基于自适应尺度变换-双稳态随机共振模型的BPSK信号检测算法

doi:10.12305/j.issn.1001-506X.2022.07.03

Abstract

Abstract:

In order to improve the detection performance of binary phase shift keying (BPSK) signals under the background of strong noise, with the signal of the mainstream methods weakened to a certain extent in the process of noise suppression and the signal processing system introducing new noises, which leads to the decline of detection performance, a BPSK signal detection algorithm based on adaptive scale transformation of bistable stochastic resonance model is proposed. The classic bistable stochastic resonance system can only handle small amplitude and low frequency periodic signals, In view of this, we first carry ont bistable stochastic resonance system scale transformation, proving that under the condition of high sampling rate, bistable stochastic resonance system can be applied to the high frequency of BPSK signal. Based on Neyman-Pearson criterion, a nonlinear threshold detection system is designed, and we quantitatively indicate the error rate of detector as a feedback, adaptively adjust system parameters, and construct the complete process of signal detection. The feasibility of scale transformation and the applicability of the proposed algorithm are verified by simulation experiments, which provides a theoretical basis for weak BPSK signal detection under the condition of low signal to noise ratio.

Key words: strong noise, no prior information, binary phase shift keying (BPSK) signal, bistable stochastic resonance system, scale transformation, Neyman-Pearson criterion

CLC Number:

TN957.52

Bin LIU, Xiangyu FAN, Ziwei ZHANG, Ye ZHANG. Detection algorithm of BPSK signal based on adaptive scale change-bistable stochastic resonance model[J]. Systems Engineering and Electronics, 2022, 44(7): 2084-2095.

Figures/Tables 15

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References 32

1	EN 302 755 V1.1.1. Frame structure channel coding and modulation for a second generation digital terrestrial television broadcasting system (DVB-T2)[S]. Nice: ESTI, 2009.
2	LI Q , LIU Y , PADMARAJU K , et al. A 10-Gb/s silicon microring resonator-based BPSK link[J]. IEEE Photonics Technology Letters, 2014, 26 (18): 1805- 1808. doi: 10.1109/LPT.2014.2336091
3	BATDALAI S , HIROKI K , NOBUO G . All-optical modulation format conversion from QPSK to symbol rate doubled BPSK using FWM and pulse width compression[J]. Journal of Lightwave Technology, 2017, 35 (19): 4219- 4228. doi: 10.1109/JLT.2017.2736001
4	HIROKI K , NOBUO G , LAWRENCE R C . All-optical wavelength preserved modulation format conversion from PDM-QPSK to PDM-BPSK using FWM and interference[J]. Journal of Lightwave Technology, 2016, 34 (23): 5505- 5515. doi: 10.1109/JLT.2016.2620170
5	ZHI T , STOJAN R . Low-noise optical amplification and signal processing in parametric devices[J]. Advances in Optics Photonics, 2013, 5 (3): 318- 384. doi: 10.1364/AOP.5.000318
6	RADAN S , FRANCESCA P , JOSEPH K , et al. All-optical phase and amplitude regenerator for next-generation telecommunications systems[J]. Nature Photonics, 2010, 4 (10): 690- 695. doi: 10.1038/nphoton.2010.203
7	FRANCESCO D R , DRAGANA V , KJELD D . Phase regeneration of DPSK signals in a silicon waveguide with reverse-biased p-i-n junction[J]. Optics Express, 2014, 22 (5): 5029- 5036. doi: 10.1364/OE.22.005029
8	HANS C H M , FRANCESCO D R , MICHAEL G , et al. Phase regeneration of a BPSK data signal using a lithium niobate phase modulator[J]. Journal of Lightwave Technology, 2015, 33 (11): 2189- 2198. doi: 10.1109/JLT.2015.2408568
9	XIE L F , HO I W H , LIEW S C , et al. The feasibility of mobile physical-layer network coding with BPSK modulation[J]. IEEE Trans.on Vehicular Technology, 2017, 66 (5): 3976- 3990.
10	ZHOU Y , ZHANG H X , YUAN D F . Differential spatial modulation with BPSK for two-way relay wireless communications[J]. IEEE Communications Letters, 2017, 21 (6): 1361- 1364. doi: 10.1109/LCOMM.2017.2678466
11	FAOUZI B , ACHREF M , SOFIENE A . Closed-form CRLBs for SNR estimation from turbo-coded BPSK-, MSK-, and square-QAM-modulated signals[J]. IEEE Trans.on Signal Processing,, 2014, 62 (15): 4018- 4033. doi: 10.1109/TSP.2014.2328328
12	KAI T S , HUANG Y H . SNR estimation based on metric normalization frequency in Viterbi decoder[J]. IEEE Communications Letters, 2011, 15 (6): 668- 670. doi: 10.1109/LCOMM.2011.040711.110028
13	AMIT K D , HARI K V S , HANZO L . Channel estimation relying on the minimum bit-error-ratio criterion for BPSK and QPSK signals[J]. IET Communications, 2014, 8 (1): 69- 76. doi: 10.1049/iet-com.2013.0173
14	ZHENG D , JULIAN C , NORMAN B . BER analysis of BPSK signals in Ricean-faded cochannel interference[J]. IEEE Trans.on Communications, 2007, 66 (10): 1994- 2001.
15	IMANI S , GHORASHI S A , BOLHASANI M . SINR maximization in collocated MIMO radars using transmit covariance matrix[J]. Signal Processing, 2016, 11 (9): 128- 135.
16	FLORIAN E , CHEVREUIL A , LOUBATON P . Blind source separation of convolutive mixtures of non-circular linearly modulated signals with unknown baud rates[J]. Signal Processing, 2012, 9 (2): 715- 726.
17	HUANG M , HUANG L , SUN W Z , et al. Accurate signal detection for BPSK-OFDM systems in time-varying channels[J]. Digital Signal Processing, 2017, 60 (3): 370- 379.
18	AHMED E-M . Multiple tone interference of multicarrier frequency-hopping BPSK system for a Rayleigh fading channel with channel estimation errors[J]. Digital Signal Processing, 2010, 20 (8): 869- 880.
19	CHEE Y M , AHMAD Z S . Adaptive windowed cross Wigner-Ville distribution as an optimum phase estimator for PSK signals[J]. Digital Signal Processing, 2013, 2 (3): 289- 301.
20	CARSTEN B . Iterative soft Interference cancellation for sparse BPSK signals[J]. IEEE Communications Letters, 2015, 19 (5): 855- 858. doi: 10.1109/LCOMM.2015.2408594
21	TANG S H , YU Y . Fast algorithm for symbol rate estimation[J]. IEICE Trans.on Communications, 2005, 88 (4): 1649- 1652.
22	CHEN H , VARSHNEY P K , KAY S M , et al. Michels, theory of the stochastic resonance effect in signal detection-part I: fixed detectors[J]. IEEE Trans.on Signal Processing, 2007, 55 (7): 3172- 3184. doi: 10.1109/TSP.2007.893757
23	CHEN H , VARSHNEY P K , KAY S M , et al. Theory of the stochastic resonance effect in signal detection-part Ⅱ: variable detectors[J]. IEEE Trans.on Signal Processing, 2008, 56 (10): 5031- 5041. doi: 10.1109/TSP.2008.928509
24	LIU X L , LIU H G , YANG J H , et al. Improving the bearing fault diagnosis efficiency by the adaptive stochastic resonance in a new nonlinear system[J]. Mechanical Systems and Signal Processing, 2017, 96, 58- 76. doi: 10.1016/j.ymssp.2017.04.006
25	WANG J , REN X , ZHANG S W , et al. Adaptive bistable stochastic resonance aided spectrum sensing[J]. IEEE Trans.on Wireless Communications, 2014, 13 (7): 4014- 4024. doi: 10.1109/TWC.2014.2317779
26	QIU Z Y , WANG P , ZHU J . Estimation of both Nyquist zone index and code rate for BPSK radar signal intercepted by Nyquist folding receiver[J]. IET Radar, Sonar & Navigation, 2017, 11 (6): 1652- 1663.
27	CHEE H P , KWANG S H , SANG W N . Biased SNR estimation using pilot and data symbols in BPSK and QPSK systems[J]. Journal of Communications and Networks, 2014, 16 (6): 583- 591. doi: 10.1109/JCN.2014.000104
28	LIN L F , YU L , WANG H Q . Parameter-adjusted stochastic resonance system for the aperiodic echo chirp signal in optimal FrFT domain[J]. Communications in Nonlinear Science and Numerical Simulation, 2017, 43 (7): 171- 181.
29	CHEN H , VARSHNEY P K , KAY S M . Theory of the stochastic resonance effect in signal detection-part I: fixed detectors[J]. IEEE Trans.on Signal Processing, 2007, 55 (7): 3172- 3184. doi: 10.1109/TSP.2007.893757
30	CHEN H , VARSHNEY P K , KAY S M . Theory of the stochastic resonance effect in signal detection-part Ⅱ: variable detectors[J]. IEEE Trans.on Signal Processing, 2008, 56 (10): 5031- 5041. doi: 10.1109/TSP.2008.928509
31	SADJAD I , SEYED A G , MOSTAFA B . SINR maximization in colocated MIMO radars using transmit covariance matrix[J]. Signal Processing, 2016, 11 (9): 128- 135.
32	LIU J , LI Z . Lowering the signal-to-noise ratio wall for energy detection using parameter-induced stochastic resonator[J]. IET Communications, 2015, 9 (1): 101- 107. doi: 10.1049/iet-com.2014.0511

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Detection algorithm of BPSK signal based on adaptive scale change-bistable stochastic resonance model

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