Index modulation-based orthogonal frequency division multiple access (IM-OFDMA) is an uplink scheme that has been recently proposed as a promising solution for future wireless communications systems. It has been shown to beat conventional OFDMA in terms of the error performance and the number of users served. Thus, more attention and research efforts should be directed to analyze IM-OFDMA from the different performance aspects. In this letter, the performance of the IM-OFDMA scheme is analyzed considering well-known practical impairments represented by the imbalance between the in-phase (I) and quadrature (Q) components in direct conversion receivers. The error rate is analyzed and quantified in the IM-OFDMA scheme considering the IQ-imbalance, and compared to the conventional OFDMA scheme. Results show that IM-OFDMA can afford a better performance than OFDMA considering different scenarios of the number of users, the number of subcarriers, and spectral efficiency values.

Index modulation-based orthogonal division multiple access (IM-OFDMA) has recently been proposed as a potential technique for future wireless communication systems due to its superior spectral efficiency and error performance over conventional multiple access schemes. However, its performance is still under investigation by researchers in light of a variety of scenarios and assumptions. Following this direction, in this paper, the individual and joint effects of the transmitter (Tx) and receiver (Rx) in-phase and quadrature imbalances (IQI) on the bit-error-rate (BER) performance of the uplink IM-OFDMA scheme are investigated by considering whether each user's RF front-end is identical or not. Moreover, to reduce the detrimental effect of IQI, a preamble-based estimation and compensation method is proposed for IM-OFDMA. Closed-form expressions for the average BER of IM-OFDMA are obtained by considering the physical effect of the Tx and Rx IQI. Also, using Monte Carlo simulations, the derived expressions are verified under different system configurations. Analytical and simulation results reveal that Tx and Rx IQI cause an error floor in the BER performance of IM-OFDMA schemes. However, the proposed estimation and compensation method not only reduces the impact of IQI but also leads to a better BER performance compared to the case of no IQI, which mainly refers to the frequency diversity caused by the IQI.

This study proposes a novel secure uplink index modulation-based orthogonal frequency division multiple access (IM-OFDMA) systems using artificial in-phase and quadrature imbalance (A-IQI). The fact of the distinct A-IQI induced at the users’ end and the unpredictable allocation of users based on confidential data brings a different perspective on physical layer security. Accordingly, in this study, the non-identical effect of A-IQI is exploited to provide a physical-layer security scheme for the IM-OFDMA systems. Specifically, the estimation method is created with preamble data in order to achieve each user’s unique A-IQI information from a legitimate receiver. The A-IQI will help to secure the transmitted data against a potential eavesdropper, and hence, improving data confidentiality. The proposed scheme’s performance is analytically evaluated by deriving closed-form expressions of the average bit error rate (BER) at both the base station and the eavesdropper. The image rejection ratios of A-IQI and practical IQI are provided for IM-OFDMA to indicate the region of intentionally generated A-IQI. Under the different system model specifications of IM-OFDMA, the analytical results are provided with simulation results obtained using the Monte Carlo simulation method. Further, the proposed secure scheme is compared with the state-of-the-art artificial noise method. Results reveal a significant improvement in the BER at the base station, accompanied by severe degradation in the BER at the eavesdropper.

This paper introduces the secrecy analysis of the index modulation-based orthogonal frequency division multiple access (IM-OFDMA) schemes by considering the in-phase and quadrature imbalance (IQI) presence at the users' RF front ends. In particular, by assuming the existence of an eavesdropper, the secrecy performance of the considered uplink multiple-access system is investigated through the strictly positive secrecy capacity and secrecy outage probability, and closed-form expressions are derived for both quantities. Moreover, the secrecy performance is conducted regarding IQI compensation based on a preamble-based estimation method. Further, analysis of the impact of the different parameters of IM-OFDMA, IQI, and estimation method is included via analytical and simulation-based results. The results reveal that IM-OFDMA is more reliable in secrecy performance than OFDMA, regardless of whether IQI is present. Also, IQI compensation can lead to a better secrecy performance compared to the case of no IQI.

This study proposes a novel method for signal detection and feature extraction based on the spectral correlation function, enabling improved characterization of grid-signal distortions. Our approach differs from existing treatments of signal distortion in its analysis of the varied spectral content of signals observed in real-world scenarios. The method we propose has state-of-the-art discriminative power that provides meaningful and understandable characterizations of various grid events and anomalies. To validate the approach, we use real world data from the Grid Event Signature Library, which is maintained jointly by Oak Ridge National Laboratory and Lawrence Livermore National Laboratory.

Rapid and accurate identification of events in power grids is critical to ensuring system reliability and security. This study introduces a novel event-type identification method, utilizing a Spectral Correlation Function (SCF)-aided Convolutional Neural Network (CNN). The proposed method employs a six-stage cascaded structure consisting of: (1) data collection, (2) clipping, (3) augmentation, (4) feature extraction (FE), (5) training, and (6) testing. Real-world power grid signals sourced from the Grid Event Signature Library are used for both training and testing. To improve robustness, additive white Gaussian noise (AWGN) is introduced at various signal-to-noise ratio (SNR) levels to augment the dataset. The SCF-based FE method captures distinctive event-type characteristics by exploiting the spectral correlation of signals, allowing the CNN architecture to effectively learn and generalize event patterns. The proposed method is benchmarked against seven conventional techniques, using real-world power grid signals representing four distinct event types: blown fuse, line switching, low amplitude arcing, and transformer energization. Key performance metrics-prediction accuracy, mean absolute error (MAE), precision, recall, F1-score, and confusion matrix—are employed to evaluate the performance. Results demonstrate that the SCF-CNN method outperforms traditional approaches across all metrics and SNR levels, achieving over 99% prediction accuracy and nearly zero error for SNR values above 6 dB. This signifies its efficacy in reliable event-type identification for power grid applications.