Journal of Jilin University(Engineering and Technology Edition) ›› 2019, Vol. 49 ›› Issue (5): 1696-1705.doi: 10.13229/j.cnki.jdxbgxb20180446

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SLF channel noise suppression method based on adaptive blanking in local variance domain

Peng ZHAO1(),Yu-zhong JIANG1(),Bin CHEN1,Chun-teng LI1,Yang-yong ZHANG2   

  1. 1. College of Electronic Engineering，Naval University of Engineering, Wuhan 430033, China
    2. No. 722 Research Institute of CSIC, Wuhan 430079, China
  • Received:2018-05-07 Online:2019-09-01 Published:2019-09-11
  • Contact: Yu-zhong JIANG E-mail:zhaopeng@cug.edu.cn;jiangyuzhong@tsinghua.org.cn

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Abstract:

The Super Low Frequency (SLF, 3~300 Hz) Channel Noise (CN) impulses are usually smeared by the transient effects in the receivers’ front-end stages, which will make the Blanking Nonlinearity (BNL) lose effectiveness. To solve this problem, based on the analysis of the waveform characteristics of transient response of impulses and the mechanism of performance reduction of the BNL, in conjunction with the consideration that the Local Variance Domain Transforming (LVDT) is capable to intensify the impulsiveness, an adaptive BNL based on LVDT is proposed. The Detection Threshold (DT) for the CN impulses is formulated based on the Constant False Alarm Rate (CFAR) principle and then the DT optimizing principle for the BNL is given. Simulations and real tests show that the proposed method outperforms other common NLs in terms of SLF CN impulse suppression. Since such method needs not to assume the CN model or estimate its parameter, thus is a blind suppression method, therefore, it is more practical.

Key words: communication and information system, channel noise, transient effects of impulse, local variance, constant false alarm rate

CLC Number: 

  • TN85

Fig.1

Transient effects of impulses and local variance"

Fig.2

Dependence of ε(N,S) on N in terms of ASWNRs"

Fig.3

Dependence of lower bound of N for ε(N,S)≤10% on fc in terms of ASWNRs"

Fig.4

Dependence of FAR on ASWNR"

Fig.5

BER performance comparisons in various CN conditions"

Fig.6

BER performance comparisons for various N values"

Fig.7

Real SLF signal and suppression result"

Table 1

Performance comparison for real test"

参 数本文算法LOTNLFCP-BNL
N=8N=64N=128N=1024
SCNRG/dB7.4210.129.988.969.926.08
BERI/%304847434720
1 EvansJ， GriffithsA S． Design of a sanguine noise processor based upon world-wide extremely low frequency (ELF) recordings[J]. IEEE Transactions on Communications, 1974, 22(4): 528-539.
2 IngramR． Performance of the locally optimum threshold receiver and several suboptimal nonlinear receivers for ELF noise[J]. IEEE Journal of Oceanic Engineering, 1984, 9(3): 202-208.
3 MiddletonD. Statistical-physical models of electromagnetic interference[J]. IEEE Transactions on Electromagnetic Compatibility, 1977, 19(3): 106-127.
4 蒋宇中， 应文威， 张曙霞， 等. 超低频非高斯噪声模型及应用[M]. 北京: 国防工业出版社， 2014.
5 OhH， NamH， ParkS． Adaptive threshold blanker in an impulsive noise environment[J]. IEEE Transactions on Electromagnetic Compatibility, 2014, 56(5): 1045-1052.
6 OhH， NamH． Design and performance analysis of nonlinearity preprocessors in an impulsive noise environment[J]. IEEE Transactions on Vehicular Technology, 2017, 66(1): 364-376.
7 应文威, 欧勇恒, 蒋宇中, 等. 新型自适应非高斯接收机设计[J]. 吉林大学学报: 工学版, 2013, 43(6): 1685-1689.
YingWen-wei, Yong-hengOu, JiangYu-zhong, et al. New adaptive receiver for channels with non-gaussian noise[J]. Journal of Jilin University (Engineering and Technology Edition), 2013, 43(6): 1685-1689.
8 SaaifanK A, HenkelW. Decision boundary evaluation of optimum and suboptimum detectors in class-a interference[J]. IEEE Transactions on Communications, 2013, 61(1): 197-205.
9 AlsusaE, RabieK M. Dynamic peak-based threshold estimation method for mitigating impulsive noise in power-line communication systems[J]. IEEE Transactions on Power Delivery, 2013, 28(4): 2201-2208.
10 EppleU, SchnellM. Advanced blanking nonlinearity for mitigating impulsive interference in OFDM systems[J]. IEEE Transactions on Vehicular Technology, 2017, 66(1): 146-158.
11 BernsteinS L, BurrowsM L, EvansJ E, et al. Long-range communications at extremely low frequencies[J]. Proceedings of the IEEE, 1974, 62(3): 292-312.
12 VartiainenJ， LehtomakiJ， SaarnisaariH, et al. Interference suppression in several transform domains[C]∥IEEE Military Communications Conference, Atlantic City, NJ, USA, 2005: 2294-2300.
13 AromaaS, HenttuP, JunttiM. Transform-selective interference suppression algorithm for spread-spectrum communications[J]. IEEE Signal Processing Letters, 2005, 12(1): 49-51.
14 WangS, AnJ P, WangA H, et al. A minimum value based threshold setting strategy for frequency domain interference excision[J]. IEEE Signal Processing Letters, 2010, 17(5): 501-504.
15 JiaQ， LiB， MaS, et al． Local variance detection for multi-antenna spectrum sensing[J]. IEEE Communications Letters, 2015, 19(12): 2142-2145.
16 SaarnisaariH， HenttuP， JunttiM． Iterative multidimensional impulse detectors for communications based on the classical diagnostic methods[J]. IEEE Transactions on Communications, 2005, 53(3): 395-398.
17 ChenY． Improved energy detector for random signals in gaussian noise[J]. IEEE Transactions on Wireless Communications, 2010, 9(2): 558-563.
18 AndrásS， BariczA， SunY． The generalized Marcum Q-Function: an orthogonal polynomial approach[J]. Acta Universitatis Sapientiae Mathematica, 2011, 3(1): 60-76.
19 RuderS． An overview of gradient descent optimization algorithms[J/OL]. [2016-05-12]. https:∥
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