An important subfield of brain–computer interface is the classification of motor imagery (MI) signals where a presumed action, for example, imagining the hands' motions, is mentally simulated. The brain dynamics of MI is usually measured by electroencephalography (EEG) due to its noninvasiveness. The next generation of brain–computer interface systems can benefit from the generative deep learning (GDL) models by providing end‐to‐end (e2e) machine learning and increasing their accuracy. In this study, to exploit the e2e‐property of deep learning models, a novel GDL methodology is proposed where only minimal objective‐free preprocessing steps are needed. Furthermore, to deal with the complicated multi‐class MI–EEG signals, an innovative multilevel GDL‐based classifying scheme is proposed. The effectiveness of the proposed model and its robustness against noisy MI–EEG signals is evaluated using two different GDL models, that is, deep belief network and stacked sparse autoencoder in e2e manner. Experimental results demonstrate the effectiveness of the proposed methodology with improved accuracy compared with the widely used filter bank common spatial patterns algorithm.

In this work, by considering a variety of realistic hardware impairments, we aim to enhance the security of a cooperative relaying network, where a source intends to transmit its confidential information to a destination in the presence of a group of untrusted amplify-and-forward relays, as potential eavesdroppers (Eves), and an entirely passive multiple-antenna aided Eve. Our goal is to safeguard the information against these two types of eavesdropping attacks, while simultaneously relying on the untrusted relays to boost both the security and reliability of the network. To reach this goal, we propose a novel joint cooperative beamforming, jamming and power allocation policy to safeguard the confidential information while concurrently achieving the required quality-of-service at the destination. We also take into account both the total power budget constraint and a practical individual power constraint for each node. Our optimization problem can be split into two consecutive sub-problems. In the first sub-problem, we are faced with a non-convex problem which can be transformed into the powerful difference of convex (DC) program. A low-complexity iterative algorithm is proposed to solve the DC program, which relies on the constrained concave-convex procedure (CCCP). We further introduce a novel initialization method, which is based on a feasible point of the original problem obtained from a novel iterative feasibility search procedure, rather than an arbitrary (infeasible) point as in the conventional CCCP. The second sub-problem of our optimization problem is a convex optimization problem and can be solved efficiently adopting the classic interior point method. The numerical results provided illustrate that although the trusted relaying scenario outperforms the untrusted relaying for small and medium total power budgets, however, by increasing the total power budget, the secrecy performances of both the trusted and untrusted relaying converge to the same. Additionally, by equally sharing the total impairments at the relays between the transmitter and the receiver the best secrecy performance is presented. INDEX TERMS Physical layer security, untrusted relay, passive eavesdropper, hardware impairments, cooperative beamforming and jamming, optimal power allocation.

A half‐duplex relaying network provides 2 opportunities for passive eavesdroppers (Eves) and 1 opportunity for untrusted relays to intercept the information. In this paper, we aim to enhance the physical layer security of a cooperative network where a single‐antenna source intends to communicate with a single‐antenna destination in the presence of a group of untrusted relays and a passive eavesdropper. The objective is to protect the data confidentially while concurrently relying on the untrusted relays as potential Eves to improve both the security and reliability of the network. To realize this objective, we design a joint cooperative beamforming and cooperative jamming strategy. With the aim of maximizing the instantaneous secrecy rate, an optimal power allocation (OPA) scheme is proposed where the transmission powers of both jammer and source are optimized simultaneously under the total power budget constraint. To further improve the secrecy rate, a jammer selection strategy is also proposed where cooperative jamming is performed by the destination or one of the idle relays. For the proposed scheme, a closed‐form expression of beamformer vector is derived by solving a generalized eigenvalue problem. Finally, for the OPA problem, it is possible to be neither convex nor concave. In this regard, all possible cases are investigated to determine the OPA factor accurately. Numerical results illustrate that the proposed joint OPA–cooperative beamforming scheme, together with the most appropriate jammer selection strategy, significantly increases the secrecy rate.

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