Industrial IoT-enabled critical infrastructures are susceptible to cyber attacks due to their mission-critical deployment. To ensure security by design, radio frequency (RF)-based security is considered an effective way for wirelessly monitored or actuated critical infrastructures. For this purpose, this paper presents a novel augmentation-driven deep learning approach to analyze unique transmitter fingerprints and determine the legitimacy of a user device or transmitter. An RF fingerprinting model is susceptible to various channel and environmental conditions that impact the learning performance of a machine/deep learning model. As data gathering cannot always be considered a feasible alternative, efficient solutions that can tackle the impact of varying propagation channels on learning performance are emergent. This work aims to shed light on the RF fingerprinting problem from a different angle when 4G, 5G, and WiFi data samples are collected from different transmitters by proposing a fine-grained augmentation approach to improve the learning performance of a deep learning model. This work also proposes an enhanced classifier structure following the fine-grained augmentation approach. Results of experiments, conducted on the POWDER dataset, demonstrate promising RF fingerprinting performance when training data are augmented in a waveform-specific fine-grained manner. Thus, the RF identification accuracy can be boosted to 97.84% on unseen RF data instances from our previously published work where we had achieved an accuracy of 87.94% using tapped delay line (TDL)/clustered delay line (CDL)-based augmentation approach. The paper also presents a sensitivity analysis of the fine-grained approach concerning different signal-to-noise-ratio (SNR), signal-to-interference-ratio (SIR) levels (20 dB and 30 dB), and signal-to-interference-plus-noise-ratio (SINR) levels (15 dB, 25 dB). The sensitivity analysis exhibits that it achieves 85.78% accuracy at 20 dB SIR on both Day 1 (train) and Day 2 (test) data. In addition, it achieves 92.37% accuracy even at 20 dB SNR on Day 2 data from POWDER dataset. Furthermore, it achieves 84.95% accuracy at 15 dB SINR on Day 2 data. Hence, these results exhibit the resiliency of the fine-grained augmentation approach against interference and noise.

In millimeter-wave (mmWave) systems, hybrid precoding/combining architectures are an attractive low-complexity alternative to the use of fully digital precoding. This is because of the reduction in the number of Radio-Frequency (RF) and mixed-signal hardware components. Hybrid precoding/combining design involves a combination of analog and digital processing that enables both beamforming and spatial multiplexing gains in mmWave systems. This paper presents an algorithm for hybrid precoding and combining designs in mmWave systems. The proposed design does not depend on the antenna array geometry, unlike the orthogonal matching pursuit (OMP) hybrid precoding/combining approach. Moreover, this algorithm allows hybrid precoders/combiners to perform close to the known optimal performance of unconstrained digital precoders and combiners. Simulation results verify that the proposed hybrid precoding/combining scheme outperforms the previous methods in the literature in terms of bit error rate (BER) and achievable spectral efficiency with lower complexity.

Hybrid precoding and combining for millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems with subarray (SA) architecture is a promising technology for 6G because of their low complexity, cost, and power consumption compared to the full array (FA) architecture. This paper proposes an iterative algorithm for designing hybrid precoding and combining for the SA architecture. It is called direct conversion of iterative hybrid precoding and combining from FA to SA (DCIFS). The proposed algorithm involves an iterative process that begins by designing a hybrid precoding and combining matrix for the FA structure and then converts it into an SA matrix by setting certain entries to zero while achieving better performance. It does not depend on the antenna array geometry, unlike other techniques such as the orthogonal matching pursuit (OMP) hybrid precoding and combining approach. We investigate the proposed algorithm with two scenarios. In the first scenario, we use the proposed DCIFS scheme only at the base station (BS) and the iterative FA hybrid scheme at the mobile station (MS), whereas in the second, we use the proposed DCIFS scheme at both the BS and the MS. Simulation results demonstrate that the proposed design approach achieves a spectral efficiency comparable to that of the FA hybrid design counterpart, especially for a large system, while maintaining low complexity. For example, when SNR= 0 dB and the number of transmitted streams ( N_s ) = 2, the proposed algorithm provides about 1.5 bps/Hz spectral efficiency gain compared to the OMP hybrid design for the first scenario. Moreover, when the number of iterations is low and the number of BS antenna and N_s is high, the proposed approach outperforms the conventional SA hybrid design in terms of spectral efficiency with the same hardware complexity.

In this letter, we investigate the sequence estimation problem of faster-than-Nyquist (FTN) signaling as a promising approach for increasing spectral efficiency (SE) in future communication systems. In doing so, we exploit the concept of Gaussian separability and propose two probabilistic data association (PDA) algorithms with polynomial time complexity to detect binary phase-shift keying (BPSK) FTN signaling. Simulation results show that the proposed PDA algorithm outperforms the recently proposed SSSSE and SSSgbKSE algorithms for all SE values with a modest increase in complexity. The PDA algorithm approaches the performance of the semidefinite relaxation (SDRSE) algorithm for SE values of 0.96 bits/sec/Hz, and it is within the 0.5 dB signal-to-noise ratio (SNR) penalty at SE values of 1.10 bits/sec/Hz for the fixed values of β = 0.3.

This paper proposes a novel approach to enhancing secure wireless communication using a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) in a multipleinput single-output system. Unlike conventional RIS, which is limited to half-space coverage, STAR-RIS enables full 360-degree coverage, making it a promising technology for next-generation secure wireless communication. In the presence of energy-harvesting eavesdroppers, this study aims to maximize the sum secrecy rate while ensuring strict energy harvesting constraints, an area that has not been widely explored, particularly under imperfect channel state information (CSI) conditions. To tackle this challenge, we formulate a complex non-convex optimization problem, which is efficiently solved using a penalty concave-convex procedure combined with an alternating optimization algorithm. This optimization framework jointly optimizes beamforming and STAR-RIS transmission and reflection coefficients, striking a balance between secure communication and energy harvesting requirements. Numerical simulations show that the proposed method outperforms conventional RIS-based approaches, demonstrating its effectiveness even in the presence of CSI uncertainty. Furthermore, the results confirm that optimizing STAR-RIS under imperfect CSI leads to superior security and energy efficiency compared to assuming perfect CSI, highlighting the practical significance of our approach in real-world wireless networks.

Unmanned aerial vehicles (UAVs) are expected to become a major component of the beyond fifth-generation (5G) cellular networks in order to provide ubiquitous connectivity. Recently, new statistical fading channel models for UAV-to-ground communications have been introduced to represent shadowing effects in the channel. In this paper, we derive the average bit error rate (ABER), ergodic capacity, and outage probability of a receiver using maximal ratio combining (MRC) operating in independent UAV-to-ground channels based on the moment generating function (MGF). Moreover, the effects of the UAV-to-ground fading model parameters on the system performance have been illustrated via numerical results. By using Monte Carlo simulations, we finally verify all the theoretical expressions.