Sub-Nyquist Cyclostationary Detection of GFDM for Wideband Spectrum Sensing

Abstract

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.

A Survey of Energy and Spectrum Harvesting Technologies and Protocols for Next Generation Wireless Networks

“…These features are used for detecting the presence of primary users by PU signals as cyclostationary features are absent in stationary noise or interference signal [97]. In [110], using the cyclostationary features of the modulated signal, optimized recovery of generalized frequency division multiplexing (GFDM) signal is presented. By exploring the sparse property, the spectral correlation function (SCF) of the GFDM signal and the covariance function of its compressive samples are constructed.…”

A Modified Interference Approximation Scheme for Improving Preamble Based Channel Estimation Performance in FBMC System

“…All pseudo pilots are real with nulls at the first and third symbols of the preamble. IAM-C that has been proposed in [26]- [28] uses pseudo-pilots which are either purely real or imaginary at all the subcarriers. This is done by setting the middle FBMC symbol equal to that in IAM-R but with the pilots at the odd subcarriers multiplied by j (to make it imaginary) and hence the magnitude of the pseudo pilots is maximized.…”

On the Performance of Quickest Detection Spectrum Sensing: The Case of Cumulative Sum

“…s Spectrum sensing is a cornerstone in the deployment of cognitive radio networks. Spectrum sensing can be achieved through different techniques including energy detection [3] and cyclostationary detection [4], [5]. On the other hand, signal detection based on probabilistic models, i.e., maximum-likelihood-ratio test (MLRT) and general-likelihood-ratio test (GLRT) [6]- [10] exploits the distributions of the received signal under the two hypotheses (occupied or vacant spectrum slot) to decide on the presence or absence of the PU's signal.…”