This paper presents a novel group secret key generation (GSKG) protocol for multi-user wireless systems with a star topology. In contrast to conventional methods relying on pairwise key derivation followed by reconciliation, the proposed approach enables the central node to directly select a group secret key uniformly at random across the full quantization space. This eliminates the need for intermediate pairwise keys, significantly reducing computational complexity, communication overhead, and latency. To ensure consistent group key reconstruction, we introduce two lightweight, non-coding reconciliation techniques: One-Sided Centering (OSC) and Quantization Pattern Alternation (QPA). Unlike coding-based reconciliation in conventional schemes, OSC and QPA allow the central node to transmit helper data – derived from adjusted measurements or shifted quantization grids – to peripheral nodes. This helper data not only aligns their channel observations with the selected quantization region, but also implicitly distributes the group key. Thus, reconciliation and distribution are seamlessly integrated into a unified protocol. The proposed GSKG framework supports both fully and partially cooperative scenarios and remains robust against unreliable or compromised nodes by selectively omitting or perturbing helper data. Furthermore, a concatenation-based group key construction method exploits existing bidirectional channel measurements to generate multiple key segments, enhancing throughput without additional channel probing. Extensive simulations validate the scheme’s performance in terms of key disagreement rate, key generation rate, computational efficiency, and robustness against eavesdropping. The results confirm that the protocol offers high scalability and security, making it a practical and efficient solution for group key establishment in IoT and other resource-constrained wireless applications.

This article presents a novel low-complexity deep learning model for physical-layer secret key generation (PSKG), specifically designed to enhance wireless security in the Internet of Things (IoT). PSKG typically generates cryptographic keys by exploiting the reciprocal nature of wireless channels; however, this reciprocity is frequently compromised in time-division duplex (TDD) systems due to hardware imperfections and noise, which significantly complicate the key generation process. To effectively address these challenges, the proposed deep neural network (DNN) is developed to efficiently learn and enhance reciprocity features, even in the presence of imperfect channel state information (CSI). Furthermore, the study introduces a DNN-based PSKG method that strategically leverages the phase of channel frequency responses for key extraction. The results conclusively demonstrate that the proposed model substantially reduces the key disagreement ratio (KDR) and enhances randomness, thus providing a robust and practical solution for securing wireless communications in IoT environments.

Physical Layer Secret Key Generation (PSKG) exploits wireless channel reciprocity to establish cryptographic keys between legitimate users, providing a promising solution for securing IoT networks. However, reciprocity in time-division duplex (TDD) systems is often compromised by hardware imperfections and channel noise, posing significant challenges for reliable key generation. To address these issues, this paper proposes an autoencoder-based noise augmentation (AE-NA) model that operates in an unsupervised manner. Specifically, the proposed model integrates dynamic Gaussian noise augmentation directly into the autoencoder architecture. This approach enhances feature extraction capabilities and improves noise robustness, enabling the network to learn noise-invariant representations effectively from noisy channel estimates. Furthermore, we present a comprehensive PSKG framework that incorporates the proposed AE-NA model. Reported results demonstrate that the AE-NA-based PSKG significantly reduces the key disagreement ratio (KDR) while enhancing key randomness, thereby providing a scalable and effective solution for secure key generation in dynamic IoT environments.

Physical Layer Secret Key Generation (PLSKG), which utilizes wireless channel reciprocity, is increasingly adopted to secure wireless communications. In Time Division Duplexing (TDD) systems, the inherent channel reciprocity facilitates the generation of cryptographic keys without requiring a key exchange process. Conversely, Frequency Division Duplexing (FDD) systems present greater challenges for key generation due to the use of distinct frequency bands for uplink and downlink, resulting in differing frequency responses. In this paper, we address this challenge by modeling the channel along with connecting cables as a two-port network and utilizing the reciprocity principles applicable to such networks within the same frequency range. For key generation, we construct the channel profile for each FDD band by utilizing the amplitude of the bidirectional scattering parameters S12and S21in two closely spaced FDD bands, thereby ensuring the required reciprocity in FDD systems. To assess the practicality of the proposed scheme, we analyze its performance across various indoor scenarios and compare it with other key generation schemes using multiple performance indicators. The comparative results confirm the feasibility and effectiveness of our scheme in terms of randomness and key disagreement ratio.

This article presents a secure spatial modulation (SM) scheme designed to protect communications within resource-constrained Internet of Things (IoT) networks. The proposed scheme leverages channel state information (CSI) and channel reciprocity in time-division duplex (TDD) communications, offering a robust security solution without demanding high computational resources. The proposed model generates two independent shuffling vectors, strategically derived from the channel’s power and phase characteristics. These vectors are utilized to permute both the signal and space constellation diagrams, disrupting eavesdropping attempts. The reported results affirm the scheme’s effectiveness. Legitimate receivers consistently achieve a low average bit error ratio (ABER), ensuring reliable information decoding. In contrast, potential eavesdroppers face significantly higher ABER, consistently around 0.5 across various signal-to-noise ratio (SNR) levels, numbers of transmit antennas, and modulation orders. Furthermore, the complexity analysis of the proposed model confirms its suitability for resource-constrained IoT devices.

This article presents a complete system model implementation of a digital communication system using Universal software radio peripheral (USRP) and Matlab. In particular, a software defined radio (SDR)-transceiver is implemented in Matlab and configured on the USRP, where several tests and measurements are performed. The deployed system allows investigating the impact of wireless channel impairments including amplitude attenuation, phase shift, time delay, and frequency offset on the overall system performance. As well, synchronization algorithms for symbol synchronization, carrier synchronization, and frame synchronization are presented and evaluated. Reported results reveal accurate implementation and robust design of variant digital communication systems. Results for 16, 64, and 256-quadrature amplitude modulation (QAM) are presented and discussed. As well, reported measurement for the bit error rate (BER) reveal accurate matching with theoretical results over Rician fading channel with a Rician K-factor of 10. Besides, the impact of the pulse shaping parameters and over sampling ratio on the overall system performance is illustrated and discussed.

This paper proposes a combination between quadrature spatial modulation (QSM) multiple-input multiple-output (MIMO) system and orthogonal frequency division multiplexing (OFDM) aiming to benefit from their superior inherent advantages. The proposed system uses multiple transmit antennas and applies QSM technique on the different OFDM sub-carriers transmitted from the multiple antennas. Therefore, at each frequency sub-carrier, one antenna will be transmitting a real part of the modulated symbol and another or the same antenna will be transmitting the imaginary part of that symbol. As such, many of the transmitted sub-carriers in one OFDM symbol will be un-modulated and transmitting no data, which promises major reduction in the peak to average power ratio (PAPR). Typical OFDM systems generally suffer from high PAPR, which causes major performance degradation in the presence of high power amplifier (HPA) nonlinearities. It is revealed, in this paper, that the proposed system achieves superior performance as compared to it's spatial modulation and spatial multiplexing counterpart systems. Analytical analysis on the overall bit error probability is provided and corroborated through Monte Carlo simulation results.