Abstract
Signal detection, identification, and characterization are among the major challenges in aerial communication systems. The ability to detect and recognize signals using cognitive technologies is still under active development when addressing uncertainties regarding signal parameters, such as blank spaces available within the transmitted signal and the utilized bandwidth. This paper proposes a learning-based identification framework for heterogeneous signals with orthogonal frequency division multiplexing (OFDM) modulation as generated in a simulated environment at an a priori unknown frequency. The implemented region-based signal identification method utilizes cyclostationary features for robust signal detection. Signal characterization is performed using a purposely-built, lightweight, region-based convolutional neural network (R-CNN). It is shown that the proposed framework is robust in the presence of additive white Gaussian noise (AWGN) and, despite its simplicity, shows better performance compared with conventional popular network architectures, such as GoogLeNet, AlexNet, and VGG 16. The signal characterization performance is validated under two degraded environments that are unknown to the system: Doppler shifted and small-scale fading. High performance is demonstrated under both degraded conditions over a wide range of signal to noise ratios (SNRs) and it is shown that the detection probability for the proposed approach is improved over those for conventional energy detectors. It is found that the signal characterization performance deteriorates under extreme conditions, such as lower SNRs and higher Doppler shifts.
Highlights
Cognitive communication is a promising solution that can help address the scarcity of spectrum resources in highly dynamic radio frequency (RF) environments for aeronautical wireless communications
It has been shown that cyclostationary properties of communication signals based on their second-order periodicity can be used for signal detection [3]–[5] and its modulation classification [6], [7] using cyclic cross-correlation functions, e.g., using the spectrum coherence function (SCF) [8] or cyclic auto-correlation
The orthogonal frequency division multiplexing (OFDM) scheme is extensively used in modern communication data links (e.g., IEEE 802.22, LTE, DVB-T/T2, and IEEE 802.11 a/g) due to its remarkable capability to reduce inter-symbol interference (ISI) and multipath effects in air-toground communications
Summary
This paper proposes utilizing a fusion of the cyclostationary detection with a learning-based approach capable of simultaneous detection and characterization of PU transmissions. In this framework both spectrum sensing and signal characterization functionalities are provided for a CR-enabled communication system. Due to benefits from the fusion of both cyclostationary analysis and the learning-based approach, the proposed method allows extending the spectrum sensing to multiple signal types while simultaneously estimating their frequency, bandwidth, and periodicity and providing robustness in the presence of noise, Doppler shifts, and small-scale fading.
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