Abstract
A deep learning-based strategy for the analysis of the self-interference in single frequency networks (SFNs) for digital terrestrial television (DTT) broadcasting is considered. Several laboratory measurements were performed to create a dataset that relates the self-interference parameters and some quality metrics of the resulting received signal. The laboratory setup emulates an SFN scenario with two DTT transmitters. The strongest received signal and the relative values of attenuation and delay between the signals stand for the input parameters. The modulation error ratio (MER) of the strongest received signal, the MER of the resulting signal, and the SFN gain (SFNG) are the output parameters. This dataset is used to train four different multi-layer perceptron (MLP) models to predict accurate maps of interference and signal quality metrics. The considered models are suitable as complements for any multiple frequency network (MFN) coverage software with the capability to return the signal strength and the position data. This way, the SFN self-interference behavior can be predicted by considering only a proper description of the MFN coverage.
Highlights
The remarkable growth of mobile services and wireless communication technologies has led to a revision of the way the available spectrum bands are allocated
In digital terrestrial television (DTT) broadcasting, the spectral efficiency achieved with multiple frequency networks (MFNs) is significantly improved when moving to single frequency networks (SFNs)
The SFN gain (GSFN ) is the third output feature, which is calculated as the difference between the MERSFN and the MERMFN parameters
Summary
The remarkable growth of mobile services and wireless communication technologies has led to a revision of the way the available spectrum bands are allocated. Self-interference in SFNs is only considered when the interfering signals arrive with a delay longer than the guard interval. This only represents a critical scenario where the interference is mostly destructive. Several network planning strategies based on deep learning (DL) algorithms are being considered as a reasonable alternative for the configuration of broadcasting systems. These strategies allow reducing the computational complexity of theoretical models and the planning cost of the field-testing-based approaches [5,6,7]. The implementation of deep learning-based models to predict the interference and the resulting signal quality metrics
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