Spectrum monitoring and interference detection are crucial for the satellite service performance and the revenue of SatCom operators. Interference is one of the major causes of service degradation and deficient operational efficiency. Moreover, the satellite spectrum is becoming more crowded, as more satellites are being launched for different applications. This increases the risk of interference, which causes anomalies in the received signal, and mandates the adoption of techniques that can enable the automatic and real-time detection of such anomalies as a first step towards interference mitigation and suppression.

In this paper, we present a Machine Learning (ML)-based approach able to guarantee a real-time and automatic detection of both short-term and long-term interference in the spectrum of the received signal at the base station. The proposed approach can localize the interference both in time and in frequency and is universally applicable across a discrete set of different signal spectra. We present experimental results obtained by applying our method to real spectrum data from the Swedish Space Corporation. We also compare our ML-based approach to a model-based approach applied to the same spectrum data and used as a realistic baseline. Experimental results show that our method is a more reliable interference detector.

Link adaptation provides high spectral efficiency in wireless communications by selecting appropriate transmission parameters, e.g., the modulation and coding scheme (MCS), based on the instantaneous wireless channel. However, link adaptation suffers from impairments due to channel state information (CSI) feedback delay. In this paper, we extend the data-driven MCS selection scheme in our previous work to the case ofoutdated CSI, by assuming that CSI history is available to the system. We present two approaches that leverage the CSI history to optimally select the MCS for the current channel, i.e., i) an end-to-end (E2E) machine learning approach and ii) a hybrid data-driven and model-based approach. The E2E method uses the CSI history as input to a neural network for MCS selection. Conversely, the hybrid method uses a lower-dimensionality sufficient statistic for the instantaneous CSI, computed from the CSI history, as input to a neural network for MCS selection. We demonstrate that replacing the CSI history with the sufficient statistic comes without loss of generality. Moreover, by means of numerical experiments, we show that both approaches effectively compensate for the feedback delay. However, we advocate the hybrid approach as it comes with the additional benefits of i) a smaller neural network, which in turn requires a lower amount of data and training time, ii) improved explainability, and iii) better insights into optimization choices.

The weighted minimum mean square error (WMMSE) algorithm was proposed to provide a locally optimum solution to the otherwise NP-hard weighted sum rate maximization beamforming problem, but it can still be prohibitively complex for real-time implementation. With the success of deep unfolding in trading off complexity and performance, we propose to apply deep unfolding to the WMMSE algorithm. With respect to traditional end-to-end learning, deep unfolding incorporates expert knowledge, with the benefits of immediate and well-grounded architecture selection, fewer trainable parameters, and better explainability. However, the classical formulation of the WMMSE algorithm given by Shi et al. is not amenable for deep unfolding due to matrix inversions, eigendecompositions, and bisection searches. Therefore, we present an alternative formulation that circumvents these operations. By means of simulations, we show that the deep unfolded WMMSE algorithm performs on par with the original WMMSE algorithm, at a lower computational load.

Downlink beamforming is a key technology for cellular networks. However, computing beamformers that maximize the weighted sum rate (WSR) subject to a power constraint is an NP-hard problem. The popular weighted minimum mean square error (WMMSE) algorithm converges to a local optimum but still exhibits considerable complexity. In order to address this trade-off between complexity and performance, we propose to apply deep unfolding to the WMMSE algorithm for a MU-MISO downlink channel. The main idea consists of mapping a fixed number of iterations of the WMMSE into trainable neural network layers. However, the formulation of the WMMSE algorithm, as provided in Shi et al., involves matrix inversions, eigendecompositions, and bisection searches. These operations are hard to implement as standard network layers. Therefore, we present a variant of the WMMSE algorithm i) that circumvents these operations by applying a projected gradient descent and ii) that, as a result, involves only operations that can be efficiently computed in parallel on hardware platforms designed for deep learning. We demonstrate that our variant of the WMMSE algorithm convergences to a stationary point of the WSR maximization problem and we accelerate its convergence by incorporating Nesterov acceleration and a generalization thereof as learnable structures. By means of simulations, we show that the proposed network architecture i) performs on par with the WMMSE algorithm truncated to the same number of iterations, yet at a lower complexity, and ii) generalizes well to changes in the channel distribution.