基于改进的AdaBoost无线光通信信号检测算法

为了提升无线光通信系统接收灵敏度, 采用一种基于改进基分类器系数的AdaBoost弱光信号检测算法, 解决多像素光子计数器(MPPC)在弱光条件下的信号检测问题。该算法采用k最近邻(KNN)为基分类器组建强分类器, 针对传统AdaBoost算法基分类器系数仅与错误率有关而产生冗余的基分类器消耗系统资源的问题, 提出一种基于错误和正确分类样本权重的基分类器系数优化AdaBoost算法(W-AdaBoost), 将信号解调问题转换为分类问题; 并采用波长450 nm半导体激光器、MPPC光电转换器件搭建了无线光通信系统。结果表明, 系统在通信速率为2 Mbit/s、误比特率为3.8×10-3时, 改进的W-AdaBoost-KNN算法较传统AdaBoost-KNN和单一KNN算法,灵敏度分别提升了1.6 dB和4.8 dB左右。此研究结果说明W-AdaBoost-KNN算法可提高弱光条件下的信号检测效率, 提升无线光通信系统接收灵敏度。

Abstract

In order to improve the receiving sensitivity of the wireless optical communication system, an AdaBoost weak-light signal detection algorithm based on the improved base classifier coefficient was adopted to solve the signal detection problem of multi-pixel photon counter (MPPC) under weak-light conditions. In this algorithm, k-nearest neighbor (KNN) was used as the base classifier to build a strong classifier. A W-AdaBoost algorithm based on the weights of incorrect and correct classification samples was proposed to solve the problem of that the traditional AdaBoost algorithm’s base classifier coefficients are only related to the error rate, which causes redundant base classifiers to consume system resources. The W-AdaBoost algorithm transforms the issue of signal demodulation into classification, a 450 nm semiconductor laser and MPPC photoelectric conversion device are used to build a wireless optical communication system. The experimental results show that the sensitivity of the improved W-AdaBoost-KNN algorithm is about 1.6 dB and 4.8 dB higher than that of the traditional AdaBoost-KNN algorithm and the single KNN algorithm respectively, when the communication rate of the system is 2 Mbit/s and the bit error rate is 3.8×10-3. The research results show that W-AdaBoost-KNN algorithm can improve the signal detection efficiency under weak-light conditions and improve the receiving sensitivity of the wireless optical communication systems.

参考文献

[1] MENAKA D, GAUNI S, MANIMEGALAI C, et al. Vision of IoUT:Advances and future trends in optical wireless communication［J］. Journal of Optics,2021, 50(3):439-452.

[2] WANG B, WU Q, LIU L Q, et al. Research progress on the underwater wireless optical communication system［J］. Laser Technology, 2022, 46(1): 99-109(in Chinese).

[3] CHEN X, LYU W, ZHANG Z, et al. 56-m/3.31-Gbps underwater wireless optical communication employing Nyquist single carrier frequency domain equalization with noise prediction［J］. Optics Express, 2022, 28(16):23784-23795.

[4] WANG J, LU C, LI S, et al. 100 m/500 Mbps underwater optical wireless communication using an NRZ-OOK modulated 520 nm laser diode［J］. Optics Express, 2019, 27(9):12171-12181.

[5] ZHANG G, YU C, ZHU C, et al. Feasibility study of multi-pixel photon counter serving as the detector in digital optical communications［J］. Optik—International Journal for Light and Electron Optics, 2013, 124(22):5781-5786.

[6] ZHAO Sh, GUO J, LIU H B, et al. Application of multi-pixel photon counters to single photon detection［J］. Optics and Precision Engineering, 2011, 19(5): 972-976(in Chinese) .

[7] SHEN J, WANG J, CHEN X, et al. Towards power-efficient long-reach underwater wireless optical communication using a multi-pixel photon counter［J］. Optics Express, 2018, 26(18):23565-23571.

[8] SHEN J, WANG J, YU C, et al. Single LED-based 46-m underwater wireless optical communication enabled by a multi-pixel photon counter with digital output［J］. Optics Communications, 2019, 438(1):78-82.

[9] WANG J L, YANG X Q, LV W Ch, et al. Underwater wireless optical communication based on multi-pixel photon counter and OFDM modulation［J］. Optics Communications, 2019, 451(22):181-185.

[10] REN Zh Y, WU G F, LIU J H, et al. An experimental system of low illumination visible light communication based on multi-pixel photon counter［J］. Journal of Signal Processing,2020, 36(7):1159-1165(in Chinese).

[11] MIURA T, NAKAMOTI T, KATAOKA J, et al. Development of a scintillation detector using a MPPC as an alternative to an APD［J］. Journal of Instrumentation, 2012, 7(2):1313-1318.

[12] CAI R, ZHANG M, DAI D, et al. Analysis of the underwater wireless optical communication channel based on a comprehensive multiparameter model［J］. Applied Sciences, 2021, 11(13):6051.

[13] LIONIS A, PEPPAS K, NISTAZAKIS H E, et al. Experimental performance analysis of an optical communication channel over maritime environment［J］. Electronics, 2020, 9(7):1109.

[14] WUEST T, WEIMER D, IRGENS C, et al. Machine learning in manufacturing: Advantages, challenges, and applications［J］. Production & Manufacturing Research, 2016, 4(1):23-45.

[15] OSHEA T, HOYDIS J. An introduction to deep learning for the physical layer［J］. IEEE Transactions on Cognitive Communications & Networking, 2017, 3(4):563-575.

[16] YUAN Y, ZHANG M, LUO P, et al. SVM detection for superposed pulse amplitude modulation in visible light communications［C］// International Symposium on Communication Systems. New York,USA: IEEE, 2016:1-5.

[17] MENG F, WU L. Demodulator based on deep belief networks in communication system［C］//International Conference on Communication. New York,USA: IEEE, 2017:1-5.

[18] WANG H, WU Z, MA S, et al. Deep learning for signal demodulation in physical layer wireless communications: Prototype platform, open dataset, and analytics［J］. IEEE Access, 2019, 7:30792-30801.

[19] ZOU F J. Research on signal modulation algorithm based on Adaboost-SVM classifier in digital communication［J］. Machine Design and Manufacturing Engineering, 2021, 50(6):111-116(in Chin-ese).

[20] HU Y J, DU J L, DONG Y, et al. Rainfall prediction based on improved AdaBoost-C4.5 algorithm［J］. Modern Electronics Technique, 2021, 44(14):6-10(in Chinese).

[21] LI J, ZHANG H, OU J , et al. A multipulse radar signal recognition approach via hrf-net deep learning models［J］. Computational Inte-lligence and Neuroscience, 2021, 2021:1-9.

[22] YAN H, LIU Y, WANG X, et al. A face detection method based on skin color features and adaboost algorithm［J］. Journal of Physics, 2021, 1748(4):042015.

[23] FREUND Y, SCHAPIRE R E. A desicion-theoretic generalization of on-line learning and an application to boosting［J］. Lecture Notes in Computer Science,1995, 904(1):23-37.

贺锋涛, 王乐莹, 王晓波, 杨祎, 李碧丽. 基于改进的AdaBoost无线光通信信号检测算法[J]. 激光技术, 2023, 47(5): 659. HE Fengtao, WANG Leying, WANG Xiaobo, YANG Yi, LI Bili. Signal detection algorithm of wireless optical communication based on the improved AdaBoost[J]. Laser Technology, 2023, 47(5): 659.

基于改进的AdaBoost无线光通信信号检测算法

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