This paper presents a proportional–integral–derivative (PID)-based automatic gain control (AGC) approach for satellite communications attacked by partial-time partial-band additive white Gaussian noise (AWGN) jamming. The analysis based on the stochastic model predictive control (SMPC) shows that the AGC performance depends on the accurate characterization of the jammed signal in the future time instants. However, such characterization is generally unavailable. To overcome the limitations of the existing AGC schemes without considering the future trend of the signal amplitude tracking errors (i.e., the difference between the average amplitude and the desired amplitude), the proposed approach uses the derivative term of signal amplitude tracking errors for anticipatory control and the integral term in the PID control to eliminate steady-state errors. Furthermore, different block sizes of the sampled signals are used for computing and selecting gain control values to achieve a good trade-off between fast response and robustness to noise/jamming. Extensive simulations of a system based on the typical satellite transponder link using Quadrature Phase Shift Keying (QPSK) modulated input signals and AWGN noise/jamming demonstrate that the proposed approach can achieve better control performance for maintaining the desired signal amplitude range and smaller bite error rate (BER) in the case of AWGN jamming, as compared with the existing AGC schemes.
KEYWORDS: Signal to noise ratio, Data modeling, Telecommunications, Signal processing, Education and training, Denoising, Deep learning, Tunable filters, Feature extraction, Interference (communication)
The scarcity and finite nature of the wireless spectrum drives technology development for spectrum utilization. With the increased complexity of the radio-access environment and susceptibility to interference disruption, challenges exist which demand advanced interference suppression techniques. Recently, the advance of artificial intelligence (AI) promotes technology for data-driven modeling of complicated relationships, which provides numerous tools and techniques for signal processing and analysis. This paper develops a deep learning-based radio signal interference suppression method by leveraging the adaptive features and Convolutional Neural Network (CNN) based Denoising autoencoder (DAE). By simulating the communication system with stochastic channel effects (AWGN channel), the proposed Suppression of Interference DEA (S-IDEA) method is validated using the original signals and the corrupted signals through channel effects. The results show that S-IDEA can effectively perform interference suppression from AWGN channel at different SNR levels and achieve excellent SNR improvement.
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