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.
Highlights
In the year 2020, data consumption is expected to increase; 3G and 4G technologies cannot accommodate these changes; a new mobile generation is needed
Each signal block is multiplied by a pseudo-random chipping sequence, and the output is passed through a low-pass integrator filter so we can obtain the compressed samples of the Generalized frequency division multiplexing (GFDM) signal
SIMULATION MODEL In our simulations, we consider a GFDM signal consisting of 16 sub symbols and 32 subcarriers with QPSK modulation
Summary
In the year 2020, data consumption is expected to increase; 3G and 4G technologies cannot accommodate these changes; a new mobile generation is needed. Another proposal in [20] suggested a recovery of the spectral correlation function (SCF) of OFDM from sub-Nyquist sampling via greedy algorithms suitable for the spectrum sensing of OFDM in CR applications. In [21], the authors proposed low-rate compressed sampling for GSM and time-division LTE signals using the cyclostationary properties of TD-LTE This method showed a high probability of detection with a short sensing time. To solve the problems associated with wideband cyclostationary sensing, we can use compressed sensing techniques [19] and [21] to recover the desired cyclic statistics from a few samples of the signal, which can reduce the sensing time and decrease the hardware cost.
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