Radio frequency (RF) fingerprinting is regarded as a promising solution to improve wireless security, especially in applications where resource-limited devices are employed. Unlike steady-state signals, such as preambles or data, the use of short-duration transient signals for RF fingerprinting offers distinct advantages for systems with low latency and low complexity requirements. One of the challenges associated with transient-based methods in practice is achieving high performance while utilizing low-cost receivers. In this study, we demonstrate for the first time that the performance of transient-based RF fingerprinting can be enhanced by designing the filter chain in a software defined radio (SDR) receiver, taking into account the relevant signal characteristics. The performance analysis is conducted using transient-based identification signals captured by the SDR receiver, focusing on the sampling rate and duration of the identification signal. In the experiments, signals collected from twenty IEEE 802.11 transmitters are used. Experimental results indicate that so long as the receiver filter parameters and the duration of the identification signal are properly determined, a high classification performance exceeding 92% can be achieved for transient-based RF fingerprinting, even at sampling rates approaching the Nyquist limit.

In addressing the multifaceted problem of multiple-input multiple-output (MIMO) detection in wireless communication systems, this work highlights the pressing need for enhanced detection reliability under variable channel conditions and MIMO antenna configurations. We propose a novel method that sets a new standard for deep unfolding in MIMO detection by integrating the iterative conjugate gradient method with Tikhonov regularization, combining the adaptability of modern deep learning techniques with the robustness of classical regularization. Unlike conventional techniques, our strategy treats the Tikhonov regularization parameter, as well as the step size and search direction coefficients of the conjugate gradient (CG) method, as trainable parameters within the deep learning framework. This enables dynamic adjustments based on varying channel conditions and MIMO antenna configurations. Detection performance is significantly improved by the proposed approach across a range of MIMO configurations and channel conditions, consistently achieving lower bit error rate (BER) and normalized minimum mean square error (NMSE) compared to well-known techniques like DetNet and CG. The proposed method has superior performance over CG and other model-based methods, especially with a small number of iterations. Consequently, the simulation results demonstrate the flexibility of the proposed approach, making it a viable choice for MIMO systems with a range of antenna configurations, modulation orders, and different channel conditions.

This study addresses the critical challenges in Vehicle-to-Vehicle (V2V) communications, particularly the issues of high mobility, dynamic channel conditions, and interference that adversely affect Bit Error Rate (BER) performance and overall system capacity. We propose an innovative integration of NOMA with a specific form of Quasi Cyclic-Low Density Parity Check (QC-LDPC) coding to enhance communication reliability and efficiency in vehicular environments, ensuring effective system operation even without clustering. Analytical and numerical simulations based on the NOMA-MIMO system are conducted to derive BER formulas for both near (Bob₁) and far (Bob₂) users. The effectiveness of Successive Interference Cancellation (SIC) techniques in mitigating interference in vehicular environments is also explored. Additionally, mathematical formulations for outage probability in NOMA-MIMO systems indicate that outage probability decreases with increased transmit power as users get closer to the base station. Our research demonstrates that QC-LDPC codes without Girth₄ cycles significantly enhance error correction capabilities and achieve higher spectral efficiency over Rician fading channels. OFDMA-LDPC and SM-NOMA (Spatial Multiplexing-Non Orthogonal Multiple Access) based vehicular communication systems are also compared, emphasizing the impact of 2×2 and 4×4 MIMO configurations on BER and capacity. The results highlight NOMA’s superiority and the simplified structure of QC-LDPC, resulting in enhanced system performance in dynamic vehicular communication scenarios. Moreover, increasing the antennas at both ends leads to further performance improvements, with results achieved at a code rate 1/3 among the benchmarked schemes. The Sum-Product algorithm is often employed as a preferred decoding method in high-mobility scenarios due to its efficiency and simplicity, which enhance communication reliability and system capacity. This research also addresses the challenges associated with high vehicle velocities and the resulting Doppler shifts, which are significant in highway and urban scenarios. The mentioned effects were particularly pronounced at a frequency of 0.2 THz and a speed of 120 km/h, impacting signal quality and reliability.

Applications of deep learning in communications systems are becoming popular today with their powerful solutions to complex problems. This study considers the utilization of deep learning detectors for small-scale multiple-input multiple-output systems. Deep neural network, long short-term memory, and one-dimenisonal convolutional neural network architectures are discussed and the bit error rate performances of these deep learning based detectors are compared with the optimal maximum likelihood and sub-optimal minimum mean square error detectors. Simulation results show that the deep neural network architecture has the best detection performance among the discussed deep learning detectors and may outperform the sub-optimal minimum mean square error detector. For small-scale multiple-input multiple-output systems, the performance of the deep learning based detector is close to that of the optimal detector.

In addressing the multifaceted problem of multiple-input multiple-output (MIMO) detection in wireless communication systems, this work highlights the pressing need for enhanced detection reliability under variable channel conditions and MIMO antenna configurations. We propose a novel method that sets a new standard for deep unfolding approaches to MIMO detection by integrating the iterative conjugate gradient method with Tikhonov regularization, combining the adaptability of modern deep learning techniques with the robustness of classical regularization. Unlike conventional techniques, our strategy treats the regularization parameter of Tikhonov regularization as well as step size values and search direction coefficients of conjugate gradient (CG) method as trainable parameters within the deep learning framework, allowing for dynamic modification according to channel conditions and MIMO antenna configurations. Detection performance is significantly improved by the proposed approach in variety of conditions. In different MIMO settings, the suggested method consistently shows better bit error rate (BER) and normalized minimum mean square error (NMSE) performance. Across a range of MIMO configurations and channel conditions, the proposed method exhibits significantly lower BER and NMSE values than well-known techniques such as DetNet and CG. The proposed method has superior performance over CG and other model-oriented methods, especially in small number of iterations. Consequently, the simulation results demonstrate the flexibility of the proposed approach, making it a viable choice for MIMO systems with a range of antenna configurations and different channnel conditions.