An intelligent impulsive noise mitigation with deep learning method

Abstract

To enable message transmission among sensors and equipment, power line communication (PLC) is a widely adopted smart grid. However, due to the occurrence of impulsive noise (IN), reliable transmissions over PLC channels in the smart grid are challenging. Therefore, in this paper, we propose an adaptive noise mitigation scheme to clip the IN with the sliding window-based method, where the altitude of the received signal in the current time slots is obtained by computing the average altitude of signals in the previous and next time slots. To detect the states of IN and dynamically estimate the power threshold of signals for the IN mitigation scheme, we develop an intelligent algorithm based on the long short-term memory network. To prevent the useful signals from being eliminated as IN signals, we propose the accelerated proximal gradient method (APGM) based on tone reservation to reduce the peak-to-average power ratio (PAPR) for the transmitting signals with low computational complexity. In addition, the closed-form expression of the bit error rate (BER) is derived for the proposed sliding window-based IN mitigation scheme according to the probability density function of the IN. Simulation results demonstrate that the proposed IN mitigation scheme achieves a better BER performance than the conventional IN mitigation schemes. In addition, the APGM aided by IN mitigation can further improve BER performance due to the PAPR reduction.

Keywords

impulsive noise / deep learning / smart grid

References

[1] Lin J, Nassar M, Evans BL. Impulsive noise mitigation in powerline communications using sparse Bayesian learning. IEEE J Selected Areas Commun. 2013;31(7):1172-1183.

[2] Ren G, Qiao S, Zhao H, Li C, Hei Y. Mitigation of periodic impulsive noise in OFDM-based power-line communications. IEEE Trans Power Delivery. 2013;28(2):825-834.

[3] Chien YR. Iterative channel estimation and impulsive noise mitigation algorithm for OFDM-based receivers with application to power-line communications. IEEE Trans Power Delivery. 2015;30(6):2435-2442.

[4] Zhang R, Cheng L, Wang S, et al. Integrated sensing and communication with massive MIMO: a unified tensor approach for channel and target parameter estimation. IEEE Trans Wirel Commun. 2024:1. In press.

[5] Meng H, Guan YL, Chen S. Modeling and analysis of noise effects on broadband power-line communications. IEEE Trans Power Delivery. 2005;20(2):630-637.

[6] Qian Y, Li S, Shi L, et al. Cache-enabled MIMO power line communications with precoding design in smart grid. IEEE Trans Green Commun Netw. 2020;4(1):315-325.

[7] Zhidkov SV. Performance analysis and optimization of OFDM receiver with blanking nonlinearity in impulsive noise environment. IEEE Trans Veh Technol. 2006;55(1):234-242.

[8] Barazideh R, Natarajan B, Nikitin AV, Niknam S. Performance analysis of analog intermittently nonlinear filter in the presence of impulsive noise. IEEE Trans Veh Technol. 2019;68(4):3565-3573.

[9] Juwono FH, Guo Q, Chen Y, Xu L, Huang DD, Wong KP. Linear combining of nonlinear preprocessors for OFDM-based power-line communications. IEEE Trans Smart Grid. 2016;7(1):253-260.

[10] Qian Y, Zhou X, Li J, Shu F, Jayakody DNK. A novel precoding and impulsive noise mitigation scheme for MIMO power line communication systems. IEEE Syst J. 2019;13(1):6-17.

[11] Juwono FH, Guo Q, Huang DD, Chen Y, Xu L, Wong KP. On the performance of blanking nonlinearity in real-valued OFDM-based PLC. IEEE Trans Smart Grid. 2018;9(1):449-457.

[12] Adebisi B, Anoh K, Rabie KM, Ikpehai A, Fernando M, Wells A. A new approach to peak threshold estimation for impulsive noise reduction over power line fading channels. IEEE Syst J. 2019;13(2):1682-1693.

[13] Oh H, Nam H. Design and performance analysis of nonlinearity preprocessors in an impulsive noise environment. IEEE Trans Veh Technol. 2017;66(1):364-376.

[14] Ndo G, Siohan P, Hamon MH. Adaptive noise mitigation in impulsive environment: application to power-line communications. IEEE Trans Power Delivery. 2010;25(2):647-656.

[15] Rabie K, Alsusa E. Improving blanking/clipping based impulsive noise mitigation over powerline channels. In: 2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC); 2013:3413-3417.

[16] Juwono FH, Guo Q, Huang D, Wong KP. Deep clipping for impulsive noise mitigation in OFDM-based power-line communications. IEEE Trans Power Delivery. 2014;29(3):1335-1343.

[17] Adebisi B, Rabie KM, Ikpehai A, Soltanpur C, Wells A. Vector OFDM transmission over non-Gaussian power line communication channels. IEEE Syst J. 2018;12(3):2344-2352.

[18] Nguyen TK, Nguyen HH, Eric Salt J, Howlett C. Optimization of partial transmit sequences for PAPR reduction of OFDM signals without side information. IEEE Trans Broadcast. 2023;69(1):313-321.

[19] Boche H, Monich UJ. Peak-to-average power control via tone reservation in general orthonormal transmission systems. IEEE Trans Signal Process. 2018;66(13):3520-3528.

[20] El Hassan M, Crussiere M, Helard JF, Nasser Y, Bazzi O. EVM closed-form expression for OFDM signals with tone reservation-based PAPR reduction. IEEE Trans Wirel Commun. 2020;19(4):2352-2366.

[21] Koma R, Fujiwara M, Kimura S, Yoshimoto N. Wide dynamic range reception of TDM-based DCO-OFDM signals using optical domain power equalization and symmetrical clipping techniques. J Light Technol. 2015;33(8):1623-1629.

[22] Rabie KM, Alsusa E. On enhancing the performance of the DPTE-based noise cancellation method utilizing the PTS PAPR reduction scheme in PLC systems. In: 2014 International Symposium on Power Line Communications and its Applications (ISPLC); 2014:1-6.

[23] Rabie KM, Alsusa E. Improved DPTE technique for impulsive noise mitigation over power-line communication channels. AEU—Int J Electron Commun. 2015;69(12):1847-1853.

[24] Qian Y, Zhou X, Li J, Zhang F, Shi L, Shu F. Design and performance analysis of power line communication networks under impulsive noise in smart home. IEEE Access. 2018;6:71368-71377.

[25] Dubey A, Mallik RK, Schober R. Performance analysis of a power line communication system employing selection combining in correlated log-normal channels and impulsive noise. IET Commun. 2014;8(7):1072-1082.

[26] Liong A, Juwono F, Gopal L, Chiong C, Rong Y. Multiple blanking preprocessors for impulsive noise mitigation in OFDM-based power-line communication systems. Int J Electr Power Energy Syst. 2021;130:106911.

[27] Ma J, Liu H, Peng C, Qiu T. Unauthorized broadcasting identification: a deep LSTM recurrent learning approach. IEEE Trans Instrum Meas. 2020;69(9):5981-5983.

[28] Khalil K, Eldash O, Kumar A, Bayoumi M. Economic LSTM approach for recurrent neural networks. IEEE Trans Circuits Syst II: Express Briefs. 2019;66(11):1885-1889.

[29] Zheng H, Lin F, Feng X, Chen Y. A hybrid deep learning model with attention-based conv-LSTM networks for short-term traffic flow prediction. IEEE Trans Intell Transport Syst. 2021;22(11):6910-6920.

[30] Lin CH, Lin YC, Tang PW. ADMM-ADAM: a new inverse imaging framework blending the advantages of convex optimization and deep learning. IEEE Trans Geosci Remote Sensing. 2022;60:1-16.

[31] Zadeh SG, Schmid M. Bias in cross-entropy-based training of deep survival networks. IEEE Trans Pattern Anal Mach Intell. 2021;43(9):3126-3137.

[32] Li J, Shao Y, Wei K, et al. Blockchain assisted decentralized federated learning (BLADE-FL): performance analysis and resource allocation. IEEE Trans Parallel Distributed Syst. 2022;33(10):2401-2415.

[33] Banelli P. Theoretical analysis and performance of OFDM signals in nonlinear fading channels. IEEE Trans Wirel Commun. 2003;2(2):284-293.

[34] Hua L, Wang Y, Lian Z, Su Y, Xie Z. Low-complexity PAPR-aware precoding for massive MIMO-OFDM downlink systems. IEEE Wirel Commun Lett. 2022;11(7):1339-1343.

[35] Jiang X, Zeng X, Sun J, Chen J. Distributed proximal gradient algorithm for nonconvex optimization over time-varying networks. IEEE Trans Control Network Syst. 2023;10(2):1005-1017.

[36] Wang Y, Xie S, Xie Z. FISTA-based PAPR reduction method for tone reservation’s OFDM system. IEEE Wirel Commun Lett. 2018;7(3):300-303.

[37] Yan M, Feng G, Zhou J, Sun Y, Liang YC. Intelligent resource scheduling for 5G radio access network slicing. IEEE Trans Veh Technol. 2019;68(8):7691-7703.

[38] Wang Y, Chen W, Tellambura C. Genetic algorithm based nearly optimal peak reduction tone set selection for adaptive amplitude clipping PAPR reduction. IEEE Trans Broadcast. 2012;58(3):462-471.

[39] Luqing Wang W, Tellambura C. Analysis of clipping noise and tone-reservation algorithms for peak reduction in OFDM systems. IEEE Trans Veh Technol. 2008;57(3):1675-1694.

[40] Marcum J. A statistical theory of target detection by pulsed radar. IEEE Trans Inf Theory. 1960;6(2):59-267.

RIGHTS & PERMISSIONS

2024 The Author(s). International Journal of Mechanical System Dynamics published by John Wiley & Sons Australia, Ltd on behalf of Nanjing University of Science and Technology.