Spectrum sensing is one of the technologies that is used to solve the current problem of low utilization of spectrum resources. However, when the signal-to-noise ratio is low, current spectrum sensing methods cannot well-handle a situation in which the prior information of the licensed user signal is lacking. In this paper, a blind spectrum sensing method based on deep learning is proposed that uses three kinds of neural networks together, namely convolutional neural networks, long short-term memory, and fully connected neural networks. Experiments show that the proposed method has better performance than an energy detector, especially when the signal-to-noise ratio is low. At the same time, this paper also analyzes the effect of different long short-term memory layers on detection performance, and explores why the deep-learning-based detector can achieve better performance.

Signal-to-noise ratio (SNR) is a priori information necessary for many signal processing algorithms or techniques. However, there are many problems exsisting in conventional SNR estimation techniques, such as limited application range of modulation types, narrow effective estimation range of signal-to-noise ratio, and poor ability to accommodate non-zero timing offsets and frequency offsets. In this paper, an SNR estimation technique based on deep learning (DL) is proposed, which is a non-data-aid (NDA) technique. Second and forth moment (M2M4) estimator is used as a benchmark, and experimental results show that the performance and robustness of the proposed method are better, and the applied ranges of modulation types is wider. At the same time, the proposed method is not only applicable to the baseband signal and the incoherent signal, but can also estimate the SNR of the intermediate frequency signal.

Sidelobe cancellation (SLC) is a well‐established beamforming technique for mitigating interference, particularly in the context of satellite communication (SATCOM). However, traditional SLC suffers from the issue of partially canceling the desired signal at high signal‐to‐noise ratio (SNR), primarily due to unconstrained beamforming processing. Extensive research has been conducted to address this problem; however, existing algorithms have limitations such as dependence on knowledge of signal array vectors or number of interferers and involve high computational complexity. In this paper, we propose a robust SLC algorithm based on beamforming vector norm constraint. Our proposal offers a practical solution by only requiring knowledge of the earth station antenna gain and maximum auxiliary array gain to the desired signal, both of which are fully known. Furthermore, compared to traditional SLC, our proposed method introduces additional computational complexity that only scales linearly with the size of the auxiliary array. Simulation results demonstrate comparable performance between our proposed method and existing techniques such as diagonal loading and spatial degrees‐of‐freedom control‐based algorithms.

To address the limitations of traditional Signal-to-Interference-plus-Noise Ratio (SINR) estimation methods under non-cooperative malicious interference, such as their confinement to specific modulation signals and the narrow effective estimation range of Signal-to-Noise Ratio (SNR), this paper proposes an adaptive robust SINR estimation algorithm based on joint decoupling of eigenvalues and steering vectors. The algorithm first realizes the joint estimation of the number of sources and noise power under finite snapshots via Marchenko-Pastur (MP) law-based iteration, then completes the coarse estimation of desired signal power through steering vector-eigenvector matching and performs strategy switching according to the coarsely estimated Signal-to-Interference Ratio (SIR). In scenarios where the desired signal power is non-dominant, the robust Capon method with adaptive diagonal loading is adopted for fine estimation, and, finally, the interference power is decoupled using the matrix trace identity. The algorithm is independent of modulation characteristics, applicable to both constant-modulus and non-constant-modulus signals, and only requires prior information of the Direction of Arrival (DOA) of the desired signal. Simulation results demonstrate that under complex non-ideal conditions, including 30 dB Interference-to-Noise Ratio (INR), steering vector mismatch, and finite snapshots, the average Root Mean Square Error (RMSE) of the proposed algorithm across the entire SNR range is only 0.83 dB, which significantly outperforms mainstream blind estimation algorithms such as RMT-BSE, M2M4, and SSME. Its robustness and universality are more in line with the requirements of non-cooperative anti-jamming communications.