Power line communication is a critical component of smart grids, which are vulnerable to eavesdropping. To address this challenge, we investigate a cooperative relay hybrid power line and wireless communication system where multiple eavesdroppers are considered. We propose an elaborate artificial noise (AN)-aided beamforming (BF) scheme to improve physical layer security. Our scheme maximizes the outage-constrained secrecy rate (OCSR) of the legitimate link while restricting the capacity of the eavesdroppers to a reasonable region. However, due to the imperfect channel state information of the wiretap channel and secrecy outage probability constraint, the robust OCSR problem becomes intractable because of the non-concave secrecy objective function and the non-convex constraints. To solve this issue, we utilize semidefinite programming and Bernstein-type inequality to transform the robust OCSR nonconvex problem into two convex sub-problems, which a block-coordinated descent algorithm can solve. Simulation results showcase the effectiveness of our robust AN-aided secure BF scheme and show that the proposed scheme outperforms the benchmark scheme in a security performance gain under various channel conditions, even in the worst case.

Motion artifacts compromise the quality of magnetic resonance imaging (MRI) and pose challenges to achieving diagnostic outcomes and image-guided therapies. In recent years, supervised deep learning approaches have emerged as successful solutions for motion artifact reduction (MAR). One disadvantage of these methods is their dependency on acquiring paired sets of motion artifact-corrupted (MA-corrupted) and motion artifact-free (MA-free) MR images for training purposes. Obtaining such image pairs is difficult and therefore limits the application of supervised training. In this paper, we propose a novel UNsupervised Abnormality Extraction Network (UNAEN) to alleviate this problem. Our network is capable of working with unpaired MA-corrupted and MA-free images. It converts the MA-corrupted images to MA-reduced images by extracting abnormalities from the MA-corrupted images using a proposed artifact extractor, which intercepts the residual artifact maps from the MA-corrupted MR images explicitly, and a reconstructor to restore the original input from the MA-reduced images. The performance of UNAEN was assessed by experimenting with various publicly available MRI datasets and comparing them with state-of-the-art methods. The quantitative evaluation demonstrates the superiority of UNAEN over alternative MAR methods and visually exhibits fewer residual artifacts. Our results substantiate the potential of UNAEN as a promising solution applicable in real-world clinical environments, with the capability to enhance diagnostic accuracy and facilitate image-guided therapies.

We propose a distributed artificial noise-assisted precoding scheme for secure communications over wiretap multi-input multi-output (MIMO) interference channels, where K legitimate transmitter-receiver pairs communicate in the presence of a sophisticated eavesdropper having more receive-antennas than the legitimate user. Realistic constraints are considered by imposing statistical error bounds for the channel state information of both the eavesdropping and interference channels. Based on the asynchronous distributed pricing model, the proposed scheme maximizes the total utility of all the users, where each user’s utility function is defined as the secrecy rate minus the interference cost imposed on other users. Using the weighted minimum mean square error, Schur complement and sign-definiteness techniques, the original non-concave optimization problem is approximated with high accuracy as a quasi-concave problem, which can be solved by the alternating convex search method. Simulation results consolidate our theoretical analysis and show that the proposed scheme outperforms the artificial noise-assisted interference alignment and minimum total mean-square error-based schemes.

This article focuses on the energy management problem in microgrid systems. Under the threat of climate change, energy management strategies should consider carbon emission issues in addition to economic benefits. Meanwhile, the uncertainty of renewable energy generation poses challenges to the reliability of energy management. Thus, this article proposes a robust low-carbon energy management scheme for economic maximization and carbon emission limitation and proposes a distributed optimization algorithm to obtain the optimal power output of participating units. The convergence of the algorithm is theoretically analyzed, and the effectiveness of the strategy is validated by numerical simulations on IEEE 39-bus systems. Simulation results show that the proposed energy management scheme can successfully maximize economic benefits while constraining system carbon emissions.

This study focuses on a topology identification problem of weighted networks with different connection strength, where binary time series generated by propagation dynamics are utilized. An influence probability matrix reflecting the weight of connection is proposed to quantify the influence of other nodes on one node as it transfers from susceptible state to infected state. Further, maximum likelihood estimate and expectation–maximization algorithm are used to obtain the influence probability matrix. A threshold method and a weight-based-identification algorithm are provided to identify connection strength. The robustness against fault data and conflicting results of the same connection is mitigated by introducing a confidence factor. Several Monte-Carlo simulations demonstrate the high identification accuracy of our methods under different network models.

In this paper, we investigate the physical layer security of a full-duplex decode-and-forward relay-aided system in the worst case, where all channel state informations are realistic imperfect. For the sake of confidentiality of signals transmitted from source to destination, a robust artificial noise (AN)-aided beamforming scheme is proposed. Explicitly, our objective function is that of maximizing the worst-case secrecy rate under the transmit power constraints, by jointly optimizing the beamforming matrix and AN at the source and the relay. To overcome the non-convexity of the robust AN-aided secure beamforming problem, we transform it into multi-block convex problems, where the semi-infinite linear matrix inequality is applied to eliminate the channel uncertainties. As a benefit, our proposed robust AN -aided secure beamforming scheme obtains substantial secrecy performance gains, which verifies the efficiency of the proposed scheme.

Millimeter wave (mmWave) communications and cognitive radio technologies constitute key technologies of improving the spectral efficiency of communications. Hence, we conceive a hybrid secure precoder for enhancing the physical layer security of a cognitive mmWave wiretap channel, where a secondary transmitter broadcasts confidential information signals to multiple secondary users under the interference temperature constraint of the primary user (PU). The optimization problem is formulated as jointly optimizing the analog and digital precoder for maximizing the minimum secrecy rate of all the secondary users under practical constraints. In particular, our design satisfies the constraint on the maximum interference power received by multiple PUs, as well as the secondary users’ minimum quality-of-service (Qos), and the unit-modulus constraint on the analog precoder. Due to the non-convexity of the resultant objective function and owing to the coupling between the analog and digital precoder, the optimization problem formulated is nonconvex and nonlinear, hence it is very challenging to solve directly. Hence, we first transform it into a tractable form, and develop a penalty dual decomposition (PDD) based iterative algorithm to locate its Karush-Kuhn-Tucker (KKT) solution. Finally, we generalize the proposed PDD algorithm to a secure hybrid precoder design relying on practical finite-resolution phase shifters and show that the proposed PDD algorithm can be straightforwardly adapted to handle the scenario, where each PU is equipped with multiple antennas and the CSI of multiple eavesdroppers (Eves) is imperfectly known. Our simulation results validate the efficiency of the proposed iterative algorithm.

Spatial Interference alignment(IA) is a promising scheme to efficiently mitigate interference and to enhance the capacity of a wireless communication network. In this paper, the sufficient conditions of spatial interference alignment operating under a realistic limited feedback are provided for a K-user MIMO interference channel. The transmit precoder's matrix index is fed back to the corresponding transmitter through an error-free non-interfered link. We investigate the number of feedback bits required for achieving the maximum theoretical multiplexing gain for the spatial interference alignment schemes considered and demonstrate the feasibility of spatial interference alignment under the limited feedback constraint investigated. It is shown that in order to maintain the same spatial multiplexing gain as that of the idealized scheme relying on perfect channel state information, the number of feedback bits per receiver scales as Nd≥ di(M - di) log2SNR, where M and di denote the number of transmit (receive) antennas and the number of data steams for user i. Finally, the analytical results are verified by simulations for realistic practical interference alignment schemes relying on limited precoding matrix feedback indices.

In this paper, we investigate the physical layer security of a full-duplex base station (BS)-aided system in the worst case, where an uplink transmitter (UT) and a downlink receiver (DR) are equipped with a single antenna, while a powerful eavesdropper is equipped with multiple antennas. For securing the confidentiality of signals transmitted from the BS and UT, an artificial noise (AN)-aided secrecy beamforming scheme is proposed, which is robust to the realistic imperfect state information of both the eavesdropping channel and the residual self-interference channel. Our objective function is that of maximizing the worst-case sum secrecy rate achieved by the BS and UT, through jointly optimizing the beamforming vector of the confidential signals and the transmit covariance matrix of the AN. However, the resulting optimization problem is non-convex and non-linear. In order to efficiently obtain the solution, we transform the non-convex problem into a sequence of convex problems by adopting the block coordinate descent algorithm. We invoke a linear matrix inequality for finding its Karush-Kuhn-Tucker (KKT) solution. In order to evaluate the achievable performance, the worst-case secrecy rate is analytically derived. Furthermore, we construct another secrecy transmission scheme using the projection matrix theory for performance comparison. Our simulation results show that the proposed robust secrecy transmission scheme achieves substantial secrecy performance gains, which verifies the efficiency of the proposed method.

In this paper, we consider the problem of jointly designing transmit precoding (TPC) matrix and receive filter matrix subject to both secrecy and per-transmitter power constraints in the multiple-input multiple-output (MIMO) interference channel, where K legitimate transmitter-receiver pairs communicate in the presence of an external eavesdropper. Explicitly, we jointly design the TPC and receive filter matrices based on the minimum total mean-squared error (MSE) criterion under a given and feasible information-theoretic degrees of freedom. More specifically, we formulate this problem by minimizing the total MSEs of the signals communicated between the legitimate transmitter-receiver pairs, while ensuring that the MSE of the signals decoded by the eavesdropper remains higher than a certain threshold. We demonstrate that the joint design of the TPC and receive filter matrices subject to both secrecy and transmit power constraints can be accomplished by an efficient iterative distributed algorithm. The convergence of the proposed iterative algorithm is characterized as well. Furthermore, the performance of the proposed algorithm, including both its secrecy rate and MSE, is characterized with the aid of numerical results. We demonstrate that the proposed algorithm outperforms the traditional interference alignment algorithm in terms of both the achievable secrecy rate and the MSE. As a benefit, secure communications can be guaranteed by the proposed algorithm for the MIMO interference channel even in the presence of a sophisticated/strong eavesdropper, whose number of antennas is much higher than that of each legitimate transmitter and receiver.

We employ a multiuser detection (MUD) method using a novel genetic algorithm (GA) based on complementary error function mutation (CEFM) and a differential algorithm (DA) for ultra-wideband (UWB) systems. The proposed MUD method is termed CEFM-GA DA for short. We describe the scheme of CEFM-GA DA, analyze its algorithm, and compare its computational complexity with other MUDs. Simulation results show that a significant performance gain can be achieved by employing the proposed CEFM-GA DA, compared with successive interference cancellation (SIC), parallel interference cancellation (PIC), conventional GA, and CEFM-GA without DA, for UWB systems in lognormal fading channel. Moreover, CEFM-GA DA not only reduces computational complexity relative to conventional GA and CEFM-GA without DA, but also improves bit error rate (BER) performance.

We propose an efficient low bit error rate (BER) and low complexity multiple-input multiple-output (MIMO) multiuser detection (MUD) method for use with multiuser MIMO orthogonal frequency division multiplexing (OFDM) systems. It is a hybrid method combining a multiuser-interference-cancellation-based decision feedback equalizer using error feedback filter (MIMO MIC DFE-EFF) and a differential algorithm. The proposed method, termed ‘MIMO MIC DFE-EFF with a differential algorithm’ for short, has a multiuser feedback structure. We describe the schemes of MIMO MIC DFE-EFF and MIMO MIC DFE-EFF with a differential algorithm, and compare their minimum mean square error (MMSE) performance and computational complexity. Simulation results show that a significant performance gain can be achieved by employing the MIMO MIC DFE-EFF detection algorithm in the context of a multiuser MIMO-OFDM system over frequency selective Rayleigh channel. MIMO MIC DFE-EFF with the differential algorithm improves both computational efficiency and BER performance in a multistage structure relative to conventional DFE-EFF, though there is a small reduction in system performance compared with MIMO MIC DFE-EFF without the differential algorithm.