Blind Cyclostationary-Based Carrier Number and Spacing Estimation for Carrier-Aggregated Direct Sequence Spread Spectrum Cellular Signals

• Published in: Electronics (Basel) 2024-09, Vol.13 (18), p.3743
• Main Author: Görçin, Ali
• Format: Article
• Language: English
• Subjects: Algorithms; Analysis; Artificial neural networks; Code Division Multiple Access; Communications equipment; Communications systems; False alarms; Hypotheses; Hypothesis testing; Mean square errors; Neural networks; Noise measurement; Parameter estimation; Pseudorandom; Sequences; Signal processing; Signal to noise ratio; Simulation; Spread spectrum; Spread spectrum transmission; Synchronism; Wireless communications
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics13183743

Automatic and blind parameter estimation based on the inherent features of wireless signals is a major research area due to the fact that these techniques lead to the simplification of receivers, especially in terms of coarse synchronization, and more importantly reduce the signaling load at the control channels. Thus, in the literature, many techniques are proposed to estimate a vast set of parameters including modulation types and orders, data and chip rates, phase and frequency offsets, and so on. In this paper, a cyclostationary feature detection (CFD) based method is proposed to estimate the carrier numbers and carrier spacing of carrier-aggregated direct sequence spread spectrum (DSSS) cellular signals blindly. The particular chip rate of the signal is also estimated through the process jointly. The proposed CFD-based method unearths the inhered and hidden second-order periodicities of carrier-aggregated DSSS signals, particularly targeting repeated pseudorandom noise sequences of users over the carriers. Throughout the paper, after the proposed method is formulated, the measurement setup that is developed to collect the data for the validation of the method is introduced. The measurement results are post-processed for performance analysis purposes. To that end, the method is investigated in terms of signal-to-noise ratio (SNR) values, different channel conditions, and measurement durations. Furthermore, the performance of the proposed method is compared with that of energy detection. The measurement results indicate superior performance of the proposed method under significant wireless channel impairments and in low-SNR regions, e.g., for 0 dB the proposed method provides more than 0.9 detection performance for the case of 0.1 false alarm rate, while the performance of ED is 0.6 under the same wireless channel impairments. The raw outputs of the method can be utilized to train a convolutional neural network to eliminate the statistical estimation process in future work.