GAN-Based Artificial Noise Generation Against Eavesdropping for Wireless Secret Key Generation in Dynamic Indoor LiFi Networks

As a high-speed, large-capacity underwater communication technology, underwater wireless optical communication (UWOC) has broad development prospects. However, accurately characterizing the underwater optical channel for various underwater conditions is still a challenging task due to light absorption and scattering, which may limit further research on UWOC. To overcome these issues, this paper proposes a novel framework of the underwater optical channel impulse response (CIR) generator, named the UCIRG framework, based on the generative adversarial network. Unlike the classical Monte Carlo approach, which is a numerical simulation method taking a long time to calculate, the UCIRG framework can generate a large number of CIRs extremely close to the actual underwater optical CIRs in a short time based on limited underwater optical CIRs as training data. Besides, traditional approximation methods for underwater optical channels usually have limitations on underwater conditions, lacking generality and flexibility, hence a generalized algorithm for UCIRG is also investigated, which is valid for various underwater conditions and can further reduce training time. Moreover, the multi-carrier autoencoder based UWOC system is adopted to evaluate the performance of the trained UCIRG, and the simulation results prove that the CIRs generated by the UCIRG are extremely similar to the actual CIRs.

In this paper, we present our research on modeling a synthetic light fidelity (LiFi) channel model that uses a deep learning architecture called generative adversarial networks (GAN). A research in LiFi that requires the generation of many multipath channel impulse responses (CIRs) can benefit from our proposed model. For example, future developments of autonomous (deep learning-based) network management systems that use LiFi as one of its high-speed wireless access technologies might require a dataset of many CIRs. In this paper, we use TimeGAN, which is a GAN architecture for time-series data. We will show that modifications are necessary to adopt TimeGAN in our use case. Consequently, synthetic CIRs generated by our model can track long-term dependency of LiFi multipath CIRs. The Kullback–Leibler divergence (KLD) is used in this paper to measure the small difference between samples of synthetic CIRs and real CIRs. Lastly, we also show a simple demonstration of our model that can run on a small virtual machine hosted over the Internet.

The fifth-generation wireless network (5G) is expected to support vehicle to infrastructure (V2I) communication which requires an accurate understanding of its highly mobile and dynamic propagation environment for efficient physical layer design. Towards this end, this paper proposes a joint design of adaptive channel prediction, beamforming and scheduling for 5G V2I communications. The channel prediction algorithm is designed without using pilot signals and assuming the channel impulse response (CIR) model. In this regard, we first propose an adaptive recursive least squares (RLS) CIR prediction approach that uses few of the past estimated CIRs to predict one or more future orthogonal frequency division multiplexing (OFDM) block CIR coefficients. Then, we jointly design beamforming and vehicle equipment (VE) scheduling for each sub-carrier to maximize the uplink channel average sum spectrum efficiency (SE) by employing the predicted CIR. The beamforming problem is formulated as a Rayleigh quotient optimization where its global optimal solution can be obtained. Moreover, the VE scheduling design is formulated as an integer programming problem solved using a greedy search. To enhance the prediction performance, we propose a model-free Q-learning technique to learn a strategy for selecting the best CIR predictor at the current OFDM block. In addition, as the CIR coefficients might contain undesired taps with zero (negligible) average powers, we further examine a dominant CIR tap index identification problem (formulated as convex) from the past observed signals. We carry out extensive simulations to validate analytical expressions and examine the effects of different parameters including the VE speed on the achievable sum SE. Numerical simulations corroborate the superiority of the proposed channel prediction and scheduling algorithms over the existing ones and the relevance of identifying dominant CIR taps.

Ultrawideband (UWB) radio frequency positioning technology has found extensive applications in indoor and outdoor localization due to its strong anti-interference capabilities, high penetration power, and precise measurement accuracy. However, its positioning accuracy significantly decreases under nonline-of-sight (NLOS) conditions, particularly in dynamic NLOS environments. Therefore, it is essential to identify NLOS propagation and mitigate its associated errors. To overcome these issues, this study proposes a novel enhanced domain-adversarial neural network for UWB signal occlusion recognition (EDANN-SOR). EDANN-SOR integrates hybrid manually extracted channel impulse response (CIR) features, effectively aligning the source and target domains and extracting discriminative and domain-invariant UWB CIR features in dynamic NLOS environments. Through adversarial learning, our method achieves high generalization with minimal CIR sample requirements, reducing the need for extensive data collection and significantly enhancing robustness in distance measurement and positioning under dynamic NLOS conditions. In contrast to state-of-the-art NLOS identification and transfer learning (TL) techniques, our method excels in both binary and multiclass NLOS classification. Specifically, it achieves an accuracy of over 97.36% in conventional LOS/NLOS binary classification across scenarios and 97.07% in multiclass classification. The integration of manually extracted features has been proved effective in improving the EDANN-SOR model’s ability to distinguish among LOS, human, and glass obstacles.

The recent emerging applications of novel underwater systems lead to increasing demand for underwater acoustic (UWA) communication and networking techniques. However, due to the challenging UWA channel characteristics, conventional wireless techniques are rarely applicable to UWA communication and networking. The cognitive and software-defined communication and networking are considered promising architecture of a novel UWA system design. As an essential component of a cognitive communication system, the modeling and prediction of the UWA channel impulse response (CIR) with deep generative models are studied in this work. Firstly, an underwater acoustic communication and networking testbed is developed for conducting various simulations and field experiments. The proposed test-bed also demonstrated the capabilities of developing and testing SDN protocols for a UWA network in both simulation and field experiments. Secondly, due to the lack of appropriate UWA CIR data sets for deep learning, a series of field UWA channel experiments have been conducted across a shallow freshwater river. Abundant UWA CIR data under various weather conditions have been collected and studied. The environmental factors that significantly affect the UWA channel state, including the solar radiation rate, the air temperature, the ice cover, the precipitation rate, etc., are analyzed in the case studies. The obtained UWA CIR data set with significant correlations to weather conditions can benefit future deep-learning research on UWA channels. Thirdly, a Wasserstein conditional generative adversarial network (WCGAN) is proposed to model the observed UWA CIR distribution. A power-weighted Jensen–Shannon divergence (JSD) is proposed to measure the similarity between the generated distribution and the experimental observations. The CIR samples generated by the WCGAN model show a lower power-weighted JSD than conventional estimated stochastic distributions. Finally, a modified conditional generative adversarial network (CGAN) model is proposed for predicting the UWA CIR distribution in the 15-minute range near future. This prediction model takes a sequence of historical and forecast weather information with a recent CIR observation as the conditional input. The generated CIR sample predictions also show a lower power-weighted JSD than conventional estimated stochastic distributions.

A machine learning (ML) framework is proposed for primary user emulation attack (PUEA) detection in Cognitive Radio Networks (CRN). The ML framework is based on various classification models that exploit features extracted using the proposed Pattern Described Link-Signature method (PDLS) to distinguish between legitimate and malicious users. PDLS defines Link-signature from the channel impulse response (CIR) of the wireless link in the multipath environment. Previous works define Link-signature as the amplitude (or power) value of CIR while PDLS, on the other hand, defines it as a pattern that describes the structure of 52 sub-CIR (i.e., CIR contains 52 sub-CIR) in Orthogonal Frequency Division Multiplexing (OFDM) based transceivers. The proposed scheme is tested by developing a Software-Defined Radio (SDR) testbed to capture real wireless channel measurements. The testbed is based on IEEE 802.11a/g/p standard transceiver which comprises OFDM physical layer. Experimental results show that the proposed approach can distinguish between legitimate and malicious users effectively.

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.

User authentication is the critical first step of network security to detect identity-based attacks and prevent subsequent malicious attacks. However, the increasingly dynamic mobile environments make it harder to always apply the cryptographic-based methods for user authentication due to their infrastructural and key management overhead. Exploiting non-cryptographic-based techniques grounded on physical layer properties to perform user authentication appears promising. To ensure the security of mobile devices in dynamic networks, we explore to use fine-grained channel state information (CSI), which is available from off-the-shelf WiFi devices, to perform proactive user authentication. We propose a user-authentication framework that has the capability to proactively request CSI and build the user profile resilient to the presence of the spoofer. Our machine learning based user-authentication techniques can distinguish two users even when they possess similar signal fingerprints and detect the existence of the spoofer in dynamic network environments. Extensive experiments in both office and apartment environments show that our framework can remove the effect of signal outliers and achieve higher authentication accuracy compared to existing approaches that use received signal strength (RSS).

Human Activity Recognition (HAR) is a fundamental building block for the current trend of smart devices in Internet of Things (IoT). Ultra-Wideband RF technology has been used in localization research while Wi-Fi Channel State Information (CSI) has been widely investigated for non-obtrusive activity recognition in the literature. This paper investigates the feasibility of using UWB technology for Human Activity Recognition (HAR). The key idea is to use machine learning classification algorithms most suited to train models to classify different activities using the Channel Impulse Response (CIR) data of the UWB signals. Our experiments show that by using CIR data as features we can classify simple activities such as standing, sitting, lying with an accuracy of 95%. To compare this performance, we have also trained statistical models using Wi-Fi CSI. We found that, for all models UWB CIR significantly outperformed Wi-Fi CSI. Thus, we believe UWB to be a very effective technology in the context of device-free activity recognition.

One of the important use-cases of 5G network is the vehicle to infrastructure (V2I) communication which requires accurate understanding about its dynamic propagation environment. As 5G base stations (BSs) tend to have multiple antennas, they will likely employ beamforming to steer their radiation pattern to the desired vehicle equipment (VE). Furthermore, since most wireless standards employ an OFDM system, each VE may use one or more sub-carriers. To this end, this paper proposes a joint design of adaptive channel prediction, beamforming and scheduling for 5G V2I communications. The channel prediction algorithm is designed without the training signal and channel impulse response (CIR) model. In this regard, first we utilize the well known adaptive recursive least squares (RLS) technique for predicting the next block CIR from the past and current block received signals (a block may have one or more OFDM symbols). Then, we jointly design the beamforming and VE scheduling for each sub- carrier to maximize the uplink channel average sum rate by utilizing the predicted CIR. The beamforming problem is formulated as a Rayleigh quotient optimization where its global optimal solution is guaranteed. And, the VE scheduling design is formulated as an integer programming problem which is solved by employing a greedy search. The superiority of the proposed channel prediction and scheduling algorithms over those of the existing ones is demonstrated via numerical simulations.

Wireless localisation is becoming increasingly important in various applications such as smart homes, elderly healthcare facilities and in industry where centimetre (cm) level localisation accuracy is desired. Ultra-wideband (UWB) systems can be used for such applications since they can achieve a ranging precision below 10 cm in a Line-of-Sight (LoS) setup. However, in non LoS(NLoS) scenarios these systems provide a lower accuracy. In this paper, we exploit the high resolution Channel Impulse Response (CIR) provided by the Decawave EVK1000 boards for localisation in a residential environment. We employ a single anchor node and use the CIR obtained from five different locations as fingerprints to investigate whether the location of the tag can be accurately estimated in NLoS scenarios. Our investigation showed that the CIR can be effectively used as fingerprints to provide a location classification accuracy as high as 98% when the environment remains relatively stable. However, using the CIR data recorded in a second experiment (same setup as first experiment) as test data and applying the trained model of the first experiment to it showed a significant degradation in performance (50-60% accuracy) due to the changes in the environment. On the other hand, by using five features extracted from the UWB signals for location classification, an accuracy in excess of 99% is obtained during testing in both experiments.

Ultra-wideband (UWB) multistatic radar can be used for target detection and tracking in buildings and rooms. Target detection and tracking relies on accurate knowledge of the bistatic delay. Noise, measurement error, and the problem of dense, overlapping multipath signals in the measured UWB channel impulse response (CIR) all contribute to make bistatic delay estimation challenging. It is often assumed that a calibration CIR, that is, a measurement from when no person is present, is easily subtracted from a newly captured CIR. We show this is often not the case. We propose modeling the difference between a current set of CIRs and a set of calibration CIRs as a hidden Markov model (HMM). Multiple experimental deployments are performed to collect CIR data and test the performance of this model and compare its performance to existing methods. Our experimental results show an RMSE of 2.85 ns and 2.76 ns for our HMM-based approach, compared to a thresholding method which, if the ideal threshold is known a priori, achieves 3.28 ns and 4.58 ns. By using the Baum-Welch algorithm, the HMM-based estimator is shown to be very robust to initial parameter settings. Localization performance is also improved using the HMM-based bistatic delay estimates.

With the booming demand of indoor position information, Wireless signals (Wi-Fi, Bluetooth, Ultra-Wide-Band etc.) are investigated to construct Indoor Positioning System (IPS). Among these wireless signals, UWB (Ultra-Wide-Band) is recognized as the most promising technology to construct IPS with decimeter-level positioning accuracy. However, there are various objects in indoor environments, UWB signals might be reflected by these surrounding objects. These None-Line-Of-Sight (NLOS) signals will induce additional errors to the distance measurements between the anchor and agent. Therefore, NLOS/LOS (Line-Of-Sight) signals classification should be carried out for identifying the NLOS reception. In UWB based IPS, the distance information is extracted through the Channel Impulse Response (CIR) waveforms. NLOS reception will lead to different CIR waveforms compared with that from the LOS signal, this difference could be characterized for NLOS/LOS signals classification. However, the hardware circuits, signal transmitting path and the NLOS reception will bring about noises to the CIR waveforms, which might influence the signal type identification. Motivated by this problem, in this letter, a reversible transformation method was proposed for de-nosing the CIR data, and a Conventional Neural Network (CNN) was employed to identify the NLOS signal. Raw and de-noised CIR dataset were both input to the CNN for assessing the effect of the de-noising method. With dataset collected from seven different sites, the results showed that the reversible transformation method was able to increase the Signal Noise Ratio (SNR) values of these CIR datasets. After the de-noising, the CNN performed higher classification accuracy with less convolutional layers. Moreover, the de-noising operation brought about higher NLOS signal identification accuracy increase than that of LOS signals.

Radio signal-based positioning in environments with complex propagation paths is a challenging task for classical positioning methods. For example, in a typical industrial environment, objects such as machines and workpieces cause reflections, diffractions, and absorptions, which are not taken into account by classical lateration methods and may lead to erroneous positions. Only a few data-driven methods developed in recent years can deal with these irregularities in the propagation paths or use them as additional information for positioning. These methods exploit the channel impulse responses (CIR) that are detected by ultra-wideband radio systems for positioning. These CIRs embed the signal properties of the underlying propagation paths that represent the environment. This article describes a feature-based localization approach that exploits machine-learning to derive characteristic information of the CIR signal for positioning. The approach is complete without highly time-synchronized receiver or arrival times. Various features were investigated based on signal propagation models for complex environments. These features were then assessed qualitatively based on their spatial relationship to objects and their contribution to a more accurate position estimation. Three datasets collected in environments of varying degrees of complexity were analyzed. The evaluation of the experiments showed that a clear relationship between the features and the environment indicates that features in complex propagation environments improve positional accuracy. A quantitative assessment of the features was made based on a hierarchical classification of stratified regions within the environment. Classification accuracies of over 90% could be achieved for region sizes of about 0.1 m. An application-driven evaluation was made to distinguish between different screwing processes on a car door based on CIR measures. While in a static environment, even with a single infrastructure tag, nearly error-free classification could be achieved, the accuracy of changes in the environment decreases rapidly. To adapt to changes in the environment, the models were retrained with a small amount of CIR data. This increased performance considerably. The proposed approach results in highly accurate classification, even with a reduced infrastructure of one or two tags, and is easily adaptable to new environments. In addition, the approach does not require calibration or synchronization of the positioning system or the installation of a reference system.

We consider a multiuser diffusion-based molecular communication (MC) system where multiple spatially distributed transmitter (TX)-receiver (RX) pairs establish point-to-point communication links employing the same type of signaling molecules. To realize the full potential of such a system, an in-depth understanding of the interplay between the spatial user density and inter-user interference (IUI) and its impact on system performance in an asymptotic regime with large numbers of users is needed. In this paper, we adopt a three-dimensional (3-D) system model with multiple independent and spatially distributed point-to-point transmission links, where both the TXs and RXs are positioned according to a regular hexagonal grid, respectively. Based on this model, we first derive an expression for the channel impulse responses (CIRs) of all TX-RX links in the system. Then, we provide the maximum likelihood (ML) decision rule for the RXs and show that it reduces to a threshold-based detector. We derive an analytical expression for the corresponding detection threshold which depends on the statistics of the MC channel and the statistics of the IUI. Furthermore, we derive an analytical expression for the bit error rate (BER) and the achievable rate of a single transmission link. Finally, we propose a new performance metric, which we refer to as area rate efficiency (ARE), that captures the tradeoff between the user density and IUI. The ARE characterizes how efficiently given TX and RX areas are used for information transmission and is given in terms of bits per area unit. We show that there exists an optimal user density for maximization of the ARE. Results from particle-based and Monte Carlo simulations validate the accuracy of the expressions derived for the CIR, optimal detection threshold, BER, and ARE.