The performance of the sixth-generation (6G) vehicle-to-vehicle (V2V) communication systems will be significantly improved, but they are also confronted with many technical challenges like massive terminal access and low transmission delay. A fundamental and difficult problem is how to establish an intelligent 6G V2V channel model with high accuracy, low complexity, and generality. In this paper, we propose a dynamic V2V channel model in complicated urban scenarios driven by effective environment information, channel data, and physical statistics. To begin with, the bimodal features representing the environment information are extracted from vector maps by a set of fully automatic algorithms. Heuristic graph datasets are constructed using features coupled with locations and ground-truth large-scale parameters (LSPs), i.e., the channel data reflecting realistic statistical properties. Then, we design a novel network based on attention-assisted graph convolution and pooling layers, which enables us to perform prediction for path loss, delay spread, and angular spreads. Compared with convolutional neural networks-based methods, the proposed LSPs prediction model can reduce both the number of trainable parameters and the FLOPs by two orders of magnitude with higher accuracy. Moreover, the predicted LSPs are next fed into multi-link V2V simulations based on physical statistics. Dynamic channel impulse response generation is implemented based on a spatially consistent geometrical modeling methodology. Eventually, we validate our model by comparing key channel characteristics with those of the ground-truth values, and better agreements are shown compared with existing methods.

Long-range wide-area network (LoRaWAN) is a widely used technology in the Internet of Things (IoT), which provides long-range (LoRa) communication with low power consumption. In LoRaWAN, an accurate path loss (PL) model is essential to realize link budget and network coverage planning. In this article, we present an artificial intelligence (AI)-enhanced generalizable scheme for PL prediction in LoRaWAN. We propose a network that performs corrective adjustments to improve the PL estimates of empirical models. The network termed STransRadio benefits from the self-attention computation in Swin Transformer to model the LoRa correlation about propagation for enhancing the adjustment prediction accuracy. To generalize our scheme to new scenarios, an multiscenario deep transfer learning (MDTL) algorithm is proposed, which finetunes the pretrained STransRadio network with limited data. We conduct simulations and measurements in the 868-MHz bands to assess the performance of the scheme in terms of prediction accuracy and generalization ability. The effectiveness of the proposed scheme has been verified with both simulations and measurements. Moreover, the STransRadio network in the scheme outperforms the convolutional neural network (CNN) and deep vision transformer (DeepViT). With the MDTL algorithm, our scheme can achieve excellent prediction performances when it is applied in a new scenario with limited training data. Furthermore, we verify that the scheme utilized in the simulated scenario can be transferred to both the new simulated scenario and the realistic scenario. With only 100 samples, the scheme achieves root mean square error (RMSE) values of 7.27 and 5.96 dB between the predicted and actual PL, respectively.

In time-variant high-speed train (HST) radio channels, the scattering environment changes rapidly with the movement of terminals, leading to a serious deterioration in communication quality. In the system- and link-level simulation of HST channels, this non-stationarity should be characterized and modeled properly. In this paper, the sizes of the quasi-stationary regions are quantified to measure the significant changes in channel statistics, namely, the average power delay profile (APDP) and correlation matrix distance (CMD), based on a measurement campaign conducted at 2.4 GHz. Furthermore, parameters of the multi-path components (MPCs) are estimated and a novel clustering-tracking-identifying algorithm is designed to separate MPCs into line-of-sight (LOS), periodic reflecting clusters (PRCs) from power supply pillars along the railway, and random scattering clusters (RSCs). Then, a non-stationary geometry-cluster-based stochastic model is proposed for viaduct and hilly terrain scenarios. Furthermore, the proposed model is verified by measured channel statistics such as the Rician K factor and the root mean square delay spread. The temporal autocorrelation function and the spatial cross-correlation function are presented. Quasi-stationary regions of the model are analyzed and compared with the measured data, the standardized IMT-Advanced (IMT-A) channel model, and a published non-stationary IMT-A channel model. The good agreement between the proposed model and the measured data demonstrates the ability of the model to characterize the non-stationary features of propagation environments in HST scenarios.

This paper investigates how to build an active attack detection framework that is driven by fundamental channel modeling and practical wireless datasets. Firstly, we propose the concept of interpretable channel fingerprints (ICFs), which correspond to the spatial-temporal parameters in real physical wireless signal propagation channels. Based on this, we design an adversarial autoencoder (AAE) with a semi-supervised learning network, which takes as inputs the power spectrum of quantized ICFs and enables small sample learning multiclassification tasks for different types of wireless channel active attacks. We have experimentally verified the performance of our AAE network using the Wireless InSite ray tracing software. Our results show that the proposed semi-supervised network outperforms the fully-supervised network especially in small sample conditions. We highlight the need for careful selection of the hyperparameters for learning rate and mini-batch size, and the system parameters for the ICF power spectrum resolution. We show that the detection accuracy of the proposed AAE model can reach more than 98% with only a small number of input samples.

Physical layer key generation (PLKG) is one of the promising security solutions for communications in the Internet of Things $(\mathrm{I}\mathrm{o}\mathrm{T})$. Among implementations for the PLKG, a low-cost, integrated, and portable design is still lacking. To this end, we build a cheap and moderately complex testbed with ESP32, which supports on-chip channel estimation. In the proposed testbed, four tasks are created to achieve channel probing and message exchanging for the PLKG. Considering independent clocks and packet mismatches in the real world, we implement a PLKG prototype that generates secret keys at a rate of 19.45 bit/s. Experimental results show that the generated keys can pass all commonly-used National Institute of Standards and Technology (NIST) randomness tests, and the transmitted information cannot be decrypted by the illegitimate user.

In this article, we present practical stochastic modeling and detailed performance analysis of our double blockchain (DBC) from Haoet al.(2021) for secure information and reputation data management in large-scale wireless Internet of Things (IoT) networks. Specifically, the DBC is a private blockchain deployed on a cloud-fog communication network which is composed of an information blockchain (IBC) storing large amounts of IoT data in the cloud layer and a reputation blockchain (RBC) storing reputation data of the IoT devices in the near-terminal fog layer. The locations of the fog layer nodes are modeled according to a random Poisson point process (PPP) over a given 2-D area to approximate the stochastic property of real-world wireless node deployments. Furthermore, we assume that the number of IoT devices transmitting to the fog nodes also follow a random Poisson distribution. Based on these models, we derive novel closed-form expressions for the storage size, transmission latency, and tampering time of the IoT fog nodes in our DBC architecture. Numerical simulations highlight high storage scalability, low latency, and superior security of the DBC design, and provide insights into the performance gains for different fog node and IoT device densities.

This article proposes a wireless key generation solution for secure low-latency communications with active jamming attack prevention in wireless networked control systems (WNCSs) of Industrial Internet of Things (IIoT) applications. We first identify a new vulnerability in physical-layer key generation schemes using wireless channel and random pilots (RPs) in static environments. We derive a closed-form expression for the probability that the RP-based key is successfully attacked by a long-term eavesdropper at a fixed location. To prevent such attacks, we propose a one-time pad (OTP) encrypted transmission solution assisted by one-way self-interference (SI), which has low-latency, high-security benefits, and active attack detection capability. The performance of the proposed scheme is analytically compared with two benchmark RP-based schemes, and its advantages are verified in a ray-tracing-based simulation environment. We further investigate the impact of critical design parameters, which reveal fundamental insights for the deployment and implementation of our proposed secure communications scheme.

Wireless secret key generation has emerged as a promising technique for Internet-of-Things (IoT) systems to establish shared encryption keys between the server and legitimate mobile user. This article focuses on the use of multidomain joint information to achieve a high key generation rate (KGR) and the implementation of a reliable, low-complexity secret key generation mechanism for distributed antenna systems (DAS) with orthogonal-frequency division multiplexing (OFDM). We present a space-time-frequency channel state information (CSI)-based key generation scheme based on a two-step approach of adaptive link selection and stepwise decorrelation algorithms. The performance is evaluated in terms of KGR, key disagreement rate (KDR), randomness, and computational complexity by using both a standardized channel model and real-world measurements. Numerical results show that our proposed low-complexity algorithms effectively utilize the space-time-frequency CSI to multiply the KGR in both indoor and outdoor environments. Through adaptive link selection in DAS, the KDR is maintained within a correctable range, thereby ensuring the validity of generated keys in dynamic environments. Further applying stepwise decorrelation reduces the computational complexity by more than half while satisfying all eight key generation randomness tests in the NIST test suite.

Physical layer secret key (PLSK) attracts much research interest in recent years due to its lightweight properties and potential to be used in real internet-of-things (IoT) applications. However, most existing works focus on PLSK generation between pairwise users under passive eavesdropping and lack of design for group key generation especially when active attacks may exist. In this paper, the RObust and LIghtweight Group key generation Against Active Attack (ROLIG3A) scheme is proposed to achieve reliable, efficient, and secure group secret key generation (GSKG) against various malicious attack patterns. In ROLIG3A, adaptive channel listening and dual code channel transmission are designed to avoid legitimate users from jamming signals. Comparing with the exiting GSKG schemes, more proactive countermeasures, back interference cancellation, and ECC-based information sharing are involved to realize the identification and cancellation of malicious attacks. Simulation results show that ROLIG3A has better security performance than the state-of-the-art GSKG schemes in the face of both sabotage attacks and manipulative attacks. In addition, ROLIG3A can provide an acceptable key generation rate and it is reliable even in low signal-to-noise-ratio (SNR) environments.

Unmanned aerial vehicle (UAV) will be an essential carrier for future wireless communications due to its flexible deployment and low cost. As such, the information security of UAV communications is of paramount concern. In this paper, a novel physical layer secret key generation scheme is proposed for air-to-ground (A2G) UAV multiple-input-multiple-output (MIMO) communications, which is applicable in frequency division duplex (FDD) systems. In UAV communications, line-of-sight (LoS) propagation is a distinctive feature, which significantly weakens the performance of channel state information (CSI) based keys. Therefore, a novel channel parameter, three-dimension (3D) spatial angle, is employed to combat against a novel active eavesdropping method, which is termed as Environment Reconstruction based Attack for SEcret keys (ERASE). Compared to the existing plane-angle-based method, our scheme can efficiently utilize spatial resources and provide a higher key generation rate (KGR). The advantages of the proposed scheme are shown through both theoretical analysis and simulations.

Recently, the utilization of unmanned aerial vehicles (UAVs) has been increasingly popular in various fields. As a brand new scenario of wireless communication, establishing feasible UAV channel models is necessary for the design and deployment of UAV-aided communication systems. In this paper, a three-dimensional (3D) geometry-based stochastic model (GBSM) is proposed for UAV air-to-air (A2A) channels. Based on this model, we derive the space-time correlation function (STCF), through which the impacts of some key parameters have been analyzed. Finally, the better universality of the proposed model is verified and some useful conclusions giving references for practical design are provided.

Secret key generation based on channel reciprocity is an important branch of wireless physical layer security which provides strong or even theoretically perfect secret. With the pursuit of higher secret key rate, the time-frequency joint key extraction schemes have been recently considered, which can exploit simultaneously the resources contained in the frequency and time domains. However, the strong correlation existing between the broadband time-variant channel samples makes the decorrelation process indispensable before quantization. Due to the large size of these channel sample matrices, decorrelation methods based on full channel covariance matrix corresponding to high calculation complexity are difficult to deploy in realistic mobile communication systems. In this work, we propose a method which separates the decorrelation of time-frequency correlated channel characteristics into two steps. The performance of this proposed method is evaluated through both standardized channel model and practical measurements. It is shown that the decorrelation of samples with the two-step method could be close to that based on the full channel covariance matrix, as well as leading to a dramatical reduction on the calculation complexity. The impact of the signal-to-noise-ratio (SNR) on the secret key rate is analyzed, and no distinct difference is shown in this aspect between these two methods.