Underwater visible light communication sensing integrated waveform design based on OTFS-LFM
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摘要:
随着对水下通信需求的不断增加，水下可见光通信(UVLC)系统备受关注。通信感知一体化的概念在UVLC系统中的实现成为了一个新的研究热点。正交时频空(OTFS)技术以其在高多普勒和高时延信道中的卓越性能受到学术界广泛关注，为系统提供了强大的通信支持。与此同时，线性调频(LFM)技术由于其对多普勒频移的低敏感性而在无线通信领域广泛应用。将OTFS技术与LFM技术相融合，设计了一种UVLC感知一体化系统。通过实验仿真对比发现，系统在误码率(BER)、模糊函数及目标速度和距离信息获取方面表现出色。融合OTFS技术和LFM技术的系统在复杂水下环境中展现出卓越的适应性，为水下通信领域带来新的可能性。同时，考虑3种常见的调制方式，进一步分析不同调制对系统性能的可能影响，为系统优化提供有益的参考。
Abstract:Underwater visible light communication (UVLC) systems have gained a lot of attention due to the growing need for underwater communication. At present, the concept of communication and sensing integration has emerged, especially the UVLC systems’ development has become a new research hotspot. Orthogonal time-frequency space (OTFS) technology has garnered significant attention due to its exceptional performance in high-Doppler and high-delay channels, thereby providing robust communication support for the system. At the same time, linear frequency modulation (LFM) technology is widely used in the field of wireless communication due to its low sensitivity to Doppler frequency shift. In this paper, an UVLC perception integration system is designed by combining OTFS technology with LFM technology. Through experimental simulation comparison, the system performs well in bit error rate (BER), ambiguity function, target speed and distance information acquisition. The system, which combines OTFS technology and LFM technology, exhibits excellent adaptability in complex underwater environments, bringing new possibilities to the field of underwater communication. At the same time, three common modulation methods are also considered, and the possible effects of different modulations on system performance are further analyzed, offering useful insights for system optimization.
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图 3 不同速度下的OFDM与OTFS误码率对比
Figure 3. BER comparison between OFDM and OTFS at different speeds
图 4 CCC算法计算OFDM雷达模糊函数
Figure 4. CCC algorithm calculation of OFDM radar ambiguity function
图 5 CCC算法计算OTFS雷达模糊函数
Figure 5. CCC algorithm calculation of OTFS radar ambiguity function
图 6 MUSIC算法估计OFDM感知性能
Figure 6. Estimating OFDM sensing performance using the MUSIC algorithm
图 7 TPSE算法估计OTFS感知性能
Figure 7. Estimating OTFS sensing performance using the TPSE algorithm
图 13 基于OTFS的16QAM-LFM信号模糊函数
Figure 13. Ambiguity function of 16QAM-LFM signal based on OTFS
图 14 基于OTFS的BPSK-LFM信号模糊函数
Figure 14. Ambiguity function of BPSK-LFM signal based on OTFS
图 15 基于OTFS的MSK-LFM信号模糊函数
Figure 15. Ambiguity function of MSK-LFM signal based on OTFS
表 1 通信系统仿真参数
Table 1. Simulation parameters of communication system
载波
频率/GHz子载波
数量子载波
符号数子载波
间隔/kHz系统
带宽/MHz调制
方式信道
估计1 128 14 15 1 16QAM 理想 
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表 2 3种系统下的感知性能
Table 2. Sensing performance under three systems
系统 速度/(m·s−1) 距离/m OFDM 19.32 30.14 OTFS 20.65 29.88 OTFS-LFM 20.11 30.02
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表 3 不同一体化波形BER与接收SNR对比
Table 3. Comparison of BER and received SNR of different integrated waveforms
调制方式 BER BPSK-LFM $\dfrac{1}{2}{\text{erfc}}(\sqrt {{S_{{\text{NR}}}}} )$ MSK-LFM $\dfrac{1}{2}{\text{erfc}}(\sqrt {{S_{{\text{NR}}}}} )$
16QAM-LFM$ 1 - {\left( {1 - \dfrac{3}{4}{\text{erfc}}\left( {\sqrt {\dfrac{1}{{20}}\sqrt {{S_{{\text{NR}}}}} } } \right)} \right)^2} $ 注：${S_{{\text{NR}}}}$为接收信噪比；$ \text{erfc(} \cdot \text{)} $表示误差函数。
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表 4 信道及光源参数设置
Table 4. Channel and light source parameter settings
透镜
折射率LED
视场角/(°)光学集光器
增益单LED
发射功率/WLED
个数海水光衰减
系数1.5 70 2.5481 1 10 0.056
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表 5 不同调制方式下的感知性能
Table 5. Sensing performance under different modulation modes
调制方式 速度/(m·s−1) 距离/m BPSK-LFM 22.630 29.821 MSK-LFM 19.022 29.778 16QAM-LFM 20.178 30.013
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