We propose a novel deep learning-based algorithm for channel prediction and energy efficiency (EE) optimisation in an intelligent reflecting surface (IRS) aided Terahertz communication system. Specifically, a multi-antenna base station with an IRS with massive reflecting elements is designed to serve multiple moving users. A deep learning-based prediction-optimisation scheme is presented where we first propose a transformer encoder with channel index embedding (TE-CIE) deep learning model for time-varying channel prediction. With the help of channel prediction, an EE optimisation algorithm is then designed to maximise the EE in advance based on the predicted channel state information (CSI). Finally, we combine both methods to construct a deep learning-based prediction-optimisation scheme. Specifically, the future CSI is predicted by TE-CIE and the IRS phase-shift and precoding matrices are optimised in advance. Simulation results demonstrate that our proposed channel prediction method achieves close-to-optimal performance in terms of low mean absolute error and a much faster speed than the two baseline models. We demonstrate that the proposed EE optimisation algorithm outperforms the baseline algorithms in terms of much better EE under diverse parameter settings. Finally, the proposed prediction-optimisation scheme achieves at least twice the EE improvement compared to the baseline methods in the literature.

Unmanned aerial vehicle (UAV) swarms have become a promising solution to enhance modern wireless communication in complicated environments. However, due to the existence of real-world malicious attacks, the performance of prediction and optimisation methods used for UAV swarms are easily degraded. In this paper, we propose a novel deep learning-based user mobility prediction, user assignment and drone position optimisation scheme for a UAV swarm-enabled wireless communication system in the presence of malicious Global Navigation Satellite System (GNSS) spoofing attackers. Specifically, a robust deep learning-based user mobility prediction model, namely denoising autoencoder recurrent transformer (DART), is designed. Additionally, two efficient user assignment and drone position optimisation methods are proposed. The proposed deep learning model forecasts user locations, on which we construct and solve assignment and position optimisation problems. Simulation results show that the proposed deep learning-based prediction-optimisation scheme can provide up to 30% higher overall sum rate compared with the adversarially trained long short-term memory (LSTM) baseline and almost double the overall sum rate compared with the vanilla LSTM baseline.

We propose an ensemble machine learning (EML) based robust cooperative spectrum sensing framework in full-duplex cognitive radio networks (FD-CRNs), which is robust with accuracy against malicious attacks and interference. FD communication improves the spectrum awareness capability of secondary users (SUs) by allowing them to sense and transmit simultaneously over the same frequency band. However, it also complicates the sensing environment by introducing self-interference and co-channel interference. Meanwhile, the presence of malicious attacks such as Primary User Emulation and Spectrum Sensing Data Falsification (SSDF) attacks also degrades the sensing performance. To alleviate the influence of interference and attacks, we design an EML framework that provides robust and accurate fusion performance. In such a context, we analyse the spectrum waste probability, collision probability and secondary throughput in both FD Listen-Before-Talk and Listen-And-Talk protocols. Simulation results show that our proposed EML framework can provide lower and more robust false-alarm probability than single-model based fusion methods with the same detection probability constraint for any size of training sets. It also outperforms the conventional majority vote based fusion strategy in terms of spectrum waste probability, collision probability and secondary throughput for any number of SSDF SUs, only at the cost of slightly higher inference time.