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.

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.

Digital compensation of nonlinear systems is an important topic in many practical applications. This paper considers the problem of predistortion of nonlinear systems described using Volterra series by connecting in tandem an adaptive Volterra predistorter. The suggested direct learning architecture (DLA) approach utilizes the spectral magnitude matching (SMM) method that minimizes the sum squared error between the spectral magnitudes of the output signal of the nonlinear system and the desired signal. The coefficients of the predistorter are estimated recursively using the generalized Newton iterative algorithm. A comparative simulation study with the nonlinear filtered-x least mean squares (NFxLMS) algorithm shows that the suggested SMM approach achieves much better performance but with higher computation complexity.