Higher order statistics for modulation and STBC recognition in MIMO systems

Khosraviyani, Mokhtar; Kalbkhani, Hashem; Shayesteh, Mahrokh G.

doi:10.1049/iet-com.2018.5497

Cited by 6 publications

(11 citation statements)

References 42 publications

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“…Traditional algorithms (TA) use the statistical features of different time delays and establish decision trees for STBC recognition, and realize good performance at low signal-tonoise ratio (SNR) condition [25]. However, traditional algorithms need to manually extract features and set the threshold of hypothesis testing, which means the recognition performance is greatly influenced by artificial parameters.…”

Section: Multi-delay Feature Extractionmentioning

confidence: 99%

“…In STBC recognition field, FB approaches recognize STBC by utilizing high-quality features, and most methods tend to emphasize a series of identifying statistics, e.g., second-order cyclostationarity [17], [18] and higher-order statistics [19]- [25]. However, very few methods attempt to account for features fusion, which may, if considered, realize complementary strengths of different characteristics.…”

Section: Proposed Cnn Recognition Model With Features Fusionmentioning

confidence: 99%

“…Most former studies in STBC recognition in MIMO systems have concentrated on traditional algorithms, which can be broadly categorized as two types: likelihood-based (LB) [10]- [12] and feature-based (FB) methods [13]- [25]. LB methods use the maximum likelihood criterion to determine the best results, but their computational complexity is too high for higher-order modulation, and thus they require a priori information, i.e., channel coefficients, modulation type, carrier frequency offset, and carrier phase [10]- [12].…”

Section: Introductionmentioning

confidence: 99%

“…Thus, meticulously designed features can provide significant performance improvements. Historically, various types of methods, such as second-order statistics [13]- [16], cyclostationarity-based statistics [17], [18], and higher-order statistics [19]- [25] have been studied. Among these, two algorithms, which both based on fourth-order statistics, has achieved excellent recognition performance at high signal-tonoise ratios (SNRs) [23], [25].…”

Section: Introductionmentioning

confidence: 99%

“…Historically, various types of methods, such as second-order statistics [13]- [16], cyclostationarity-based statistics [17], [18], and higher-order statistics [19]- [25] have been studied. Among these, two algorithms, which both based on fourth-order statistics, has achieved excellent recognition performance at high signal-tonoise ratios (SNRs) [23], [25]. Nevertheless, the performance of FB methods is mainly based on the hand-crafted features designed by experts, which means that these methods need to readjust the parameters after changing the channel and have poor adaptability to low SNR environment.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Automatic Space-Time Block Code Recognition Using Convolutional Neural Network With Multi-Delay Features Fusion

Zhang¹,

Yan²,

Zhang³

et al. 2021

IEEE Access

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Automatic signal recognition (ASR) is becoming increasingly important in spectrum identification and cognitive radio, but most existing space-time block code (STBC) recognition algorithms are traditional ones and do not account for the complementarities between different features. To overcome these deficiencies, a multi-delay features fusion scheme for ASR of STBC using a convolutional neural network (CNN) is proposed in this study. The proposed scheme tries to fuse different time-delay features of received STBC signals to obtain more discriminating features. The dilated convolution of different dilation rates is applied to realize automated multi-delay feature extraction. Then, two fusion methods, i.e., maxcorrelation (MC) fusion and multi-delay average (MDA) fusion, are proposed to combine the features of different time delays, and a residual block is applied to achieve better representation of signals. Finally, the simulation results demonstrate the superior performance of the proposed method. It is notable that the recognition accuracy can reach 97.4% with a signal-to-noise ratio (SNR) of −5 dB. In addition, the proposed multi-delay features fusion CNN (MDFCNN) scheme does not need a priori information, i.e., modulation type, channel coefficients, and noise power, which is well suited to non-collaborative communication.

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Section: Multi-delay Feature Extractionmentioning

confidence: 99%

Section: Proposed Cnn Recognition Model With Features Fusionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Automatic Space-Time Block Code Recognition Using Convolutional Neural Network With Multi-Delay Features Fusion

Zhang¹,

Yan²,

Zhang³

et al. 2021

IEEE Access

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A Comprehensive Review of Direction-of-Arrival Estimation and Localization Approaches in Mixed-Field Sources Scenario

Molaei,

Zakeri,

Andargoli

et al. 2024

IEEE Access

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Direction-of-arrival (DOA) estimation plays a crucial role in array signal processing across various domains, including radar, sonar, wireless communications, and seismic exploration. However, traditional DOA techniques often assume either far-field (FF) or near-field (NF) propagation, limiting their applicability in scenarios involving mixed-field sources. DOA estimation and localization in scenarios involving mixed NF and FF sources is a complex and dynamic field that has garnered significant research attention in recent years. This multifaceted and evolving area holds promise for addressing challenges in radar, wireless communications, and acoustic sensing applications. This review paper provides a comprehensive overview of the methodologies, techniques, and advancements in this domain. We categorize existing methodologies, discussing their advantages and limitations. Furthermore, we delve into the mathematical modeling of mixed-field sources and essential signal processing techniques for parameter estimation. Special attention is given to technical issues such as aperture loss, computational complexity, and hardware considerations. The paper discusses the various sources of noise in the mentioned scenario and highlights the importance of modeling noise accurately for effective estimation. It also explores different scenarios and assumptions considered in the literature, ranging from non-Gaussian and nonstationary noise environments to scenarios involving multipath propagation and unknown mutual coupling effects. A detailed examination of the statistical approaches used in DOA estimation and localization reveals a diverse range of methods, including higher-order statistics and second-order statistics, each with its own advantages and applications. A comparative evaluation of various approaches highlights their performance in terms of estimation accuracy, resolution, aperture loss and computational efficiency. This provides insights into the trade-offs involved in choosing between different approaches. The review also identifies promising future research directions, such as the exploration of advanced signal processing techniques like compressive sensing and deep learning, exact NF modeling, estimation based on one-bit measurements, the integration of polarization diversity, employing metasurface antennas, tracking parameters, and the utilization of full-wave or experimental data for a more realistic representation of the challenges. By reviewing advances in methodologies and techniques, as well as outlining future research directions aimed at addressing the complexities of mixed-field scenarios, this paper paves the way for the development of more robust and reliable localization systems capable of handling real-world complexities.

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Non-Standalone 5G NR Fiber-Wireless System Using FSO and Fiber-Optics Fronthauls

Lopes

Lima

Pereira

et al. 2021

J. Lightwave Technol.

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Higher order statistics for modulation and STBC recognition in MIMO systems

Cited by 6 publications

References 42 publications

Automatic Space-Time Block Code Recognition Using Convolutional Neural Network With Multi-Delay Features Fusion

Automatic Space-Time Block Code Recognition Using Convolutional Neural Network With Multi-Delay Features Fusion

A Comprehensive Review of Direction-of-Arrival Estimation and Localization Approaches in Mixed-Field Sources Scenario

Non-Standalone 5G NR Fiber-Wireless System Using FSO and Fiber-Optics Fronthauls

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