This paper pioneers the domain of multi-user holographic unmanned aerial vehicle (UAV) communications, establishing a robust foundation for future advancements in next-generation aerial wireless networks. It investigates the joint design of hybrid holographic beamforming and three-dimensional (3D) positioning for a UAV equipped with a reconfigurable holographic surface (RHS), with the objective of maximizing the network’s sum rate. To tackle this inherently complex and non-convex optimization problem, a novel alternating optimization framework is proposed. The solution leverages zero-forcing (ZF) digital beamforming and a gradient ascent strategy to iteratively update the holographic beamforming weights and the UAV’s 3D location, while satisfying key system constraints. This framework is tailored to efficiently navigate the trade-offs between hybrid transceiver design and UAV mobility limitations, ensuring both adaptability and performance scalability. Simulation results confirm that the proposed approach achieves substantial gains in sum rate and system robustness compared to conventional methods, validating its effectiveness under diverse channel and deployment conditions.

This work proposes a framework for the robust design of UAV-assisted wireless networks that combine 3D trajectory optimization with user mobility prediction to address dynamic resource allocation challenges. We proposed a sparse second-order prediction model for real-time user tracking coupled with heuristic user clustering to balance service quality and computational complexity. The joint optimization problem is formulated to maximize the minimum rate. It is then decomposed into user association, 3D trajectory design, and resource allocation subproblems, which are solved iteratively via successive convex approximation (SCA). Extensive simulations demonstrate: (1) near-optimal performance with ϵ ≈ 0.67% deviation from upper-bound solutions, (2) 16% higher minimum rates for distant users compared to non-predictive 3D designs, and (3) 10 − 30% faster outage mitigation than time-division benchmarks. The framework’s adaptive speed control enables precise mobile user tracking while maintaining energy efficiency under constrained flight time. Results demonstrate superior robustness in edge-coverage scenarios, making it particularly suitable for 5G/6G networks.

Intelligent resource allocation maintains a better quality of service among devices in next-generation heterogeneous network infrastructures (NG-HetNetIs). NG-HetNetIs include industry 5.0 enabled infrastructures like Internet of Things (IoT), cognitive radio (CR) enabled B5G and 6G networks, unmanned aerial vehicles (UAVs), wireless sensor networks (WSNs) and autonomous vehicles (AVs). Digital twin (DT) joins hand with cognitive radio and resource aggregation technologies to provide the integrated framework for intelligent resource allocation in NG-HetNetIs. In NG-HetNetIs, the obtained statistics of measured radio activity as prior information play an instrumental role in enabling optimized resource allocation using context awareness. Unfortunately, the already available static approaches are inefficient to replicate (DT) the radio activity in a heterogeneous radio environment. To address the issue, static implementation framework is extended as dynamic radio activity characterization framework (DRAC) to have context awareness in NG-HetNetIs. The proposed DRAC replicates (DT) the wide sense stationarity of time and carrier aggregated radio activity due to its exploitation of more localized temporal and spectral information in NG-HetNets. The obtained localized statistics using DRAC can be exploited as appropriate prior knowledge and test statistics during the spectrum sensing phase of NG-HetNetIs for intelligent resource allocation instead of a single statistic obtained by the static approach.

This work investigates the application of Beyond Diagonal Intelligent Reflective Surface (BD-IRS) to enhance THz downlink communication systems, operating in a hybrid: reflective and transmissive mode, to simultaneously provide services to indoor and outdoor users. We propose an optimization framework that jointly optimizes the beamforming vectors and phase shifts in the hybrid reflective/transmissive mode, aiming to maximize the system sum rate. To tackle the challenges in solving the joint design problem, we employ the conjugate gradient method and propose an iterative algorithm that successively optimizes the hybrid beamforming vectors and the phase shifts. Through comprehensive numerical simulations, our findings demonstrate a significant improvement in rate when compared to existing benchmark schemes, including time- and frequency-divided approaches, by approximately 30.5% and 69.9% respectively and even outperforms the STAR-IRS system by 76.99%. This underscores the significant influence of IRS elements on system performance relative to that of base station antennas, highlighting their pivotal role in advancing the communication system efficacy.

Reconfigurable Intelligent surfaces (RIS) have the potential to significantly improve the performance of future 6G LEO satellite networks. In particular, RIS can improve the signal quality of ground terminal, reduce power consumption of satellite and increase spectral efficiency of overall network. This paper proposes an energy-efficient RIS-enabled NOMA communication for LEO satellite networks. The proposed framework simultaneously optimizes the transmit power of ground terminals at LEO satellite and passive beamforming at RIS while ensuring the quality of services. Due to the nature of the considered system and optimization variables, the problem of energy efficiency maximization is formulated as non-convex. In practice, it is very challenging to obtain the optimal solution for such problems. Therefore, we adopt alternating optimization methods to handle the joint optimization in two steps. In step 1, for any given phase shift vector, we calculate efficient power for ground terminals at satellite using Lagrangian dual method. Then, in step 2, given the transmit power, we design passive beamforming for RIS by solving the semi-definite programming. To validate the proposed solution, numerical results are also provided to demonstrate the benefits of the proposed optimization framework.

This work considers the orthogonal frequency division multiple access (OFDMA) technology that enables multiple unmanned aerial vehicles (multi-UAV) communication systems to provide on-demand services. The main aim of this work is to derive the optimal allocation of radio resources, 3D placement of UAVs, and user association matrices. To achieve the desired objectives, we decoupled the original joint optimization problem into two sub-problems: i) 3D placement and user association and ii) sum-rate maximization for optimal radio resource allocation, which are solved iteratively. The proposed iterative algorithm is shown via numerical results to achieve fast convergence speed after less than 10 iterations. The benefits of the proposed design are demonstrated via superior sum-rate performance compared to existing reference designs. Moreover, the results declared that the optimal power and sub-carrier allocation helped mitigate the co-cell interference that directly impacts the system’s performance.

Proliferating applications of unmanned aerial vehicles (UAVs) impose new service requirements, leading to several challenges. One of the crucial challenges in this vein is to facilitate the autonomous navigation of UAVs. Concretely, the UAV needs to individually process the visual data and subsequently plan its trajectories. Since the UAV has limited onboard storage constraints, its computational capabilities are often restricted and it may not be viable to process the data locally for trajectory planning. Alternatively, the UAV can send the visual inputs to the ground controller which, in turn, feeds back the command and control signals to the UAV for its safe navigation. However, this process may introduce some delays, which is not desirable for autonomous UAVs’ safe and reliable navigation. Thus, it is essential to devise techniques and approaches that can potentially offer low-latency solutions for planning the UAV’s flight. To this end, this paper analyzes a multi-access edge computing aided UAV and aims to minimize the latency of the task processing. More specifically, we propose an offloading strategy for a UAV by optimally designing the offloading parameter, local computational resources, and altitude of the UAV. The numerical and simulation results are presented to offer various design insights, and the benefits of the proposed strategy are also illustrated in contrast to the other baseline approaches.

With the changing lifestyle, a large population suffers from a bone disease known as an osteoarthritis affecting the knee, spine, and hip. Therefore, timely detection and classification of the disease are necessary to minimize the loss, however, it is a time-consuming task and requires various tests and physicians’ in-depth analysis. Thus, an accurate automated technique, timely detection and classification are needed to cope with the aforementioned challenges. This study proposes a technique based on an efficient DenseNet that uses the knee image’ features to identify the Knee Osteoarthritis (KOA) and determine its severity level according to the KL grading system such as Grade-I, Grade-II, Grade-III, and Grade-IV. We introduced the reweighted cross-entropy loss function which makes our proposed algorithm more robust as the training data is imbalanced. The dense connections of efficient DenseNet with regularization power help to reduce the overfitting during the training of small knee sample training sets. The proposed algorithm is an efficient approach that can identify the early symptoms of KOA and classify the severity level of the disease for better decision making by orthopedics. The algorithm is a pre-trained network that does not require a huge training set, therefore, the existing dataset i.e. Mendeley VI has been utilized for the training and testing. Additionally, cross-validation has been employed using the OAI dataset to assess the performance of the proposed model. The algorithm achieved 98.22% accuracy over the testing set and 98.08% accuracy over cross-validation. Various experiments have been performed to confirm that our proposed algorithm is more consistent and capable of detecting and classifying the KOA disease than existing state of the art.

Low Earth Orbit (LEO) satellite communication (SatCom) has drawn particular attention recently due to its high data rate services and low round-trip latency. It has low launching and manufacturing costs than Medium Earth Orbit (MEO) and Geostationary Earth Orbit (GEO) satellites. Moreover, LEO SatCom has the potential to provide global coverage with a high-speed data rate and low transmission latency. However, the spectrum scarcity might be one of the challenges in the growth of LEO satellites, impacting severe restrictions on developing ground-space integrated networks. To address this issue, cognitive radio and rate splitting multiple access (RSMA) are the two emerging technologies for high spectral efficiency and massive connectivity. This paper proposes a cognitive radio enabled LEO SatCom using RSMA radio access technique with the coexistence of GEO SatCom network. In particular, this work aims to maximize the sum rate of LEO SatCom by simultaneously optimizing the power budget over different beams, RSMA power allocation for users over each beam, and subcarrier user assignment while restricting the interference temperature to GEO SatCom. The problem of sum rate maximization is formulated as non-convex, where the global optimal solution is challenging to obtain. Thus, an efficient solution can be obtained in three steps: first we employ a successive convex approximation technique to reduce the complexity and make the problem more tractable. Second, for any given resource block user assignment, we adopt KarushKuhnTucker (KKT) conditions to calculate the transmit power over different beams and RSMA power allocation of users over each beam. Third, using the allocated power, we design an efficient algorithm based on the greedy approach for resource block user assignment. For comparison, we propose two suboptimal schemes with fixed power allocation over different beams and random resource block user assignment as the benchmark. Numerical results provided in this work are obtained based on the Monte Carlo simulations, which demonstrate the benefits of the proposed optimization scheme compared to the benchmark schemes.

Backscatter communications (BC) has emerged as a promising technology for providing low-powered transmissions in nextG (i.e., beyond 5G) wireless networks. The fundamental idea of BC is the possibility of communications among wireless devices by using the existing ambient radio frequency signals. Non-orthogonal multiple access (NOMA) has recently attracted significant attention due to its high spectral efficiency and massive connectivity. This paper proposes a new optimization framework to minimize total transmit power of BC-NOMA cooperative vehicle-to-everything networks (V2XneT) while ensuring the quality of services. More specifically, the base station (BS) transmits a superimposed signal to its associated roadside units (RSUs) in the first time slot. Then the RSUs transmit the superimposed signal to their serving vehicles in the second time slot exploiting decode and forward protocol. A backscatter device (BD) in the coverage area of RSU also receives the superimposed signal and reflect it towards vehicles by modulating own information. Thus, the objective is to simultaneously optimize the transmit power of BS and RSUs along with reflection coefficient of BDs under perfect and imperfect channel state information. The problem of energy efficiency is formulated as non-convex and coupled on multiple optimization variables which makes it very complex and hard to solve. Therefore, we first transform and decouple the original problem into two sub-problems and then employ iterative sub-gradient method to obtain an efficient solution. Simulation results demonstrate that the proposed BC-NOMA V2XneT provides high energy efficiency than the conventional NOMA V2XneT without BC.

Reflecting intelligent surfaces (RIS) is a low-cost and energy-efficient solution to achieve high spectral efficiency in sixth-generation (6G) networks. The basic idea of RIS is to smartly reconfigure the signal propagation by using passive reflecting elements. On the other side, the demand of high throughput geostationary (GEO) satellite communications (SatCom) is rapidly growing to deliver broadband services in inaccessible/insufficient covered areas of terrestrial networks. This paper proposes a GEO SatCom network, where a satellite transmits the signal to a ground mobile terminal using multicarrier communications. To enhance the effective gain, the signal delivery from satellite to the ground mobile terminal is also assisted by RIS which smartly shift the phase of the signal towards ground terminal. We consider that RIS is mounted on a high building and equipped With multiple re-configurable passive elements along with smart controller. We jointly optimize the power allocation and phase shift design to maximize the channel capacity of the system. The joint optimization problem is formulated as nonconvex due to coupled variables which is hard to solve through traditional convex optimization methods. Thus, we propose a new $\epsilon-$ optimal algorithm which is based on Mesh Adaptive Direct Search to obtain an efficient solution. Simulation results unveil the benefits of RIS-assisted SatCom in terms of system channel capacity.

Modeling of the underlying noise in a hyperspectral image reveals important information about the characteristics of the hyperspectral sensor and the image itself. While the focus in the literature has mostly been on the estimation of noise statistics, it is also of interest to estimate the actual noise present in each pixel, which can not only directly contribute to denoising of the image but can also aid other image processing algorithms exploiting such information. In this letter, we propose a novel method for per-pixel noise estimation that is also able to deal with spectral correlation in the noise. The method makes no assumptions on the behavior of the underlying noise. Simulation results show that the proposed method performs significantly better than the existing methods in cases where there is a correlation in the noise.

Authorship attribution aims to identify the author of a text based on the stylometric analysis.Authorship obfuscation, on the other hand, aims to protect against authorship attribution by modifying a text's style.In this paper, we evaluate the stealthiness of state-of-the-art authorship obfuscation methods under an adversarial threat model.An obfuscator is stealthy to the extent an adversary finds it challenging to detect whether or not a text modified by the obfuscator is obfuscated -a decision that is key to the adversary interested in authorship attribution.We show that the existing authorship obfuscation methods are not stealthy as their obfuscated texts can be identified with an average F1 score of 0.87.The reason for the lack of stealthiness is that these obfuscators degrade text smoothness, as ascertained by neural language models, in a detectable manner.Our results highlight the need to develop stealthy authorship obfuscation methods that can better protect the identity of an author seeking anonymity.

Abstract Stylometric authorship attribution aims to identify an anonymous or disputed document’s author by examining its writing style. The development of powerful machine learning based stylometric authorship attribution methods presents a serious privacy threat for individuals such as journalists and activists who wish to publish anonymously. Researchers have proposed several authorship obfuscation approaches that try to make appropriate changes (e.g. word/phrase replacements) to evade attribution while preserving semantics. Unfortunately, existing authorship obfuscation approaches are lacking because they either require some manual effort, require significant training data, or do not work for long documents. To address these limitations, we propose a genetic algorithm based random search framework called Mutant-X which can automatically obfuscate text to successfully evade attribution while keeping the semantics of the obfuscated text similar to the original text. Specifically, Mutant-X sequentially makes changes in the text using mutation and crossover techniques while being guided by a fitness function that takes into account both attribution probability and semantic relevance. While Mutant-X requires black-box knowledge of the adversary’s classifier, it does not require any additional training data and also works on documents of any length. We evaluate Mutant-X against a variety of authorship attribution methods on two different text corpora. Our results show that Mutant-X can decrease the accuracy of state-of-the-art authorship attribution methods by as much as 64% while preserving the semantics much better than existing automated authorship obfuscation approaches. While Mutant-X advances the state-of-the-art in automated authorship obfuscation, we find that it does not generalize to a stronger threat model where the adversary uses a different attribution classifier than what Mutant-X assumes. Our findings warrant the need for future research to improve the generalizability (or transferability) of automated authorship obfuscation approaches.

Many hyperspectral image processing algorithms (e.g., detection, classification, endmember extraction, and so on) are generally designed with the assumption of no spectral or spatial correlation in noise. However, previous studies have shown the presence of nonnegligible correlation between the noise samples in different spectral bands, especially between noises in adjacent bands, and that most of the well-known intrinsic dimension estimation algorithms give poor estimates in the presence of correlated noise. Thus, there is a need to tackle the specific case of spectrally correlated noise for noise estimation. We show, in this paper, that the commonly employed hyperspectral noise estimation algorithm based on regression residuals can be significantly affected by spectrally correlated noise and we suggest a modified approach that proves to be robust to noise correlation. Furthermore, the proposed method improves the noise variance estimates in comparison to the classic residual method even for the case of uncorrelated noise. Simulation results show that the estimation error is reduced at times by a factor of 5 when there is high spectral correlation in the noise. Our proposed per-pixel noise estimator requires an estimate of the noise covariance matrix, and for this, we also propose a method to estimate the noise covariance matrix. Simulation results demonstrate that the per-pixel noise estimates obtained via the use of estimated noise statistics are almost as good as those obtained via use of the true statistics.

An efficient algorithm for bit-loading in multicarrier systems is proposed based upon an allocation rhythm/order underlying the classical optimal greedy bit-allocation procedure. An accurate complexity comparison in terms of the exact number of execution cycles over a processor is made with the classical Greedy solution along with another recently proposed optimal bit-loading algorithm. Simulation results verify the significant complexity advantage of our algorithm with no loss in performance.

Multicarrier systems have shown better performance measures when system parameters (power, bits etc.) are optimized over different frequencies with respect to the channel gain factor. On the other hand stringent wireless spectrum constraints requiring inter-system spectrum sharing are giving birth to peak-power constrained multicarrier systems like MB-OFDM (A. Batra et al., 2004) based UWB system that rely on limiting peak emission power in order to inhibit interference with other systems using the same frequency spectrum. Power optimization for such systems must incorporate the additional peak-power constraint and at the same time respect the crucial time constraints of a very high data-rate system like that of UWB. This work addresses both of these issues by first analytically solving the peak power constrained BER minimizing power allocation problem and finally an important contribution comes in the development of a simplistic implementation algorithm which is found to converge faster than some recently proposed solutions based on iterative waterfilling for optimal power allocation in peak-power constrained systems.

The capacity approaching performances (Richardson et al., 2001) of different types of low density parity check (LDPC) codes have led them to undergo extensive research in recent years. Along with asymptotic performances analysis, the optimization of 'irregularity' profile for different channels and the performance analysis of practical finite-length codes has also been extensively explored. With multi-carrier communications becoming the physical layer choice for many emerging wireless systems, a feasible solution for optimizing the irregularity profile of irregular LDPC codes for a frequency selective channel is a problem of particular importance and interest. This paper proposes a simple-to-implement greedy 'check' allocation (GCA) based method for the construction of BER-optimized irregular LDPC codes using the criterion of Gallager upper bound (Gallager, 1962) for probabilistic decoding. A comparison of our GCA algorithm with some existing works (Mannoni et al., 2002) on the irregular LDPC codes optimization for multicarrier systems based on the classical Gaussian approximation of the 'density evolution' approach, shows that the same irregularity behavior can be achieved with a much simpler method.

The recent adoption of ultra-wideband (UWB) technology for applications involving short-range very high data- rate wireless communication necessitates efficient allocation of system resources (bits, power etc.) because of the stringent spectral mask constraints. The multiband-OFDM(MB-OFDM) based UWB proposal permits usage of adaptive resource allocation over different sub-carriers so as to efficiently respond to the frequency-selective nature of the UWB channel.This paper proposes a new discrete bit-allocation methodology that results in minimizing power consumption for a fixed throughput and which appears to converge to the optimal solution faster than most known optimal discrete bit-loading methods. With peak- power-constraint and channel gain factor given for a sub-carrier, the sufficient condition for determining maximum permissible number of bits is also established. While earlier bit-loading algorithms have mostly targeted the ADSL-type systems, UWB application scenario is targeted in this paper to seek the total energy improvement factor with respect to a non-adaptive UWB system. However, the allocation methodology can be used for any multi-carrier based system.