A DSSS Signal Detection Method Based on Wavelet Decomposition and Delay Multiplication

Zhang, Xiaolin; Li, Sijia; Chen, Zengmao

doi:10.1109/apcap.2018.8538308

Cited by 4 publications

(5 citation statements)

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“…The MN, which has no any prior knowledge about the embedding parameters of the SW, can only detect the SW by blind detection methods. These existing blind detection methods of direct sequence spread spectrum signals include autocorrelation detection [14], delay multiplication detection [15], cepstrum detection [16], cyclic cepstrum detection [17], etc. In practice, the autocorrelation detection has been widely used due to its simple implementation and the better performance in the low SNR.…”

Section: ) Transmission Performance Of the Swmentioning

confidence: 99%

“…Similar to the proof shown in [19], the iterative setups in (15) is proved to converge to the Karush-Kuhn-Tucker solution of (10), for which the details are omitted here. We provide the detailed procedures for solving (15), as shown in algorithm 1. Algorithm 1 D.C Approach for Solving (15) and Optimal G Set: k = 0.…”

Section: ) Rate-adjustable Transmission At the Snmentioning

confidence: 99%

“…We provide the detailed procedures for solving (15), as shown in algorithm 1. Algorithm 1 D.C Approach for Solving (15) and Optimal G Set: k = 0. Repeat: set k = k + 1, G = 2 k − 1.…”

Section: ) Rate-adjustable Transmission At the Snmentioning

confidence: 99%

“…Use CVX to find P (j+1) , which is the solution of the problem shown in (15). Compute: M (j+1) = (P (j+1) ) − (P (j+1) ).…”

Section: Repeatmentioning

confidence: 99%

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Secure Transmission Method of Spectrum Watermark for Fine-Grained Spectrum Management

et al. 2020

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Fine-grained spectrum management is promising in addressing the contradiction between the access requirements for massive radio systems and the shortage of spectrum resources. It is difficult to identify the radio system and securely transmit its spectrum identity information due to the complexity, openness, and variability of the electromagnetic environment, which has become a major challenge for the fine-grained spectrum management. In this paper, we consider a practical and proactive spectrum monitoring scenario, where the spectrum monitoring node monitors the communication signal sent by a source node to a destination node for the fine-grained spectrum management. A malicious node eavesdrops on the transmission for accessing and deceiving them. We propose a secure transmission method of spectrum watermark for the fine-grained spectrum management. The spectrum watermark for identifying the radio system is hidden in the normal communication signal via adjusting the embedding parameters, and has no effect on the required transmission performance. We investigate the transmission performance of the spectrum watermark in two typical use cases. In first case, the transmission rate of the communication signal is given and fixed. In another case, the transmission rate of the communication signal can be adaptively adjusted according to the channel quality. The analysis and simulation results show that the proposed method achieves the optimal and secure transmission of the spectrum watermark by adjusting the embedding parameters of the spectrum watermark while ensuring the required and optimal transmission performance of the communication signal. INDEX TERMS Fine-grained spectrum management, spectrum watermark, embedding parameters of spectrum watermark, secure transmission. I. INTRODUCTION With the rapid development and broad application of wireless communication technology, the number of mobile users and their requirements for bandwidth are growing rapidly. According to Cisco's 2019 visual network index, global mobile data traffic would grow 7-fold from 2017 to 2022 [1]. The shortage of spectrum resources has become a bottleneck The associate editor coordinating the review of this manuscript and approving it for publication was Ruofei Ma. restricting the sustainable development and application of wireless communication technology. Fine-grained spectrum management, which can improve the effectiveness of spectrum management, is regarded as one of the most promising methods to solve the contradiction between the increasing spectrum demand and the shortage of spectrum resources. Radio spectrum identification is critical to achieve the fine-grained spectrum management. Due to the openness of the electromagnetic environment and the boundless of wireless transmission, the spectrum identification information of

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Section: ) Transmission Performance Of the Swmentioning

confidence: 99%

Section: ) Rate-adjustable Transmission At the Snmentioning

confidence: 99%

“…We provide the detailed procedures for solving (15), as shown in algorithm 1. Algorithm 1 D.C Approach for Solving (15) and Optimal G Set: k = 0. Repeat: set k = k + 1, G = 2 k − 1.…”

Section: ) Rate-adjustable Transmission At the Snmentioning

confidence: 99%

“…Use CVX to find P (j+1) , which is the solution of the problem shown in (15). Compute: M (j+1) = (P (j+1) ) − (P (j+1) ).…”

Section: Repeatmentioning

confidence: 99%

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Secure Transmission Method of Spectrum Watermark for Fine-Grained Spectrum Management

et al. 2020

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“…It mainly improves the detection performance of the direct spread spectrum signal by suppressing the influence of the main peak value on the threshold value, thereby reducing the influence of other signals on blind detection of the direct spread spectrum signal. Based on previous research, Zhang et al proposed a detection method combining wavelet decomposition and delayed correlation to address the problem of discontinuous and unstable correlation peaks in DSSS signals under low signal-to-noise ratio conditions [ 10 ]. The noise of the signal is reduced through wavelet decomposition, making the correlation peaks of the DSSS signal more obvious and easier to detect, effectively improving the detection performance of the direct spread spectrum signal in low signal-to-noise ratio situations.…”

Section: Introductionmentioning

confidence: 99%

DSSS Signal Detection Based on CNN

Liu

et al. 2023

Sensors

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With the wide application of direct sequence spread spectrum (DSSS) signals, the comprehensive performance of DSSS communication systems has been continuously improved, making the electronic reconnaissance link in communication countermeasures more difficult. Electronic reconnaissance technology, as the fundamental means of modern electronic warfare, mainly includes signal detection, recognition, and parameter estimation. At present, research on DSSS detection algorithms is mostly based on the correlation characteristics of DSSS signals, and autocorrelation algorithm is the most mature and widely used method in practical engineering. With the continuous development of deep learning, deep-learning-based methods have gradually been introduced to replace traditional algorithms in the field of signal processing. This paper proposes a spread spectrum signal detection method based on convolutional neural network (CNN). Through experimental analysis, the detection performance of the CNN model proposed in this paper on DSSS signals in various situations has been compared and analyzed with traditional autocorrelation detection methods for different signal-to-noise ratios. The experiments verified the estimation performance of the model in this paper under different signal-to-noise ratios, different spreading code lengths, different spreading code types, and different modulation methods and compared it with the autocorrelation detection algorithm. It was found that the detection performance of the model in this paper was higher than that of the autocorrelation detection method, and the overall performance was improved by 4 dB.

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Competitive Feature Extraction for Activity Recognition based on Wavelet Transforms and Adaptive Pooling

Abdu-Aguye

Gomaa

2019

2019 International Joint Conference on Neural Networks (IJCNN)

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A DSSS Signal Detection Method Based on Wavelet Decomposition and Delay Multiplication

Cited by 4 publications

References 4 publications

Secure Transmission Method of Spectrum Watermark for Fine-Grained Spectrum Management

Secure Transmission Method of Spectrum Watermark for Fine-Grained Spectrum Management

DSSS Signal Detection Based on CNN

Competitive Feature Extraction for Activity Recognition based on Wavelet Transforms and Adaptive Pooling

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