The freshness of information related to status updates is crucial in time-critical applications like disaster response and search and rescue operations. We can enhance network connectivity by using electric vertical take-off and landing vehicles (eVTOLs) operating in the affected region as portable wireless repeaters as part of the operation. In this work, we study the spectrum allocation in ultra-reliable low latency communication (URLLC) networks assisted by eVTOLs while minimizing the age of information (AoI). The optimal spectrum allocation for eVTOL-assisted networks is a challenging problem that depends on various dynamic factors, such as bit error rate (BER), data rate, power consumption, and the flight trajectory of the eVTOLs. Therefore, we propose a dynamic approach that can efficiently allocate spectrum among the eVTOLs to minimize the total AoI between the source and the destination, as well as the individual AoI of each eVTOL acting as a relay node. Simulations exhibit that the proposed algorithm can outperform the classical approaches in terms of AoI by improving the AoI by $57.42 \%$ over the classical fairness approach and $43.43 \%$ over the classical optimal relay selection approach. We also find an improvement in data rate by $8.89 \%$ and $6.76 \%$, respectively, along with a marginal improvement in the BER. Our approach offers an efficient solution for next-generation AoI-aware spectrum management in eVTOL-assisted networks.

The reliability of 5G systems and their applications in a complex, dynamic, and heterogeneous environment requires rigorous testing and real-time detection for system vulnerabilities and unintended emergent behaviors. In this paper, fuzz testing is performed on 5G systems by randomly injecting and permuting control commands into the system under test (SUT) of the 5G radio resource control (RRC) authentication and authorization process, emulating Man-In-The-Middle (MITM) attacks to trigger potential vulnerabilities and unintended behaviors. The fuzzed system behaviors contain information that could indicate the system's health status, and potential vulnerabilities, and, more importantly, it enables the causation analysis in the SUT to detect the location and type of attacks or abnormal inputs from the profiling of the impacted behaviors. We then propose a Real-time Framework for Root Cause Analyses (RAFT) in NextG Vulnerability Detection based on analyzing the random fragments of the log file generated during the communication process. By processing the random fragments of the logging profiles captured during fuzz testing with the continuous bag-of-words (CBOW) Model, we extract the information of states and states transitions and perform causal analysis to identify the root cause for vulnerability detection in the 5G system. The novelty of our framework lies in the creation and analysis of the information extraction that does not require capturing the entire log file instead only the log file fragments to achieve high accuracy. This approach enables real-time detection and deployment to real-life scenarios where access to the entire logging profile is difficult to obtain or unavailable. The presented framework RAFT directly adapts to various machine learning (ML) models, which allow the adaptation to hardware with various computation complexity from internet-of-things (IoT) to Radio Access Network (RAN) servers. The experimental results show a significant performance gain and are thoroughly evaluated by the accuracy and area under the curve (AUC) results. In particular, we show that the proposed framework can attain a high AUC value ($0.92\leq\text{AUC} < 0.96$) by accessing only a 70% fragment of the original log file while maintaining a higher accuracy. In addition, we find that RAFT reduces the time complexity by more than 5% as the fragment size reduces to 70% of the original log file. The causation analysis nature of RAFT summarizes vulnerability information into essential root causes that can be easily transmitted within the network in real-time and turned into guidance for back-end engineers. The unique advantages of RAFT, including accurate causation with information fragments, reliable performance without large training datasets and less computation complexity guarantee a wide range of use cases and deployment environment of RAFT.

Controlling the carbon footprint and operating costs of 5G and nextG networks remains a venerable problem among network designers aiming for high spectrum efficiency and communication performance. This paper introduces a Game-Theoretic solution known as GeTOA (Game-Theoretic Age of Information), which leverages Nash bargaining (NB) to optimize the Age of Information (AOI) for multi-user electric vertical take-off and landing (eVTOL) communication. Considering the unique trajectory of the e VTOLs, which have substantial alterations in the channel conditions, coupled with the variation in the AOI during critical phases of flight, we calculate the Pareto optimal solutions for fair and efficient use of available resources while increasing the information content in our communication. We compare the cooperative GeTOA approach against the non-cooperative utility maximization (UM) approach, resulting in a notable 12.76% improvement while ensuring equitable resource allocation among the nodes. In contrast to the traditional UM approach, GeTOA significantly enhances energy allocation efficiency for multiple e VTOLs operating with diverse trajec-tories while enabling zero-touch fair resource management in open-access spectrum scenarios. In particular, results show that GeTOA handles fairness among the eVTOLs by ensuring an equal rate of information and fair distribution of power at the eVTOLs, which is especially relevant for Citizens Broadband Radio Service (CBRS) and C- Band applications. The flexibility and prioritization of AOI-based optimization allow a significant number of e VTOLs to operate efficiently within a congested spec-trum, facilitating Ultra-Reliable Low Latency Communication (URLLC) and improving power efficiency for the advancement of large-scale Urban Air Mobility (UAM) services which the limited flight range of e VTOLs has historically restricted.

Motivated by unique scattering properties at petahertz frequencies, we present a novel statistical model of fault-tolerant cooperative signal detection for short-range optical petahertz wireless communications, where a single-scattering assumption holds valid. We characterize the received non-line-of-sight (NLOS) signal by a fault-tolerant continuous waveform wideband detector with a non-zero failure probability. To better reflect the physical properties of the petahertz communication, both signal-independent and signal-dependent noise sources are considered in characterizing the received signal. The distribution of the received signal is quantified with each scattered path following the Málaga distribution. We leverage the location flexibility of randomly distributed collaborative users, considering each user experiences an independent channel condition. Utilizing the Neyman-Pearson criterion, we develop the binary hypothesis testing problem and subsequently derive the likelihood ratio for the test statistics. Moreover, to quantify the performance, a framework is developed for the average area under the receiver operating characteristic (ROC) curve for both single user and cooperative scenarios. An optimal decision fusion with a majority rule for fault-tolerant signal detection is applied to exploit the maximum spectrum opportunity. It is found that with the optimal voting rule and for a given target error rate, the network requires fewer collaborative secondary users than the total number of users available in an optical network. However, in the limiting case, it is shown that, as the cost function approaches its minimum or maximum value within its allowable range, the optimal number of collaborative users becomes independent of the failure probabilities. With the realistic assumption of fault-tolerant users, it is found that the false alarm probability increases faster than the detection probability.

Fuzzy clustering is a common way to divide data into groups. Even though it has been improved a lot, fuzzy clustering still has problems while clustering real high-dimensional Big Data with complicated latent distributions. To solve this problem, this study comes up with a way to represent the data in a feature space that was built from a scalable deep neural network using Apache Spark on HPC. In this paper, we proposed SDnnRSIO-FCM, a Scalable Deep Neural Network Random Sampling Iterative Optimization-FCM clustering algorithm, and the SDnnLFCM, a scalable version of the Deep Neural Network Literal Fuzzy c-Means algorithm. We focus on the design and implementation of the proposed SDnnRSIO-FCM and SDnnLFCM algorithms using the Apache Spark cluster in a High-Performance Computing (HPC) environment by representing the data in a feature space produced by the neural network to handle Big Data. First, data is mapped into new feature space to aid in the reconstruction of the original data by providing a good representation. Second, scalable fuzzy clustering is embedded with neural networks to propose deep fuzzy clustering methods. The experimental results conducted on two huge benchmark datasets show that the SDnnRSIO-FCM algorithm outperforms the SDnnLFCM algorithm in terms of Normalized Mutual Information (NMI), Adjusted Rand Index (ARI), and F-score. Furthermore, the proposed SDnnRSIO-FCM applied to huge soybean protein sequences in comparison with SDnnLFCM shows a significant improvement in terms of Silhouette index (SI), Davies-Bouldin index (DBI), and Calinski-Harabasz index (CHI).

Taking advantage of the proliferation of optical wireless communications, this paper proposes a new paradigm of optical scattering (or ultraviolet) vehicle-to-vehicle (V2V) communication with experimental analysis based on extensive channel measurements at a wavelength of 266 nm. This new optical scattering-based V2V technology can ease a fundamental requirement of pointing, acquisition, and tracking (PAT) commonly found in conventional optical V2V systems and can also provide an additional robust capability of efficient non-line-of-sight (NLOS) V2V transmission. Based on the experiments, it is demonstrated that in a mobile V2V channel where the dynamics are changing rapidly, the scattering of the UV photons in the upper atmosphere produces some unique characteristics, resulting in greater randomness and severe fading. From the experimental results, we characterize the channel by a non-stationary time-frequency-selective fading channel with a statistical description of the time-frequency-varying power profile. An examination of the measured data reveals that the distribution of the fading statistics follows the Rayleigh distribution but a departure from the Rayleigh distribution is also observed. In addition, to facilitate the real-time capture of the dynamic behavior of the UV-based V2V channel, a Kalman filter-based algorithm is presented to estimate the time-varying optical scattering channel parameters. Finally, some open issues and challenges in the optical scattering-based V2V technology are presented.

This paper presents collaborative spectrum sensing for optical wireless scattering communications. The received signal is characterized by the conditional Poisson distribution, given the mean photon count following Málaga distributed correlated fading. We consider multi-scattered non-coplanar links and use the stochastic analytical method to model the channel. A system model is developed, utilizing the cooperative diversity drawn from the fact that the users are randomly distributed. A novel centralized cooperative spectrum sensing technique is developed that all the raw data available at each collaborative user are combined at the fusion center to make a decision. Another novel decentralized technique is also proposed where only one-bit information is required to be sent to the fusion center. For this decentralized technique, we propose a unique voting rule that leverages the location flexibility of randomly distributed users with each user experiencing correlated fading. Results demonstrate their effectiveness and provide insights into the proposed cooperative spectrum sensing system. Interestingly, it is also found that the use of the proposed voting rule requires fewer than the total number of collaborative users while satisfying a given error rate, and yields the Bernoulli distributed output of the fusion center.

This paper presents a novel ultraviolet (UV) based multiuser indoor wireless communication system. We consider a shot-noise limited photon-counting signal model for a multiple-input single-output (MISO) optical fading channel. The system model is described, considering practical constraints such as transmit power limitations and UV safety exposure limits. We characterize the minimum mean squared error (MMSE) filter to minimize the multiple access interference. The lower bound of the bit error rate (BER) of the proposed system is presented, assuming the channel state information (CSI) at the receiver. A maximum likelihood sequence detection (MLSD) is also developed to efficiently mitigate indoor channel impairments when no CSI is available. It is shown that the performance of the proposed system with MLSD approaches the CSI lower bound performance, even with very short sequences. Simulation results also indicate that the proposed system effectively realizes the diversity gain expected from the MISO configuration.

In this paper, we propose a non-line-of-sight (NLOS) single scattering ultraviolet (UV) transmit-receive link model based on the switch-and-stay combining (SSC) diversity technique with M-ary phase shift keying (M-PSK) subcarrier intensity modulation over turbulent channel. Atmospheric turbulence is one of the main impairments affecting the performance of the UV communication link. It causes fluctuation in the intensity of the received UV signal, resulting in reduced signal-to-noise ratio (SNR) and subsequent increase in the symbol error probability and outage probability. To mitigate turbulence induced intensity fluctuations, a dual-branch SSC diversity reception technique is proposed. In addition, to avoid the requirement of adaptive threshold, the proposed SSC diversity is incorporated with M-PSK subcarrier intensity modulation. The intensity fluctuations in the received UV signal are modeled by a Gamma-Gamma distribution. The receivers are assumed to be exponentially correlated. We derive an analytical expression for the outage probability and the average-bit-error-rate (ABER) considering the correlated receivers.