An Impulse Noise Suppression Algorithm Based on Dual Interface Diversity and Data Samples
Chen Zhixiong1,2, Zhang Zhikun1, Zhao Xiongwen1,2
1. School of Electrical & Electronic Engineering North China Electric Power University Baoding 071003 China;; 2. Hebei Key Laboratory of Power Internet of Things Technology Baoding 071003 China
Abstract：Hybrid power line and wireless communication has wide application prospect in the Internet of Things. How to suppress the pulse noise of the power line channel in dual interface communication and improve the reliability of communication system is one of the key problems to be solved urgently. Aiming at the interference problem of power line burst pulse noise (BIN) in the power line and wireless dual interface communication, this paper put forward a BIN suppression algorithm based on diversity signal cancellation and adaptive threshold estimation (DSC-ATE), by using the independence and difference of power line and wireless channel and the consistency of diversity signal. In DSC-ATE, the wireless channel is used to assist the power line channel to extract the noise samples, and then the pulse noise threshold is estimated adaptively through the noise samples, finally the pulse noise is separated from the received signal. The traditional OFDM system has the problem of peak-to-flat ratio PAPR, which is difficult to obtain the position information of pulse noise directly in the power line channel. Based on the dual-interface communication architecture, a noise sample extraction method based on diversity signal cancellation is proposed, which improves the accuracy of noise detection and can be applied to the optimal threshold prediction. The simulation results show that after diversity signal cancellation, the effect of PAPR is minimal because only background interference remains, and the precise position information of impulse noise can be obtained by using nonlinear transformation, which provides data support for the optimal threshold estimation algorithm. Secondly, the sample space of impulse noise is constructed by means of signal cancellation processing in diversity transmission, in which the noise data of low SNR ensures the diversity of samples. Then, an optimal threshold estimation algorithm based on noise sample data and nonlinear functions is proposed, which can minimize the bit error rate of the communication system and does not require the prior information of impulse noise. The simulation result shows that the performance of threshold precision, transmission rate, and bit error rate under different SNRs of the algorithm is superior to the traditional algorithms such as the weighted combination criterion (WCC) and siegert criterion (SC). The proposed algorithm also has certain robustness and robustness under different channel fading and noise parameters. Finally, aiming at the problem of pulse noise elimination in non-stationary environment, the noise threshold is iteratively estimated by using the data sample and the objective function minimization/maximization algorithm, so as to determine the location information of the pulse noise. The convergence rate of the algorithm is adjusted by importing parameters such as learning rate and discount factor, and the threshold can be dynamically adjusted according to the changes of the environment, so as to achieve the effective compromise between the robustness and convergence rate of the algorithm. The simulation result shows that when the learning rate of the algorithm is fixed, the larger the data sample length is, the smaller and more stable the threshold fluctuation is. However, the large data sample length will increase the computational burden, storage burden, and data sample acquisition time, which slow down the update speed of the threshold. In the future, the proposed algorithm DSC-ATE can be combined with sparse theory and compressed sensing for other noise models such as narrowband noise, to further improve the universality of the algorithm.
陈智雄, 张志坤, 赵雄文. 基于双接口分集和数据样本的脉冲噪声抑制算法[J]. 电工技术学报, 2023, 38(13): 3400-3412.
Chen Zhixiong, Zhang Zhikun, Zhao Xiongwen. An Impulse Noise Suppression Algorithm Based on Dual Interface Diversity and Data Samples. Transactions of China Electrotechnical Society, 2023, 38(13): 3400-3412.
[1] de L Filomeno M, de Campos M L R, Poor H V, et al. Hybrid power line/wireless systems: an optimal power allocation perspective[J]. IEEE Transactions on Wireless Communications, 2020, 19(10): 6289-6300. [2] Liu Huanlin, Zhu Pingxin, Chen Yong, et al.Power allocation for downlink hybrid power line and visible light communication system[J]. IEEE Access, 2020, 8: 24145-24152. [3] 黄彦钦, 余浩, 尹钧毅, 等. 电力物联网数据传输方案:现状与基于5G技术的展望[J]. 电工技术学报, 2021, 36(17): 3581-3593. Huang Yanqin, Yu Hao, Yin Junyi, et al.Data transmission schemes of power Internet of Things: present and outlook based on 5G technology[J]. Transactions of China Electrotechnical Society, 2021, 36(17): 3581-3593. [4] Qian Yuwen, Yan Jiahui, Guan Haibing, et al.Design of hybrid wireless and power line sensor networks with dual-interface relay in IoT[J]. IEEE Internet of Things Journal, 2019, 6(1): 239-249. [5] Chen Zhixiong, Wang Lijiao, Han Dongsheng, et al.A unified performance analysis of relaying communication system for IoT application with hybrid fading[J]. IEEE Internet of Things Journal, 2020, 7(1): 570-583. [6] 蒲红红, 刘晓胜, 韩铭, 等. 电力线通信信道下协作非正交多址接入系统的分布式机会中继选择[J]. 电工技术学报, 2020, 35(11): 2306-2318. Pu Honghong, Liu Xiaosheng, Han Ming, et al.Distributed opportunistic relay selection for cooperative non-orthogonal multiple access power line communication networks[J]. Transactions of China Electrotechnical Society, 2020, 35(11): 2306-2318. [7] 史燕平, 王丽娇, 李秀彩, 等. 电力线和无线双媒质通信系统的自适应中继协议[J]. 电力自动化设备, 2021, 41(6): 171-179. Shi Yanping, Wang Lijiao, Li Xiucai, et al.Adaptive relay protocol for power line-wireless hybrid media communication system[J]. Electric Power Automation Equipment, 2021, 41(6): 171-179. [8] Bai Tong, Zhang Hongming, Wang Jingjing, et al.Fifty years of noise modeling and mitigation in power-line communications[J]. IEEE Communications Surveys & Tutorials, 2021, 23(1): 41-69. [9] Lai S W, Shabehpour N, Messier G G, et al.Performance of wireless/power line media diversity in the office environment[C]//2014 IEEE Global Communications Conference, Austin, TX, USA, 2015: 2972-2976. [10] Elgenedy M, Awadin M M, Hamila R, et al.Sparsity-based joint NBI and impulse noise mitigation in hybrid PLC-wireless transmissions[J]. IEEE Access, 2018, 6: 30280-30295. [11] 邓蓉, 谭周文, 肖郭璇, 等. 基于压缩感知的电力线脉冲噪声抑制[J]. 电工技术学报, 2018, 33(23): 5555-5563. Deng Rong, Tan Zhouwen, Xiao Guoxuan, et al.Impulse noise suppression in power line communication based on compressed sensing[J]. Transactions of China Electrotechnical Society, 2018, 33(23): 5555-5563. [12] 吕新荣, 李有明, 国强. MIMO-OFDM系统的信道与脉冲噪声联合估计方法[J]. 通信学报, 2021, 42(12): 54-64. Lyu Xinrong, Li Youming, Guo Qiang.Joint channel and impulsive noise estimation method for MIMO-OFDM systems[J]. Journal on Communications, 2021, 42(12): 54-64. [13] Gao Shuang, Zheng Jianping.Clipping noise recovery of OTFS signals using reliability-based compressed sensing[J]. IEEE Transactions on Vehicular Technology, 2021, 70(7): 7187-7192. [14] 罗忠涛, 郭人铭, 詹燕梅. 脉冲噪声非线性变换设计的研究综述[J]. 系统工程与电子技术, 2021, 43(7): 1971-1980. Luo Zhongtao, Guo Renming, Zhan Yanmei.Review of nonlinear transformation design for impulsive noise[J]. Systems Engineering and Electronics, 2021, 43(7): 1971-1980. [15] 谭周文, 刘宏立, 成运. 低压电力线通信自适应脉冲噪声抑制[J]. 电力系统及其自动化学报, 2022, 34(1): 129-136. Tan Zhouwen, Liu Hongli, Cheng Yun.Adaptive mitigation of impulsive noise in low-voltage power line communication[J]. Proceedings of the CSU-EPSA, 2022, 34(1): 129-136. [16] Rožić N, Banelli P, Begušić D, et al.Multiple-threshold estimators for impulsive noise suppression in multicarrier communications[J]. IEEE Transactions on Signal Processing, 2018, 66(6): 1619-1633. [17] Zhidkov S V.Analysis and comparison of several simple impulsive noise mitigation schemes for OFDM receivers[J]. IEEE Transactions on Communications, 2008, 56(1): 5-9. [18] Vadali S R K, Ray P, Mula S, et al. Linear detection of a weak signal in additive cauchy noise[J]. IEEE Transactions on Communications, 2017, 65(3): 1061-1076. [19] Kim Y C, Bae J N, Kim J Y.Novel noise reduction scheme for power line communication systems with smart grid applications[C]//2011 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA, 2011: 791-792. [20] 罗忠涛, 詹燕梅, 郭人铭, 等. 脉冲噪声中基于指数函数的可变拖尾非线性变换设计[J]. 电子与信息学报, 2020, 42(4): 932-940. Luo Zhongtao, Zhan Yanmei, Guo Renming, et al.Variable tailing nonlinear transformation design based on exponential function in impulsive noise[J]. Journal of Electronics & Information Technology, 2020, 42(4): 932-940. [21] 张慧, 卢文冰, 赵雄文, 等. 基于最小二乘支持向量机和小波神经网络的电力线通信信道噪声建模研究[J]. 电工技术学报, 2018, 33(16): 3879-3888. Zhang Hui, Lu Wenbing, Zhao Xiongwen, et al.Noise modeling for power line communication channel using the LS-SVM and wavelet neural networks[J]. Transactions of China Electrotechnical Society, 2018, 33(16): 3879-3888. [22] Alam M S, Selim B, Kaddoum G, et al.Mitigation techniques for impulsive noise with memory modeled by a two state Markov-Gaussian process[J]. IEEE Systems Journal, 2020, 14(3): 4079-4088. [23] Ndo G, Siohan P, Hamon M H.Adaptive noise mitigation in impulsive environment: application to power-line communications[J]. IEEE Transactions on Power Delivery, 2010, 25(2): 647-656. [24] 臧海祥, 程礼临, 刘玲, 等. 基于数据驱动的太阳辐射估计和预测研究与展望[J]. 电力系统自动化, 2021, 45(11): 170-183. Zang Haixiang, Cheng Lilin, Liu Ling, et al.Research and prospect for data-driven estimation and prediction of solar radiation[J]. Automation of Electric Power Systems, 2021, 45(11): 170-183. [25] Zheng Jialin, Zhao Zhengming, Shi Bochen, et al.A discrete state event driven simulation based losses analysis for multi-terminal megawatt power electronic transformer[J]. CES Transactions on Electrical Machines and Systems, 2020, 4(4): 275-284. [26] 臧海祥, 郭镜玮, 黄蔓云, 等. 基于深度迁移学习的时变拓扑下电力系统状态估计[J]. 电力系统自动化, 2021, 45(24): 49-56. Zang Haixiang, Guo Jingwei, Huang Manyun, et al.State estimation for power systems with time-varying topology based on deep transfer learning[J]. Automation of Electric Power Systems, 2021, 45(24): 49-56. [27] 陈剑, 杜文娟, 王海风. 采用深度迁移学习定位含直驱风机次同步振荡源机组的方法[J]. 电工技术学报, 2021, 36(1): 179-190. Chen Jian, Du Wenjuan, Wang Haifeng.A method of locating the power system subsynchronous oscillation source unit with grid-connected PMSG using deep transfer learning[J]. Transactions of China Electrotechnical Society, 2021, 36(1): 179-190. [28] She Changyang, Sun Chengjian, Gu Zhouyou, et al.A tutorial on ultrareliable and low-latency communi-cations in 6G: integrating domain knowledge into deep learning[J]. Proceedings of the IEEE, 2021, 109(3): 204-246.
2023, Vol. 38 
