Low-Complexity OTFS Channel Equalization Based on CLU-MMSE

doi:10.12142/ZTECOM.202601004

ZTE Communications ›› 2026, Vol. 24 ›› Issue (1): 16-24.DOI: 10.12142/ZTECOM.202601004

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Low-Complexity OTFS Channel Equalization Based on CLU-MMSE

Jia Haoxiang¹(), Zhao Danfeng¹, Xin Yu², Hua Jian²

^1.College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
^2.ZTE Corporation, Shenzhen 518057, China

Received:2024-03-18 Online:2026-03-25 Published:2026-03-17
About author:Jia Haoxiang (jiahaoxiang@hrbeu.edu.cn) received his BS degree in communication engineering from Shandong University of Science and Technology, China in 2018. He is working toward his PhD degree at the School of Information and Communication Engineering, Harbin Engineering University, China. His main research interests include high-performance coding and modulation schemes.
Zhao Danfeng received his PhD degree in communication systems from Harbin Engineering University, China in 2006， where he is currently a professor. His research interests include network coding, underwater acoustic sensor networks, and high-performance coding and modulation.
Xin Yu graduated from Beijing University of Posts and Telecommunications, China in 2003. He is currently a senior engineer and senior expert in technical pre-research at ZTE Corporation, whose research direction is wireless communication technology. He first proposed the FB-OFDM and GFB-OFDM waveform schemes and has published dozens of papers on waveform technologies. He is now mainly responsible for the pre-research of 6G candidate new waveform technologies.
Hua Jian received his master’s degree from Harbin Engineering University, China. He is currently an intermediate engineer at ZTE Corporation. His research interests include phase noise modeling, compensation scheme design, waveform modulation, and other technologies in terahertz communication scenarios.
Supported by:
ZTE Industry?University?Institute Cooperation Funds(KY10800230005)

Abstract

Abstract:

In view of the high computational complexity of traditional linear equalization algorithms in Orthogonal Time Frequency Space (OTFS) systems, a minimum mean square error (MMSE) channel equalization algorithm based on Matrix Chunking Lower and Upper Triangular Decomposition (CLU) is proposed. The proposed algorithm derives the structural properties of the chunked MMSE equalization matrix by leveraging the block diagonal structure of the Cyclic Prefix OTFS (CP-OTFS) time-domain channel matrix and the quasi-band structure of its constituent block matrices. On this basis, triangular decomposition combined with forward and backward substitution is used to avoid matrix inversion. This approach significantly reduces the complexity of the MMSE algorithm without sacrificing its performance.

Key words: OTFS, equalization algorithm, MMSE, Lower and Upper Triangular Decomposition

Jia Haoxiang, Zhao Danfeng, Xin Yu, Hua Jian. Low-Complexity OTFS Channel Equalization Based on CLU-MMSE[J]. ZTE Communications, 2026, 24(1): 16-24.

Figures/Tables 10

Figure 1 Schematic diagram of the relationship between channel resource transformations in the TF and DD domains

Figure 2 Block diagram of an OTFS communication system

Figure 3 Schematic diagram of a CP-OTFS transmitter

Figure 4 Schematic of CP-OTFS time domain channel matrix structure

Figure 5 Schematic diagram of the LU decomposition of the chunked matrix

Figure 6 Flowchart of the proposed CLU-MMSE algorithm

Table 1 Computational complexity comparison of linear equalization algorithms

Algorithm name	Complexity order
Traditional matrix-inverse ZF	$Ο ((M N) 3)$
Traditional matrix-inverse MMSE	$Ο ((M N) 3)$
MMSE based on doubly circulant characteristic	$Ο (M N l o g 2 M N)$
Proposed CLU-MMSE	$Ο (M N l o g 2 N)$

Table 1 Computational complexity comparison of linear equalization algorithms

Algorithm name	Complexity order
Traditional matrix-inverse ZF	$Ο ((M N) 3)$
Traditional matrix-inverse MMSE	$Ο ((M N) 3)$
MMSE based on doubly circulant characteristic	$Ο (M N l o g 2 M N)$
Proposed CLU-MMSE	$Ο (M N l o g 2 N)$

Figure 7 BER performance comparison under 4QAM modulation

Figure 8 BER performance comparison under 16QAM modulation

Table 2. Simulation parameters

References 11

[1]	He X, Jia H X, Sun Y T, et al. Low-complexity iterative equalization for OTFS based on alternating minimization [J]. Journal of systems engineering and electronics, 2023, 34(4): 851–860. DOI: 10.23919/JSEE.2023.000089
[2]	Raviteja P, Hong Y, Viterbo E, et al. Effective diversity of OTFS modulation [J]. IEEE wireless communications letters, 2020, 9(2): 249–253. DOI: 10.1109/LWC.2019.2951758
[3]	Surabhi G D, Augustine R M, Chockalingam A. On the diversity of uncoded OTFS modulation in doubly-dispersive channels [J]. IEEE transactions on wireless communications, 2019, 18(6): 3049–3063. DOI: 10.1109/TWC.2019.2909205
[4]	Li S Y, Yuan J H, Yuan W J, et al. Performance analysis of coded OTFS systems over high-mobility channels [J]. IEEE transactions on wireless communications, 2021, 20(9): 6033–6048. DOI: 10.1109/TWC.2021.3071493
[5]	Cheng J Q, Jia C L, Gao H, et al. OTFS based receiver scheme with multi-antennas in high-mobility V2X systems [C]//Proc. IEEE International Conference on Communications Workshops (ICC Workshops). IEEE, 2020: 1–6. DOI: 10.1109/iccworkshops49005.2020.9145313
[6]	Shan Y R, Wang F G, Hao Y X, et al. Doppler rate estimation for OTFS via large-scale antenna array [J]. ZTE communications, 2025, 23(1): 115–122. doi: 10.12142/ZTECOM.202501015
[7]	Surabhi G D, Chockalingam A. Low-complexity linear equalization for OTFS modulation [J]. IEEE communications letters, 2020, 24(2): 330–334. DOI: 10.1109/LCOMM.2019.2956709
[8]	Tiwari S, Das S S, Rangamgari V. Low complexity LMMSE receiver for OTFS [J]. IEEE communications letters, 2019, 23(12): 2205–2209. DOI: 10.1109/LCOMM.2019.2945564
[9]	Sun Y T, Jia H X, He X, et al. A low complexity OTFS detection algorithm based on GA-MP [J]. Telecommunication engineering, 2024, 64(2): 288–294. DOI: 10.20079/j.issn.1001-893x.220818005
[10]	Xiao L X, Li S, Qian Y, et al. An overview of OTFS for Internet of Things: concepts, benefits, and challenges [J]. IEEE Internet of Things journal, 2022, 9(10): 7596–7618. DOI: 10.1109/JIOT.2021.3132606
[11]	Das S S, Rangamgari V, Tiwari S, et al. Time domain channel estimation and equalization of CP-OTFS under multiple fractional Dopplers and residual synchronization errors [J]. IEEE access, 2021, 9: 10561–10576. DOI: 10.1109/ACCESS.2020.3046487

Low-Complexity OTFS Channel Equalization Based on CLU-MMSE

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