On cyclostationary analysis of WiFi signals for direction estimation

Du, Changlai; Zeng, Huacheng; Lou, Wenjing; Hou, Yang

doi:10.1109/icc.2015.7248876

Cited by 14 publications

(4 citation statements)

References 11 publications

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“…reduces the spatial domain, thus minimizing the feature dimensionality by maintaining the spatial features. In our model, max-pooling layers are used as computed by equation (13), where the input is x, the window size is p, and s denotes the stride value.…”

Section: Spectrum Monitoring Using Spectrumnetmentioning

confidence: 99%

SPECTRUMNET: Cooperative Spectrum Monitoring Using Deep Neural Networks

Suriya

G²

2022

International Journal of Antennas and Propagation

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Spectrum monitoring is one of the significant tasks required during the spectrum sharing process in cognitive radio networks (CRNs). Although spectrum monitoring is widely used to monitor the usage of allocated spectrum resources, this work focuses on detecting a primary user (PU) in the presence of secondary user (SU) signals. For signal classification, existing methods, including cooperative, noncooperative, and neural network-based models, are frequently used, but they are still inconsistent because they lack sensitivity and accuracy. A deep neural network model for intelligent wireless signal identification to perform spectrum monitoring is proposed to perform efficient sensing at low SNR (signal to noise ratio) and preserve hyperspectral image features. A hybrid deep learning model called SPECTRUMNET (spectrum sensing using deep neural network) is presented. It can quickly and accurately monitor the spectrum from spectrogram images by utilizing cyclostationary features and convolutional neural networks (CNN). The class imbalance issue is solved by uniformly spreading the samples throughout the classes using the oversampling method known as SMOTE (Synthetic Minority Oversampling Technique). The proposed model achieves a classification accuracy of 94.46% at a low SNR of −15 dB, which is an improvement over existing CNN models with minor trainable parameters.

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Section: Spectrum Monitoring Using Spectrumnetmentioning

confidence: 99%

SPECTRUMNET: Cooperative Spectrum Monitoring Using Deep Neural Networks

Suriya

G²

2022

International Journal of Antennas and Propagation

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“…A selection of hardware prototypes were built for reception of the arbitrary-phase HOPS spread spectrum waveform [16] and implemented on an Intel Arria 10 SoC FPGA, including: (1) a brute-force 3 matched-filter model, (2) a 1-bit truncated coefficients model, (3) a truncated coefficients model with λ = √ 2/2 pruning, and (4) a truncated pruned coefficients model with 4× folded correlation taps. The HOPS signals are 3 The hardware prototype HOPS system employs an 8 symbol preamble and 175 chip spread ratio. Since the Arria 10 FPGA is limited to 3374 multipliers, the 3 • 8 • 175 = 4200 multiply operations required by the brute-force design are clocked at a higher rate to fit on the device.…”

Section: Hardware Prototype Validationmentioning

confidence: 99%

“…The design of burst-mode communication systems presents additional challenges over that of a standard continuous data link, in particular due to the need to re-acquire the signal on a burst-by-burst basis. In low-power devices, such as those suitable for Internet of Things (IoT), burst-mode waveforms traditionally employ techniques to make the acquisition preamble as easy to receive as possible, typically by embedding pilot tones [1], repeated cyclic prefixes [2], cyclic autocorrelation functions [3], soft-handoff between spreading codes [4], Barker-sequence / short preamble repetition [5], maximal-likelihood estimation [6], and/or variations of matched-filter techniques [7], [8]. Virtually all of these approaches rely on an inherent cyclostationary signal feature of the preamble bursts, facilitating blind detection and/or exploitation by an unintended receiver.…”

Section: Introductionmentioning

confidence: 99%

Fallthrough Correlation Techniques for Arbitrary-Phase Spread Spectrum Waveforms

2019

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The use of practically non-repeating spreading codes to generate sequence-based spread spectrum waveforms is a strong method to improve transmission security, by limiting an observer's opportunity to cross-correlate snapshots of the signal into a coherent gain. Such time-varying codes, particularly when used to define multi-bit resolution arbitrary-phase waveforms, present significant challenges to the intended receiver, who must synchronize acquisition processing to match the time-varying code each time it changes. This paper presents a series of options for optimizing the traditional brute-force matchedfilter preamble correlator for burst-mode arbitrary-phase spread spectrum signals, achieving significant computational gains and flexibility, backed by measurable results from hardware prototypes built on an Intel Arria 10 Field Programmable Gate Array (FPGA). The most promising of which requires no embedded multipliers and reduces the total hardware logic by more than 76%. Extensions of the core fallthrough correlator techniques are considered to support low-power asynchronous reception, underlay-based physical layer firewall functions, and Receiver-Assigned Code Division Multiple Access (RA-CDMA) protocols in Internet of Things (IoT)-caliber devices.

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“…Including this property in signal processing algorithm design can improve the performance of existing algorithms, especially the DOA estimation algorithms. Several algorithms have been proposed in the literature along this line [5][6][7]. Instead of using the correlation matrix as being done in conventional methods, these cyclostationarity-based algorithms require estimating the cyclic correlation (CCO) matrix to reflect the cyclostationarity of incoming signals which can be one of the following three cases: (1) having baud rates or (2) being modulated by a carrier signal in the way that they are used in radar and radio communication applications or (3) both.…”

Section: Introductionmentioning

confidence: 99%

Robust Cyclic MUSIC Algorithm for Finding Directions in Impulsive Noise Environment

Chen

2017

International Journal of Antennas and Propagation

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This paper addresses the issue of direction finding of a cyclostationary signal under impulsive noise environments modeled byα-stable distribution. Sinceα-stable distribution does not have finite second-order statistics, the conventional cyclic correlation-based signal-selective direction finding algorithms do not work effectively. To resolve this problem, we define two robust cyclic correlation functions which are derived from robust statistics property of the correntropy and the nonlinear transformation, respectively. The MUSIC algorithm with the robust cyclic correlation matrix of the received signals of arrays is then used to estimate the direction of cyclostationary signal in the presence of impulsive noise. The computer simulation results demonstrate that the two proposed robust cyclic correlation-based algorithms outperform the conventional cyclic correlation and the fractional lower order cyclic correlation based methods.

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On cyclostationary analysis of WiFi signals for direction estimation

Cited by 14 publications

References 11 publications

SPECTRUMNET: Cooperative Spectrum Monitoring Using Deep Neural Networks

SPECTRUMNET: Cooperative Spectrum Monitoring Using Deep Neural Networks

Fallthrough Correlation Techniques for Arbitrary-Phase Spread Spectrum Waveforms

Robust Cyclic MUSIC Algorithm for Finding Directions in Impulsive Noise Environment

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