Second-Order Cyclostationarity-Based Detection of LTE SC-FDMA Signals for Cognitive Radio Systems

Abstract

Abstract-In this paper, we investigate the detection of long term evolution (LTE) single carrier-frequency division multiple access (SC-FDMA) signals, with application to cognitive radio systems. We explore the second-order cyclostationarity of the LTE SC-FDMA signals, and apply results obtained for the cyclic autocorrelation function to signal detection. The proposed detection algorithm provides a very good performance under various channel conditions, with a short observation time and at low signal-to-noise ratios, with reduced complexity. The validity of the proposed algorithm is verified using signals generated and acquired by laboratory instrumentation, and the experimental results show a good match with computer simulation results.Index Terms-Cognitive radio, long term evolution (LTE), signal detection, single carrier-frequency division multiple access (SC-FDMA), signal cyclostationarity.

Cyclostationarity: New trends and applications

“…(1) the spectral line regeneration (Section 6.1, [114]) [79,335,357]; (2) the statistical test for presence of cyclostationarity (Section 6.3, [63]) [6,149,178,233,271,270,303,302]; (3) the statistical test for presence of spectral coherence (Section 6.4, [156]) or the cycle frequency domain profile (Section 7.2, [187]) [219,355]; (4) higher-order cyclostationarity properties (Section 6.7) [82,221,279,327,329].…”

“…In addition, cyclic statistics have been derived for: longcode DSSS signals [107], continuous phase modulated (CPM) signals [45,258], digital pulse streams modulated by cyclostationary sequences [268], multicarrier modulated signals [377], UWB signals [266,267,354], OFDM signals [59], OFDM and single carrier linearly digitally modulated signals [279], WiMAX and long-term evolution (LTE) OFDM signals [6], spatial multiplexing OFDM (SM-OFDM) and Alamouti coded OFDM (AL-OFDM) signals [181], block transmitted SCLD (BT-SCLD) signals [379], LTE SC-FDMA signals [178], LTE advanced signals [336], BOC signals [228], and global position system (GPS) signals [256].…”

Three-Event Energy Detection with Adaptive Threshold for Spectrum Sensing in Cognitive Radio Systems

“…Among the methods used for spectrum sensing, energy detection (ED) [4][5][6][7][8] is the most commonly used one, due to its simplicity as well as its almost universal applicability. Alternative spectrum sensing approaches use eigenvalue-based algorithms [9], covariance-based detection methods [10,11], cyclostationary feature detection [12,13], or compressive sensing [14,15]. A thorough and very recent survey on most known spectrum sensing methods can be found in [16].…”

Implementation of Deep Learning-Based Automatic Modulation Classifier on FPGA SDR Platform

“…In addition, this type of method is more sensitive to the mismatch of the model; for example, when a time shift occurs, the recognition performance deteriorates considerably; (2) The second method is a feature-based (FB) method that relies on prior knowledge. The features for classification include Dobre's use of first-order cyclostationary features of digital signals [7] and second-order cyclostationarity [8], the space-time correlation matrix features proposed by Marey and Choqueuse [9,10], wavelet features proposed by Hassan [11], and constellation features proposed by Mobasseri [12]. This method can obtain better results under the condition of a low SNR, but it is necessary to calculate various feature quantities according to the mathematical model of the modulation method in advance, and the calculation method is relatively complicated.…”