Spectrum Sensing Based on Higher Order Statistics for OFDM Systems over Multipath Fading Channels in Cognitive Radio

Haque, Md. Dulal; Sugiura, Yuta; Shimamura, Tetsuya

doi:10.2299/jsp.23.257

Cited by 3 publications

(2 citation statements)

References 25 publications

(33 reference statements)

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“…By sensing the channel parameters, the adaptive qualities of the Cognitive Radio enable it to dynamically change the modulation scheme used for transmission in order to improve the transmission rates and quality. However, dynamic modulations require a recognition method for the received signal modulation scheme, which is known as automatic modulation classification, or modulation recognition and modulation detection in the literature [ 3 , 4 , 5 ]. Spectrum sensing is essential for adaptive networks that must change according to their location without causing any interruption to local users’ daily communication needs, especially in times of emergency, where every message counts.…”

Section: Introductionmentioning

confidence: 99%

Efficient Cumulant-Based Automatic Modulation Classification Using Machine Learning

Dgani,

Cohen

2024

Sensors

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This paper introduces a new technique for automatic modulation classification (AMC) in Cognitive Radio (CR) networks. The method employs a straightforward classifier that utilizes high-order cumulant for training. It focuses on the statistical behavior of both analog modulation and digital schemes, which have received limited attention in previous works. The simulation results show that the proposed method performs well with different signal-to-noise ratios (SNRs) and channel conditions. The classifier’s performance is superior to that of complex deep learning methods, making it suitable for deployment in CR networks’ end units, especially in military and emergency service applications. The proposed method offers a cost-effective and high-quality solution for AMC that meets the strict demands of these critical applications.

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Section: Introductionmentioning

confidence: 99%

Efficient Cumulant-Based Automatic Modulation Classification Using Machine Learning

Dgani,

Cohen

2024

Sensors

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“…Higher order statistics are useful in digital signal processing, communication systems, signal detection, and a variety of other applications [17][18][19][20]. The higher order statistics, including third order statistics and fourth order statistics, are utilized for sensing OFDM transmitted signals, where the noise uncertainty cases are not considered for OFDM sensing [21]. Although some recent works considered spectrum sensing in noise uncertainty environments, the sensing performance is not very good in severe noise environments [22], [23].…”

Section: -Introductionmentioning

confidence: 99%

Spectrum Sensing of OFDM Signals Utilizing Higher Order Statistics under Noise Uncertainty Environments in Cognitive Radio Systems

HAQUE,

Shimamura

2023

jist

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Cognitive radio (CR) is an important issue to solve the spectrum scarcity problem for modern and forthcoming wireless communication systems. Spectrum sensing is the ability of the CR systems to sense the primary user signal to detect an ideal portion of the radio spectrum. Spectrum sensing is mandatory to solve the spectrum scarcity problem and the interference problem of the primary user. Noise uncertainty consideration for orthogonal frequency division multiplexing (OFDM) transmitted signals in severe noise environments is a challenging issue for measuring the performance of spectrum sensing. This paper proposed a method using higher order statistics (HOS) functions including skewness and kurtosis for improving the sensing performance of a cyclic prefix (CP) based OFDM transmitted signal for noise uncertainty. The detection performance of OFDM systems is measured for various CP sizes using a higher order digital modulation technique over a multipath Rayleigh fading channel for low signal-to-noise ratio (SNR) cases. In the proposed method, the CP-based OFDM transmitted signal sensing performance is measured and compared with the conventional methods under noise uncertainty environments. Through comprehensive evaluation of simulation, it is demonstrated that the sensing performance of this method significantly outperforms conventional schemes in the case of noise uncertainty in severe noise environments.

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Efficient Cumulant-Based Automatic Modulation Classification Using Machine Learning

Dgani,

Cohen

2023

Preprint

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This paper presents a novel method for automatic modulation classification (AMC) for cognitive radio (CR) networks based on a simple classifier that is trained with high-order cumulant. The proposed method focuses on the statistical behavior of modulated signals and includes analog modulation and digital schemes, which received less attention in the literature. The effectiveness of the proposed method is demonstrated through simulation results using high-quality generated signals under different signal-to-noise ratios (SNRs) and channel conditions. The classification performance achieved by the proposed method is superior to that of the more complex deep learning methods, making it well-suited for deployment in end units of CR networks, particularly in military and emergency service applications. The proposed method offers a cost-effective, high-quality solution for AMC that meets the stringent requirements of these critical applications.

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Spectrum Sensing Based on Higher Order Statistics for OFDM Systems over Multipath Fading Channels in Cognitive Radio

Cited by 3 publications

References 25 publications

Efficient Cumulant-Based Automatic Modulation Classification Using Machine Learning

Efficient Cumulant-Based Automatic Modulation Classification Using Machine Learning

Spectrum Sensing of OFDM Signals Utilizing Higher Order Statistics under Noise Uncertainty Environments in Cognitive Radio Systems

Efficient Cumulant-Based Automatic Modulation Classification Using Machine Learning

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