Cyclostationary detection of 5G GFDM waveform using time smoothing algorithms in cognitive radio transmission

El-Alfi, Noura A.; Abdel-Atty, Heba M.; Mohamed, Mohamed A.

doi:10.1109/icuwb.2017.8250957

Cited by 8 publications

(10 citation statements)

References 11 publications

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“…First, the simulation model is presented, and then the performance of the proposed method is discussed with respect to the impact of the rate reduction, the noisy environment and the sensing time. Finally, a comparative study is presented for our proposal and the proposed methods in [18] and [19].…”

Section: Resultsmentioning

confidence: 99%

“…x (17) where (W=F −T ⊗I N ) and I N is the identity matrix of size N. From (16) and 17, we can obtain r x = H † Ws (c) x (18) where H † is the pseudo-inverse of H . In the following section, we denote the sampling process as z t =Ax t, where z t is the sub-Nyquist sample vector and A is the measurement matrix.…”

Section: A Projection Relationshipmentioning

confidence: 99%

“…By plugging (18) into 22, we obtain the linear projection relation between the measurement vector r z and the SCFs r z = φH † Ws (c) x = ψs (c) x (23) where x from (23),which is an underdetermined system, so we solve the problem using the linear programming method:…”

Section: } N/2×(n(n+1)/2) and Q M {0 1/2 1} (M(m+1)/2)×m/2 Are Mapmentioning

confidence: 99%

“…Here, we compare the results of the proposed method to GFDM detection with the references [18] and [19]. In related work [19], the authors succeeded in partially reconstructing the cyclic autocorrelation function (CAF) of OFDM by deducing the projection relationship between the CAF and the covariance matrix of compressed samples of OFDM and then applying the greedy algorithm.…”

Section: Comparative Studymentioning

confidence: 99%

“…In [18], the cyclostationary properties of GFDM were analyzed using time smoothing algorithms: the strip spectral correlation algorithm (SSCA) and the fast Fourier transform (FFT) accumulation method (FAM). The performance of the SSCA was better than that of the classic method by 77%, and better than the FAM method by 38%.…”

Section: Introductionmentioning

confidence: 99%

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Sub-Nyquist Cyclostationary Detection of GFDM for Wideband Spectrum Sensing

2019

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Spectrum scarcity is a challenging problem in wireless communications: high data rates are needed to support 5G new technologies. However, the spectrum is underutilized. To address this problem, cognitive radio (CR) is proposed to exploit the underutilized spectrum. The main requirement for the future CR networks is wideband spectrum sensing, which provides secondary users with the available frequency bands across a wide frequency range. Secondary users should fill these bands without causing interference to licensed users. Thus, new waveforms are proposed for the 5G physical layer. Generalized frequency division multiplexing (GFDM) is considered to be a contender for the 5G new physical layer. The GFDM is a block-based waveform that is suitable for fragmented spectrum scenarios and is designed to overcome the drawbacks of orthogonal frequency-division multiplexing (OFDM) used in 4G. The GFDM is the perfect candidate for 5G and CR technologies. Considering the cyclostationarity properties of modulated signals, we propose an optimized recovery method for the GFDM signals in the wideband regime. By exploiting the signal sparsity, we can recover the spectral correlation function (SCF) of the GFDM from digital samples of the GFDM taken at a sub-Nyquist rate to reduce the sampling time. Furthermore, a generalized likelihood ratio test is applied to the recovered function to detect multiple signal sources and identify the spectrum occupancy. The numerical results show that our method achieves a high probability of detection at a low signal-to-noise ratio (SNR) with robustness in terms of rate reduction in wireless networks. INDEX TERMS Cognitive radio, compressive sampling, cyclostationarity, generalized frequency division multiplexing (GFDM), wideband spectrum sensing.

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Section: Resultsmentioning

confidence: 99%

Section: A Projection Relationshipmentioning

confidence: 99%

Section: } N/2×(n(n+1)/2) and Q M {0 1/2 1} (M(m+1)/2)×m/2 Are Mapmentioning

confidence: 99%

Section: Comparative Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sub-Nyquist Cyclostationary Detection of GFDM for Wideband Spectrum Sensing

2019

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Evaluation and Analysis of FBMC/OQAM Systems Based on Pulse Shaping Filters

2020

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Cyclic prefix orthogonal frequency division multiplexing (CP-OFDM) is the most widespread multicarrier modulation scheme. However, the use of a rectangular filter in CP-OFDM causes poor outof-band (OoB) radiation. Moreover, the CP causes spectral efficiency loss. To overcome these limitations, a filter bank multicarrier (FBMC) was proposed which uses prototype pulse shaping filters that can be adapted to fulfil the system requirements. The choice of the filter is crucial for FBMC/offset quadrature amplitude modulation (OQAM) due to its significant impact on the achieved performance. In this paper, new prototype pulse shaping filters in FBMC/OQAM systems are proposed, aiming to improve the system performance. A number of prototype pulse shaping filters, such as raised cosine pulse (RC), root raised cosine pulse (RRC), better than raised cosine pulse (BTRC), modified Bartlett-Hanning (MBH), improved sinc power pulse (ISP), phase modified sinc pulse (PMSP), parametric linear pulse (PLP), linear combination pulse (LCP), PHYDYAS and Hermite, are considered. The performance of each pulse shaping filter in FBMC/OQAM is evaluated and compared other in terms of the power spectral density (PSD), spectral efficiency, signal-to-interference ratio (SIR), time offset (TO), carrier frequency offset (CFO) and bit error rate (BER) performance over various channels. In addition, as multiple input multiple output (MIMO) is one of the main challenges associated with FBMC, we propose the new approach that use the frequency/time block spreading based on a Walsh-Hadamard (WH) code in MIMO FBMC/OQAM and investigate the combination of the our proposed prototype pulse shaping filters with MIMO systems. The proposed filters are shown to be suitable candidates for FBMC/OQAM systems.

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Filtered Multicarrier Waveforms Classification: A Deep Learning-Based Approach

2021

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Automatic signal recognition (ASR) plays an important role in various applications such as dynamic spectrum access and cognitive radio, hence it will be a key enabler for beyond 5G communications. Recently, many research works have been exploring deep learning (DL) based ASR, where it has been shown that simple convolutional neural networks (CNN) can outperform expert features based techniques. However, such works have been primarily focusing on single-carrier signals. With the advent of spectrally efficient filtered multicarrier waveforms, we propose in this paper, to revisit the DL based ASR to account for the variety and complexity of these new transmission schemes. Specifically, we design two types of classification algorithms. The first one relies on the cyclostationarity characteristics of the investigated waveforms combined with a support vector machine (SVM) classifier; while the second one explores the use of a four-layer CNN which performs both features extraction and classification. The proposed approaches do not require any a priori knowledge of the received signal parameters, and their performance is evaluated in a multipath channel through simulations for a signal-to-noise ratio (SNR) ranging from −8 to 20 dB. The simulation results show that, despite cyclostationary characteristics being highly discriminative, the CNN outperforms the cyclostationary based classification especially for short time received signals, and low SNR levels.INDEX TERMS Automatic signal recognition, multicarrier waveforms, classification, deep neural networks, support vector machines, cyclostationarity.

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Cyclostationary detection of 5G GFDM waveform using time smoothing algorithms in cognitive radio transmission

Cited by 8 publications

References 11 publications

Sub-Nyquist Cyclostationary Detection of GFDM for Wideband Spectrum Sensing

Sub-Nyquist Cyclostationary Detection of GFDM for Wideband Spectrum Sensing

Evaluation and Analysis of FBMC/OQAM Systems Based on Pulse Shaping Filters

Filtered Multicarrier Waveforms Classification: A Deep Learning-Based Approach

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