications, Rome, Italy, 2023, pp. 1319-1325, doi: 10.1109/ICC45041.2023.10279828.
    [129] A. V. Oppenheim, A. S. Willsky, and S. H. Nawab, Signals and Systems, 2nd ed. Upper Saddle River, NJ, USA: Prentice Hall, 1997.
    [130] Priya, Preety, Yi Hong, and Emanuele Viterbo. ”OTFS channel estimation and detection for channels with very large delay spread.” IEEE Transactions on Wireless Communications 23.9 (2024): 11920-11930.
    [131] Proakis, John G., and Masoud Salehi. Digital communications. Vol. 4. New York: McGraw-hill, 2001.
    [132] Wu, Yongzhi, Chong Han, and Zhi Chen. ”DFT-spread orthogonal time frequency space system with superimposed pilots for terahertz integrated sensing and communication.” IEEE Transactions on Wireless Communications 22.11 (2023): 7361-7376.
    [133] Zakaria, Rostom, and Didier Le Ruyet. ”A novel filter-bank multicarrier scheme to mitigate the intrinsic interference: Application to MIMO systems.” IEEE Transactions on Wireless Communications 11.3 (2012): 1112-1123.
    [134] Shen, Cheng, Jinhong Yuan, and Hai Lin. ”Error performance of rectangular pulse-shaped OTFS with practical receivers.” IEEE Wireless Communications Letters 11.12 (2022): 2690-2694.
    [135] Rugini, Luca, and Paolo Banelli. ”BER of OFDM systems impaired by carrier frequency offset in multipath fading channels.” IEEE Transactions on Wireless Communications 4.5 (2005): 2279-2288.
    [136] Singh, Prem, Shashank Tiwari, and Rohit Budhiraja. ”Low-complexity LMMSE receiver design for practical-pulse-shaped MIMO-OTFS systems.” IEEE Transactions on Communications 70.12 (2022): 8383-8399.
    [137] Singh, Prem, et al. ”BER analysis for OTFS zero forcing receiver.” IEEE Transactions on Communications 70.4 (2022): 2281-2297.
    [138] Corvaja, Roberto, and Ana García Armada. ”SINR degradation in MIMO-OFDM systems with channel estimation errors and partial phase noise compensation.” IEEE Transactions on Communications 58.8 (2010): 2199-2203.
    [139] Stojanovic, Milica, John G. Proakis, and Josko A. Catipovic. ”Analysis of the impact of channel estimation errors on the performance of a decision-feedback equalizer in fading multipath channels.” IEEE Transactions on Communications 43.2/3/4 (2002): 877-886.
    [140] Yadav, Rakesh Kumar, et al. ”IRS-OTFS systems: Design of reflection coefficients for low-complexity ZF equalizer.” IEEE Transactions on Vehicular Technology 73.10 (2024): 15721-15726.
    [141] Yuan, Zhengdao, et al. ”Iterative detection for orthogonal time frequency space modulation with unitary approximate message passing.” IEEE transactions on wireless communications 21.2 (2021): 714-725.
    [142] Deng, Lei, et al. ”A unified energy efficiency and spectral efficiency tradeoff metric in wireless networks.” IEEE Communications Letters 17.1 (2012): 55-58.
    [143] Rao, Xiaoxue, et al. ”Interference Analyses for Wireless Mobile Orthogonal Time-Frequency-Space (OTFS) Communication Systems.” IEEE Transactions on Communications (2026).
    [144] Hawkins, Hugo, et al. ”CDMA/OTFS sensing outperforms pure OTFS at the same communication throughput.” IEEE Open Journal of Vehicular Technology 6 (2025): 502-519.
    [145] Chen, Jie, Xianbin Wang, and Lajos Hanzo. ”OTFS-MDMA: An elastic multi-domain resource utilization mechanism for high mobility scenarios.” IEEE Journal on Selected Areas in Communications 43.4 (2025): 1405-1420.
    [146] Y. Fan, C. Yang, R. Yuan and M. Peng, ”Dynamic Delay-Doppler-Angle Domain Channel Tracking for THz Massive MIMO-OTFS Communication Systems,” in IEEE Transactions on Wireless Communications, vol. 24, no. 6, pp. 5163-5178, Jun. 2025, doi: 10.1109/TWC.2025.3546417.
    [147] B. C. Pandey, S. K. Mohammed, P. Raviteja, Y. Hong, and E. Viterbo, “Low complexity precoding and detection in multi-user massive MIMO OTFS downlink,” IEEE Transactions on Vehicular Technology, vol. 70, no. 5, pp. 4389–4405, May 2021, doi: 10.1109/TVT.2021.3061694.
    [148] Y. Liu, M. Chen, C. Pan, T. Gong, J. Yuan and J. Wang, ”OTFS Versus OFDM: Which is Superior in Multiuser LEO Satellite Communications,” in IEEE Journal on Selected Areas in Communications, vol. 43, no. 1, pp. 139-155, Jan. 2025, doi: 10.1109/JSAC.2024.3460060.
    [149] Z. Zhang, Y. Wu, Z. Ma, X. Lei, L. Lei and Z. Wei, ”Coordinated Multi-Satellite Transmission for OTFS-Based 6G LEO Satellite Communication Systems,” in IEEE Journal on Selected Areas in Communications, vol. 43, no. 1, pp. 156-170, Jan. 2025, doi: 10.1109/JSAC.2024.3460108.
    [150] S. Zheng, S. Wu, H. Jia, Z. Ji, A. Xiao and L. Kuang, ”Attention-Enhanced OAMP: An Unrolled Channel Estimation Network for Massive MIMO-OTFS LEO Satellite Systems,” in IEEE Transactions on Communications, vol. 74, pp. 154-169, 2026, doi: 10.1109/TCOMM.2025.3618703.
    [151] R. Chong, S. Li, Z. Wei, M. Matthaiou, D. W. K. Ng and G. Caire, ”Cross-Domain Iterative Detection for OTFS Transmission With Frequency Domain Equalization,” in IEEE Transactions on Communications, vol. 73, no. 10, pp. 9886-9902, Oct. 2025, doi: 10.1109/TCOMM.2025.3581967.
    [152] S. K. Dora, S. J. Darak and H. B. Mishra, ”Low Complexity High Speed Channel Estimation for OTFS on System on Chip,” in IEEE Transactions on Circuits and Systems I: Regular Papers, early access, 2025, doi: 10.1109/TCSI.2025.3632906.

    下載圖示
    校外:立即公開
    QR CODE