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In this work the discrete cosine transform is proposed for LTE systems with the aid of feed-forward neural network as a suitable equalizer to retrieve the effect of channel within Rayleigh Faded channels. This system was implemented using the QuadraturePhase Shift Keying as a modulation technique, and using different Maximum Doppler Shift, which represents the highest Doppler shift that can occur between the transmitter and the receiver in a given wireless channel. by using DCT-FFNN with different MDS values effectively mitigates signal distortion resulting from multipath propagation and common issues in wireless communication networks and demonstrates higher accuracy in predicting BER values. According to the research, the performance will be better at MDS 50 when compared to the rest of the MDS used in the paper. These advantages come with minimal loss in data rate and bandwidth and no additional expense in terms of power. The simulation results indicate that an FFNNs-based channel estimator outperforms the pilot-based channel estimator in LTE systems operating over a Rayleigh fading channel, because FFNNs have low complexity and can quickly and accurately adjust the signal strength of incoming signals based on their input. All LTE system models were implemented using MATLAB 2016. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles (UAVs) are typical real-time embedded systems, which require precise locations for completing flight missions. The Global Navigation Satellite System (GNSS) plays a crucial role in navigation and positioning for UAVs. However, GNSS spoofing attacks pose an increasing threat to GNSS-dependent UAVs. Existing spoofing detection methods primarily rely on simulated data, perception data from multiple UAVs, or various control parameters. This paper proposes SigFeaDet, a signal feature-based GNSS spoofing detection approach for UAVs utilizing machine learning techniques. The core concept revolves around identifying anomalies in signal features arising from differences between authentic and spoofing signals. Key signal features, including Carrier-to-Noise Density Ratio (CN0) and Doppler frequency crucial for GNSS positioning, are employed to discern spoofing signals. Various machine learning algorithms are leveraged to train on GNSS signal data, determining the most effective classifier. TEXBAT GNSS dataset is processed to extract spoofing signal data, and flight experiments are conducted to gather GNSS data, augmenting the authentic GNSS signal dataset. The detection accuracy exceeds 95%. Equal Error Rate (EER) is approximately 5%. We evaluate various impact factors on SigFeaDet to show its robustness, including differences in velocities, altitudes, and experimental locations (10 kilometers apart), and the accuracy consistently surpasses 99%. [ABSTRACT FROM AUTHOR]