面对高速移动场景的OTFS系统导频设计方法

李一兵; 汤云鹤; 简鑫; 孙骞; 陈浩

doi:10.11999/JEIT240349

面对高速移动场景的OTFS系统导频设计方法

doi: 10.11999/JEIT240349 cstr: 32379.14.JEIT240349

李一兵¹,
汤云鹤¹,
简鑫¹,
孙骞^1, ,,
陈浩²

1.
哈尔滨工程大学信息与通信工程学院哈尔滨 150001
2.
武汉船舶通信研究所武汉 430010

基金项目: 国家自然科学基金(52271311)，中国船舶集团有限公司第七二二所 2022 年创新基金(2022J-4)

详细信息

作者简介:
李一兵：男，教授，研究方向为通信信号处理

汤云鹤：男，硕士生，研究方向为OTFS通信信号处理

简鑫：男，硕士生，研究方向为5G通信信号处理

孙骞：男，副教授，研究方向为通信信号处理

陈浩：男，高级工程师，研究方向为通信信号处理

通讯作者:
孙骞　qsun@hrbeu.edu.cn

中图分类号: TN929.5
计量
- 文章访问数: 344
- HTML全文浏览量: 180
- PDF下载量: 53
- 被引次数: 0
出版历程
- 收稿日期: 2024-05-07
- 修回日期: 2025-01-24
- 网络出版日期: 2025-02-09
- 刊出日期: 2025-02-28

Pilot Design Method for OTFS System in High-Speed Mobile Scenarios

1.
College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
2.
Wuhan Maritime Communication Research Institute, Wuhan 430010, China

Funds: The National Natural Science Foundation of China General Project (52271311), 2022 Innovation Foundation of China State Shipbuilding Corporation 722 Institute (2022J-4)

摘要

摘要: 正交时频空(Orthogonal Time Frequency Space, OTFS)系统由于在面对高速移动通信场景下的时频双色散信道时的优异性能受到了广泛关注。为了准确获取信道状态信息，采用基于压缩感知的信道估计方法，并辅以特殊的导频序列完成信道估计。该文针对导频优化问题，提出了一种基于改进遗传算法的OTFS导频序列优化方法，该方法以互相关最小化为优化目标，采用遗传算法进行寻优，并能够自适应调整交叉和变异概率，在较少的迭代次数下即可实现比传统伪随机序列更优的互相关性，能够有效提高信道估计的准确性。此外，考虑到目标函数的计算量较大，该文分析了互相关的计算过程，并对其中的冗余计算进行了化简，与直接计算字典集的互相关值相比大大提高了算法的优化效率。
- 正交时频空(OTFS) /
- 压缩感知 /
- 导频优化 /
- 遗传算法
Abstract: Objective Orthogonal Time Frequency Space (OTFS) have attracted significant attention in recent years due to excellent performance in high-speed mobile communication scenarios characterized by time-frequency double-selective channels. Accurate and efficient channel state information acquisition is critical for these systems. To address this, a channel estimation method based on compressed sensing is employed, using specialized pilot sequences. The performance of such channel estimation algorithms based on compressed sensing and the cross-correlation properties of the dictionary sets generated by these pilot sequences. which vary depending on the sequence design. This study addresses the pilot design problem in OTFS communication systems, proposing an optimization method to identify pilot sequences that enhance channel estimation accuracy effectively. Methods A pilot-assisted channel estimation algorithm based on compressed sensing is employed to estimate the delay and Doppler channel state information in OTFS systems for high-speed mobile scenarios. To improve channel estimation accuracy in the Delay-Doppler domain and achieve better performance than traditional pseudo-random sequences, this study proposes a pilot sequence optimization method using an Improved Genetic Algorithm (IGA). The algorithm takes the cross-correlation among dictionary set columns as the optimization goal, leveraging the GA’s strong integer optimization capabilities to search for optimal pilot sequences. An adaptive adjustment strategy for crossover and mutation probabilities is also introduced to enhance the algorithm’s convergence and efficiency. Additionally, to address the high computational complexity of the fitness function, the study analyzes the expressions for calculating cross-correlation among dictionary set columns and simplifies redundant calculations, thereby improving the overall optimization efficiency. Results and Discussions This study investigates the channel estimation performance of OTFS systems using different pilot sequences. The simulation parameters are presented in (Table 1), and the simulation results are shown in (Figure 2), (Figure 3), and (Figure 4). (Figure 2) illustrates the convergence performance of several commonly used group heuristic intelligent optimization algorithms applied to the pilot optimization problem, including the Particle Swarm Optimization (PSO) algorithm, Discrete Particle Swarm Optimization (DPSO) algorithm, Snake Optimization (SO) algorithm, and Genetic Algorithm (GA). The results indicate that the performance of common continuous optimization algorithms, such as PSO and SO, is comparable, while DPSO slightly outperforms traditional PSO, GA, due to its unique genetic and mutation mechanisms, demonstrates significantly faster convergence and better solutions. Furthermore, this study proposes a targeted IGA capable of adaptively adjusting crossover and mutation probabilities, leading to better solutions with fewer iterations. The objective function calculation process is also analyzed and simplified, reducing its computational complexity from $ {O}({\lambda ^2}k_p^2{l_p}) $ to $ {O}(\lambda {k_p}{l_p}) $ without altering the cross-correlation coefficient, which significantly reduces the computational load while maintaining optimization efficiency. (Figure 3) and (Figure 4) depict the Normalized Mean Square Error (NMSE) and Bit Error Rate (BER) performance of OTFS systems using different pilot sequences for channel estimation. The commonly used pseudo-random sequences, including m-sequences, Gold sequences, Zadoff-Chu sequences, and the optimized sequences generated by the proposed algorithm, are compared. The results demonstrate that the optimized pilot sequences generated by the proposed algorithm achieve superior channel estimation performance compared with other pilot sequences. Conclusions This study analyzes a pilot-assisted channel estimation method for OTFS systems based on compressed sensing and proposes a pilot sequence optimization approach using an IGA to address the pilot optimization challenge. The optimization objective function is constructed based on the correlation among dictionary set columns, and an adaptive adjustment strategy for crossover and mutation probabilities is proposed to enhance the algorithm’s convergence speed and optimization capability, outperforming other commonly used group heuristic optimization algorithms. To address the high computational complexity associated with directly calculating cross-correlation coefficients, the calculation steps are simplified, reducing the complexity from $ {O}({\lambda ^2}k_p^2{l_p}) $ to $ {O}(\lambda {k_p}{l_p}) $, while preserving the cross-correlation properties, thereby improving optimization efficiency. Simulation results demonstrate that the proposed optimized pilot sequences offer better channel estimation performance than traditional pseudo-random pilot sequences, with relatively low optimization complexity.
- Orthogonal Time Frequency Space (OTFS) /
- Compressed sensing /
- Pilot optimization /
- Genetic Algorithm (GA)

HTML全文

图 1 OTFS系统总体框图

下载: 全尺寸图片幻灯片

图 4 不同导频序列的信道估计误码率性能

下载: 全尺寸图片幻灯片

图 2 不同智能算法的导频优化收敛性能对比

下载: 全尺寸图片幻灯片

图 3 不同导频序列的信道估计归一化均方误差性能

下载: 全尺寸图片幻灯片

表 1 OTFS系统仿真参数

参数	值
时延点数$M$	16
多普勒点数$N$	16
中心频率${f_{\rm{c}}}$	4×10⁹ Hz
子载波间隔$\Delta f$	10⁵ Hz
最大时延${\tau _{\max }}$	2 510 ns
最大移动速度${v_{\max }}$	1 000 km/h
字典集过采样因子$\lambda $	10

下载: 导出CSV

参考文献(20)

[1]	HADANI R, RAKIB S, TSATSANIS M, et al. Orthogonal time frequency space modulation[C]. IEEE Wireless Communications & Networking Conference, San Francisco, USA, 2017: 1–6. doi: 10.1109/WCNC.2017.7925924.
[2]	QIAN Mi, JI Fei, GE Yao, et al. Block-wise index modulation and receiver design for high-mobility OTFS communications[J]. IEEE Transactions on Communications, 2023, 71(10): 5726–5739. doi: 10.1109/TCOMM.2023.3288568.
[3]	WANG Xuehan, SHI Xu, WANG Jintao, et al. On the Doppler squint effect in OTFS systems over doubly-dispersive channels: Modeling and evaluation[J]. IEEE Transactions on Wireless Communications, 2023, 22(12): 8781–8796. doi: 10.1109/TWC.2023.3265989.
[4]	SHEN Wenqian, DAI Linglong, AN Jianping, et al. Channel estimation for orthogonal time frequency space (OTFS) massive MIMO[J]. IEEE Transactions on Signal Processing, 2019, 67(16): 4204–4217. doi: 10.1109/TSP.2019.2919411.
[5]	WEN Haifeng, YUAN Weijie, YUEN C, et al. MF-OAMP-based joint channel estimation and data detection for OTFS systems[J]. IEEE Transactions on Vehicular Technology, 2024, 73(2): 2948–2953. doi: 10.1109/TVT.2023.3319562.
[6]	RAVITEJA P, PHAN K T, and HONG Yi. Embedded pilot-aided channel estimation for OTFS in delay-Doppler channels[J]. IEEE Transactions on Vehicular Technology, 2019, 68(5): 4906–4917. doi: 10.1109/TVT.2019.2906357.
[7]	LIU Tianjun, FAN Pingzhi, LI Jiangdong, et al. Sequence design for optimized ambiguity function and PAPR under arbitrary spectrum hole constraint[C]. 2017 Eighth International Workshop on Signal Design and Its Applications in Communications (IWSDA), Sapporo, Japan, 2017: 173–177. doi: 10.1109/IWSDA.2017.8097080.
[8]	ZHANG Hongyang, HUANG Xiaojing, and ZHANG J A. Low-overhead OTFS transmission with frequency or time domain channel estimation[J]. IEEE Transactions on Vehicular Technology, 2024, 73(1): 799–811. doi: 10.1109/TVT.2023.3305921.
[9]	WANG Siqiang, GUO Jing, WANG Xinyi, et al. Pilot design and optimization for OTFS modulation[J]. IEEE Wireless Communications Letters, 2021, 10(8): 1742–1746. doi: 10.1109/LWC.2021.3078527.
[10]	OUCHIKH R, CHONAVEL T, AÏSSA-EL-BEY A, et al. Joint channel estimation and data detection for high rate orthogonal time frequency space systems[J]. International Journal of Communication Systems, 2023, 36(16): e5579. doi: 10.1002/dac.5579.
[11]	ZHANG Yi, VENKATESAN R, DOBRE O A, et al. Novel compressed sensing-based channel estimation algorithm and near-optimal pilot placement scheme[J]. IEEE Transactions on Wireless Communications, 2016, 15(4): 2590–2603. doi: 10.1109/TWC.2015.2505315.
[12]	DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289–1306. doi: 10.1109/TIT.2006.871582.
[13]	CHEN Jianqiao, ZHANG Xi, and ZHANG Ping. Bayesian learning for BPSO-based pilot pattern design over sparse OFDM channels[C]. IEEE International Conference on Communications (ICC), Dublin, Ireland, 2020: 1–6. doi: 10.1109/ICC40277.2020.9148704.
[14]	SRINIVAS M and PATNAIK L M. Adaptive probabilities of crossover and mutation in genetic algorithms[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1994, 24(4): 656–667. doi: 10.1109/21.286385.
[15]	YUAN Pu. Low PAPR pilot for delay-Doppler domain modulation[C]. 2022 IEEE/CIC International Conference on Communications in China (ICCC Workshops), Foshan, China, 2022: 466–471. doi: 10.1109/ICCCWorkshops55477.2022.9896687.
[16]	HASHIM F A and HUSSIEN A G. Snake Optimizer: A novel meta-heuristic optimization algorithm[J]. Knowledge-Based Systems, 2022, 242: 108320. doi: 10.1016/j.knosys.2022.108320.
[17]	CAI Jun, HE Xueyun, and SONG Rongfang. Pilot optimization for structured compressive sensing based channel estimation in large-scale MIMO systems with superimposed pilot pattern[J]. Wireless Personal Communications: An International Journal, 2018, 100(3): 977–993. doi: 10.1007/s11277-018-5361-x.
[18]	KIM Y J, SULTAN Q, and CHO Y S. Pilot-based sequence design to overcome a blockage in mmWave cellular systems[C]. 2020 International Conference on Information and Communication Technology Convergence (ICTC), Jeju, Korea (South), 2020: 42–44. doi: 10.1109/ICTC49870.2020.9289488.
[19]	FRANK R, ZADOFF S, and HEIMILLER R. Phase shift pulse codes with good periodic correlation properties (Corresp.)[J]. IRE Transactions on Information Theory, 1962, 8(6): 381–382. doi: 10.1109/TIT.1962.1057786.
[20]	HU Weiwen, LI C P, and CHEN J C. Peak power reduction for pilot-aided OFDM systems with semi-blind detection[J]. IEEE Communications Letters, 2012, 16(7): 1056–1059. doi: 10.1109/LCOMM.2012.050412.120482.