Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) have emerged as a promising technology for enabling full-space signal manipulation and enhancing wireless network coverage and capacity. In this article, we present a comprehensive analytical comparison of STAR-RIS-assisted systems with single-input single-output (SISO), conventional RISs, and decode-and-forward (DF) relaying schemes, including both half-duplex (HD) and full-duplex (FD) modes. Closed-form expressions are derived for the achievable secrecy rates of STAR-RIS-aided communications under both the absence and presence of eavesdroppers. Unlike most existing works, the direct source–destination link is incorporated in all considered schemes, and optimal transmit power allocation is investigated for HD- and FD-DF relaying. Furthermore, we provide the conditions under which STAR-RIS outperforms HD- and FD-DF relaying and quantify the minimum number of STAR-RIS elements required to achieve superior rates. The impacts of key system parameters—including transmit power, number of elements, reflection and transmission amplitude coefficients, element-splitting factor, and deployment positions—on both achievable and secrecy performance are investigated. The results reveal that STAR-RIS systems can achieve superior rates and secrecy rates compared to all benchmark schemes.

This paper investigates the secure communication capabilities of a non-orthogonal multiple access (NOMA) network supported by a STAR-RIS (simultaneously transmitting and reflecting reconfigurable intelligent surface) deployed on an unmanned aerial vehicle (UAV), in the presence of passive eavesdroppers. The STAR-RIS facilitates concurrent signal reflection and transmission, allowing multiple legitimate users-grouped via NOMA-to be served efficiently, thereby improving spectral utilization. Each user contends with an associated eavesdropper, creating a stringent security scenario. Under Nakagami fading conditions and accounting for phase shift inaccuracies in the STAR-RIS, closed-form expressions for the ergodic secrecy rates of users in both transmission and reflection paths are derived. An optimization framework is then developed to jointly adjust the UAV's positioning and the STAR-RIS power splitting coefficient, aiming to maximize the system's secrecy rate. The proposed approach enhances secure transmission in STAR-RIS-NOMA configurations under realistic hardware constraints and offers valuable guidance for the design of future 6G wireless networks.

This paper reports on findings on the analysis of the fast Fourier transform (FFT) hybrid power line communication (PLC) - visible light communication (VLC) system (PLC- VLC) based on ACO-OFDM in impulsive noise, when the perfect nulling technique is utilized to combat noise in the system. In this work, we develop theoretical expressions that describe the error floors realized in the system due to perfect nulling. Perfect nulling refers to a noise mitigation technique that perfectly identifies impulsive noise locations in a string of data samples and clips them to zero, leading to an error floor at a high Signal to Noise (SNR) ratio. Thus, the principal contribution of this paper is to analyze and derive theoretical Bit Error Rate (BER) expressions for the error floor achieved by a BPSK OFDM signal in the FFT hybrid PLC-VLC system based on asymmetrically clipped optical - orthogonal frequency division multiplexing (ACO-OFDM), in a channel corrupted with Added White Gaussian Noise (AWGN) and impulsive noise. The derived theoretical BER results will be compared to the error floors obtained through simulations.

This paper investigates the use of machine learning (ML) in predicting and estimating the impulse noise. Four ML's algorithms (Multilayer perceptron MLP, support vector machine SVM, k nearest neighbour kNN and naïve Bayesian classifier NBC) were implemented in an OFDM system affected by impulse noise. The impulse noise model used was the Middleton Class A noise model. The ML's were trained with Middleton Class A impulse noise model so that they can be able to predict the presence of impulse noise in the communication system. In terms of prediction accuracy, results showed that kNN slightly outperformed MLP and NBC and accomplished high prediction accuracy of 99.8%. SVM achieved the lowest prediction accuracy among the four used methods. These results indicates that machine learning could be used to estimate impulse noise in OFDM communications system.

This paper puts in Cascade the power line communications (PLC) channel and the visible light communications (VLC) channel, in order to use the PLC channel as backbone for the VLC channel. This combination is suitable for applications in which hybrid PLC-VLC systems are needed. We investigate the behavior of the cascaded channels for a full link transmission. Quadrature phase shift keying combined with orthogonal frequency division multiplexing (QPSK-OFDM) is used over the PLC channel and color shift keying (CSK) is deployed over the VLC channel to convey the information. Cascaded channel variances are analyzed. Complete simulated bit error rate (BER) is analyzed and presented for multiple scenarii that could occur in the two channels.

For communication schemes employing Frequency Hopping/Multiple Frequency Shift Keying modulation, we present an algorithm for finding good non-binary synchronization sequences, which are permutations, to be used with permutation codes to synchronize/resynchronize data in channels with background noise and interference(frequency jamming/fading). For the synchronization sequences, new analytical expressions for the probability of false acquisition are also given. Using simulation results, we show that our synchronization sequences perform better than some conventional non-binary synchronization sequences, in the presence of background noise and interference.