arXiv subjects

Waqas Khalid

Publications and source records attributed to Waqas Khalid.

At least 19 recordsLinked to original sources

arXiv2026

Beam Squint and Aperture--Bandwidth Limitations in Wideband RIS-Assisted THz Links

This paper analyzes the ergodic rate of a wideband reconfigurable intelligent surface (RIS)-assisted terahertz link under beam squint and finite-resolution phase control. Independent Rayleigh fading on the two RIS-assisted hops produces double-Rayleigh cascaded amplitudes. Exact second- and fourth-order channel moments are derived by incorporating the frequency-dependent array response and phase-quantization errors. Moment matching then provides a Gamma approximation for the received power on each subcarrier and a tractable closed-form approximation for the wideband ergodic rate. A first-null bandwidth approximation characterizes the aperture--bandwidth limitation of frequency-flat RIS control. Monte Carlo simulations validate the analytical framework.

cs.IT↗

arXiv2026

Deterministic versus Stochastic Optimization for Joint Path Planning and Dynamic Time Splitting in Multiple-UAV-Cached IoT Networks

This paper examines wireless-powered Internet of Things (IoT) networks involving multiple unmanned aerial vehicles (UAVs) equipped with backscatter and caching technologies to relay and transmit signals. For data communication and energy harvesting (EH), the source transmits information and power to UAVs using the dynamic time splitting (DTS) method. UAVs use harvested energy for passive communication (backscatter) and for active communication (transmitting information) to the destination. The primary objective is to maximize the total throughput by jointly optimizing the DTS ratio, trajectory, and transmission power, leveraging the UAVs' caching capability. This optimization problem is challenging due to its non-convexity. Therefore, an efficient alternating algorithm using the block coordinate descent (BCD) method is proposed to optimize each variable given the fixed values of the other parameters. By applying the Karush-Kuhn-Tucker (KKT) conditions, we derive a closed-form expression for the optimal DTS ratio, significantly reducing computation time. The optimal values for the other two parameters are determined using the BCD. In order to thoroughly assess the effectiveness of various solutions for the original problem, this paper introduces an approach leveraging a genetic algorithm (GA). The GA in this context employs a one-point crossover method, value mutation, and rank-based selection based on fitness values. Numerical results show that the BCD and GA achieve at least 31% throughput improvement over the benchmarks, with reduced computational time. These findings demonstrate the performance gain and practical feasibility of our solutions in caching-enabled UAV-aided IoT networks.

cs.IT↗

arXiv2026

Nested array design of extended coprime sets for DOA estimation of non-circular signals

In recent years, direction of arrival estimation utilizing non-circular signals has become a focal point for scholarly research. To enhance the degrees of freedom (DOF) in receiver arrays specifically for non-circular signal DOA estimation, this study introduces a novel array configuration. This design leverages an extended coprime framework, applying a sliding translation technique to optimize sensor placement. Crucially, this rearranged structure preserves the continuity of the difference co-array (DCA). Furthermore, the sum co-array (SCA) is shifted to merge seamlessly with the DCA, eliminating redundancy and substantially expanding both the virtual aperture array (VAA) and the DOF. Consequently, the proposed array demonstrates superior performance in practical DOA estimation tasks involving non-circular signals. Simulation results and comparative analyses confirm that, relative to traditional Nested Arrays (NA), Extended Sliding Nested Array (ESNA), and other benchmark structures, the proposed array achieves better DOF and VAA, leading to enhanced estimation accuracy in practical scenarios.

cs.NI↗

arXiv2026

Advanced Anomaly Detection and Threat Intelligence in Zero Trust IoT Environments Using Machine Learning

The growing adoption of IoT and cloud computing, combined with rapid advancements in digital technologies, has considerably increased the cyber-attack surface, resulting in increasingly complex and persistent attacks. Traditional security methods, primarily based on perimeter defenses, are insufficient to meet these developing threats, especially within the context of a Zero Trust Security (ZTS) architecture. This study investigates the application of sophisticated artificial intelligence (AI) and machine learning (ML) techniques, including the use of the Synthetic Minority Oversampling Technique (SMOTE), to improve anomaly detection and threat intelligence systems. This study focuses on how Support Vector Machine (SVM), Random Forest (RF), and Decision Tree (DT) classifiers might increase threat detection accuracy in IoT environments. The research endeavors to improve cybersecurity resilience by mitigating false positives and providing actionable intelligence through supervised learning algorithms. The KDD Cup 1999 dataset is used in the study to assess how well these models perform in simulating various network intrusions and regular traffic. The application of SMOTE significantly enhanced the performance of these models by addressing class imbalance, leading to improved detection accuracy. Furthermore, as supplementary methods for detecting malicious URLs and advanced persistent threats (APTs), edge-based machine learning and blockchain technology are investigated. This study addresses the shortcomings of conventional security systems and supports the growing demand for reliable threat detection in a world that is becoming more interconnected. It also advances the creation of more proactive and adaptable cybersecur

cs.CR↗

arXiv2026

A Modulated Electron Lattice (MEL) Criterion for Metallic Superconductivity

A central unresolved question in the theory of superconductivity is why only a small subset of metallic elements exhibit a superconducting state, whereas many others remain strictly normal. Neither the conventional Bardeen Cooper Schrieffer (BCS) framework nor its extensions involving charge density wave (CDW) or pair density wave (PDW) order provide a predictive or material-selective criterion capable of distinguishing superconducting metals from non-superconducting ones. In particular, the persistent absence of superconductivity in simple noble metals with well-defined Fermi surfaces poses a challenge for all traditional approaches. Here we address this problem using the Modulated Electron Lattice (MEL) Ginzburg Landau (GL) framework introduced in our previous work. In this formulation, a coarse-grained MEL charge field $ρ_{\mathrm{MEL}}(\mathbf{r})$ with momentum dependent stiffness $α(q)$ is coupled to the superconducting (SC) order parameter $ψ(\mathbf{r})$. We show that metallic superconductivity emerges only when the system satisfies a specific ``MEL enhancement window,'' characterized by a negative minimum of $α(q)$ at either a finite modulation wave vector $q^{\ast}$ or at $q=0$, together with sufficiently strong coupling between $ρ_{\mathrm{MEL}}$ and $ψ$. This unified criterion naturally partitions metallic elements into three universal classes: (i) MEL-enhanced superconductors with a finite-$q^{\ast}$ charge mode, (ii) conventional BCS superconductors as the homogeneous $q^{\ast}=0$ limit of the MEL framework, and (iii) metals for which $α(q)$ remains positive for all $q$, suppressing all MEL modes and preventing any superconducting instability. By applying this criterion to simple metallic elements, we identify why some metals develop superconductivity while others do not, possibly resolving a selection problem long open within the BCS paradigm.

cond-mat.supr-con↗

arXiv2026

SecureDyn-FL: A Robust Privacy-Preserving Federated Learning Framework for Intrusion Detection in IoT Networks

The rapid proliferation of Internet of Things (IoT) devices across domains such as smart homes, industrial control systems, and healthcare networks has significantly expanded the attack surface for cyber threats, including botnet-driven distributed denial-of-service (DDoS), malware injection, and data exfiltration. Conventional intrusion detection systems (IDS) face critical challenges like privacy, scalability, and robustness when applied in such heterogeneous IoT environments. To address these issues, we propose SecureDyn-FL, a comprehensive and robust privacy-preserving federated learning (FL) framework tailored for intrusion detection in IoT networks. SecureDyn-FL is designed to simultaneously address multiple security dimensions in FL-based IDS: (1) poisoning detection through dynamic temporal gradient auditing, (2) privacy protection against inference and eavesdropping attacks through secure aggregation, and (3) adaptation to heterogeneous non-IID data via personalized learning. The framework introduces three core contributions: (i) a dynamic temporal gradient auditing mechanism that leverages Gaussian mixture models (GMMs) and Mahalanobis distance (MD) to detect stealthy and adaptive poisoning attacks, (ii) an optimized privacy-preserving aggregation scheme based on transformed additive ElGamal encryption with adaptive pruning and quantization for secure and efficient communication, and (iii) a dual-objective personalized learning strategy that improves model adaptation under non-IID data using logit-adjusted loss. Extensive experiments on the N-BaIoT dataset under both IID and non-IID settings, including scenarios with up to 50% adversarial clients, demonstrate that SecureDyn-FL consistently outperforms state-of-the-art FL-based IDS defenses.

cs.CR↗

arXiv2026

Single- and Multi-Objective Stochastic Optimization for Next-Generation Networks in the Generative AI and Quantum Computing Era

Next Generation (NG) networks move beyond simply connecting devices to creating an ecosystem of connected intelligence, especially with the support of generative Artificial Intelligence (AI) and quantum computation. These systems are expected to handle large-scale deployments and high-density networks with diverse functionalities. As a result, there is an increasing demand for efficient and intelligent algorithms that can operate under uncertainty from both propagation environments and networking systems. Traditional optimization methods often depend on accurate theoretical models of data transmission, but in real-world NG scenarios, they suffer from high computational complexity in large-scale settings. Stochastic Optimization (SO) algorithms, designed to accommodate extremely high density and extensive network scalability, have emerged as a powerful solution for optimizing wireless networks. This includes various categories that range from model-based approaches to learning-based approaches. These techniques are capable of converging within a feasible time frame while addressing complex, large-scale optimization problems. However, there is currently limited research on SO applied for NG networks, especially the upcoming Sixth-Generation (6G). In this survey, we emphasize the relationship between NG systems and SO by eight open questions involving the background, key features, and lesson learned. Overall, our study starts by providing a detailed overview of both areas, covering fundamental and widely used SO techniques, spanning from single to multi-objective signal processing. Next, we explore how different algorithms can solve NG challenges, such as load balancing, optimizing energy efficiency, improving spectral efficiency, or handling multiple performance trade-offs. Lastly, we highlight the challenges in the current research and propose new directions for future studies.

cs.IT↗

arXiv2025

Short-Range Modulated Electron Lattice and d-Wave Superconductivity in Cuprates: A Phenomenological Ginzburg-Landau Framework

We develop a phenomenological Ginzburg-Landau (GL) framework for high-$T_c$ cuprates in which a short-range modulation of the electronic charge density couples to a $d$-wave superconducting condensate. The resulting modulated electron lattice (MEL) state is distinct from long-range static charge density wave order: it is short range, partially phase coherent, and linked to superconducting coherence. A preferred wave vector $q^{\ast} \approx 0.3$ reciprocal lattice units along the Cu-O bond direction emerges from the interplay between a momentum-dependent susceptibility and bond-stretching phonons, consistent with neutron and x-ray data on YBa$_2$Cu$_3$O$_{7-δ}$ and related cuprates. The GL free energy contains coupled $d$-wave superconducting and charge sectors with parameters constrained by optimally doped YBa$_2$Cu$_3$O$_{7-δ}$. We identify an MEL enhancement window in doping, temperature, MEL correlation length, and disorder where a coherence linked modulation enhances the superfluid stiffness. Classical Monte Carlo simulations yield an in-plane stiffness enhancement of order ten percent, which we treat as a qualitative prediction to be tested by self-consistent Bogoliubov de Gennes calculations. The MEL framework yields falsifiable experimental signatures. For scanning tunneling spectroscopy in Bi-based cuprates we highlight two predictions: the Fourier-transformed local density of states should exhibit a $q^{\ast} \approx 0.3$ peak whose spectral weight sharpens as superconducting phase coherence develops below $T_c$, in contrast to static charge scenarios, and the local gap magnitude $Δ(r)$ should correlate positively with the local MEL amplitude. The framework implies correlations between MEL correlation length, superfluid stiffness, disorder, and vortex pinning, and organizes cuprate observations into testable STM/STS predictions.

cond-mat.supr-con↗

arXiv2025

Adaptive Network Selection for Latency-Aware V2X Systems under Varying Network and Vehicle Densities

This paper presents ANS-V2X, an Adaptive Network Selection framework tailored for latency-aware V2X systems operating under varying vehicle densities and heterogeneous network conditions. Modern vehicular environments demand low-latency and high-throughput communication, yet real-time network selection is hindered by diverse application requirements and the coexistence of multiple Radio Access Technologies (RATs) such as 4G, 5G, and ad hoc links. ANS-V2X employs a heuristic-driven approach to assign vehicles to networks by considering application sensitivity, latency, computational load, and directionality constraints. The framework is benchmarked against a Mixed-Integer Linear Programming (MILP) formulation for optimal solutions and a Q-learning-based method representing reinforcement learning. Simulation results demonstrate that ANS-V2X achieves near-optimal performance, typically within 5 to 10% of the utility achieved by MILP-V2X, while reducing execution time by more than 85%. Although MILP-V2X offers globally optimal results, its computation time often exceeds 100 milliseconds, making it unsuitable for real-time applications. The Q-learning-based method is more adaptable but requires extensive training and converges slowly in dynamic scenarios. In contrast, ANS-V2X completes decisions in under 15 milliseconds and consistently delivers lower latency than both alternatives. This confirms its suitability for real-time, edge-level deployment in latency-critical V2X systems

cs.NI↗

arXiv2025

Quantum Skyshield: Quantum Key Distribution and Post-Quantum Authentication for Low-Altitude Wireless Networks in Adverse Skies

Recently, low-altitude wireless networks (LAWNs) have emerged as a critical backbone for supporting the low-altitude economy, particularly with the densification of unmanned aerial vehicles (UAVs) and high-altitude platforms (HAPs). To meet growing data demands, some LAWN deployments incorporate free-space optical (FSO) links, which offer exceptional bandwidth and beam directivity. However, without strong security measures in place, both conventional radio frequency channels and FSO beams remain vulnerable to interception and spoofing and FSO in particular can suffer from turbulence, misalignment, and weather-related attenuation. To address these challenges in the quantum era, a quantum-secure architecture called Quantum Skyshield is proposed to enable reliable communication between the base transceiver station (BTS) and LAWN. The proposed design integrates BB84 quantum key distribution (QKD) with post-quantum authentication mechanisms. Simulation results confirm the reliable generation of a 128-bit symmetric key when the quantum bit error rate (QBER) remains below the threshold of 11%. Authentication is enforced using Lamport one-time signatures and hash-based message authentication codes (HMAC) to ensure message integrity. A Grover-inspired threat detection mechanism identifies anomalies with up to 89% probability in a single iteration, enabling real-time trust evaluation. Lastly, future research challenges have also been identified and discussed to guide further development in this area.

cs.CR↗

arXiv2024

Hierarchical Blockchain Radio Access Networks: Architecture, Modelling, and Performance Assessment

Demands for secure, ubiquitous, and always-available connectivity have been identified as the pillar design parameters of the next generation radio access networks (RANs). Motivated by this, the current contribution introduces a network architecture that leverages blockchain technologies to augment security in RANs, while enabling dynamic coverage expansion through the use of intermediate commercial or private wireless nodes. To assess the efficiency and limitations of the architecture, we employ Markov chain theory in order to extract a theoretical model with increased engineering insights. Building upon this model, we quantify the latency as well as the security capabilities in terms of probability of successful attack, for three scenarios, namely fixed topology fronthaul network, advanced coverage expansion and advanced mobile node connectivity, which reveal the scalability of the blockchain-RAN architecture.

cs.NI↗

arXiv2024

Active RIS-Aided Terahertz Communications with Phase Error and Beam Misalignment

Terahertz (THz) communications will be pivotal in sixth-generation (6G) wireless networks, offering significantly wider bandwidths and higher data rates. However, the unique propagation characteristics of the THz frequency band, such as high path loss and sensitivity to blockages, pose substantial challenges. Reconfigurable intelligent surfaces (RISs) present a promising solution for enhancing THz communications by dynamically shaping the propagation environment to address these issues. Active RISs, in particular, can amplify reflected signals, effectively mitigating the multiplicative fading effects in RIS-aided links. Given the highly directional nature of THz signals, beam misalignment is a significant concern, while discrete phase shifting is more practical for real-world RIS deployment compared to continuous adjustments. This paper investigates the performance of active-RIS-aided THz communication systems, focusing on discrete phase shifts and beam misalignment. An expression for the ergodic capacity is derived, incorporating critical system parameters to assess performance. Numerical results offer insights into optimizing active-RIS-aided THz communication systems.

cs.NI↗

arXiv2024

Optimal Operation of Active RIS-Aided Wireless Powered Communications in IoT Networks

Wireless-powered communications (WPCs) are increasingly crucial for extending the lifespan of low-power Internet of Things (IoT) devices. Furthermore, reconfigurable intelligent surfaces (RISs) can create favorable electromagnetic environments by providing alternative signal paths to counteract blockages. The strategic integration of WPC and RIS technologies can significantly enhance energy transfer and data transmission efficiency. However, passive RISs suffer from double-fading attenuation over RIS-aided cascaded links. In this article, we propose the application of an active RIS within WPC-enabled IoT networks. The enhanced flexibility of the active RIS in terms of energy transfer and information transmission is investigated using adjustable parameters. We derive novel closed-form expressions for the ergodic rate and outage probability by incorporating key parameters, including signal amplification, active noise, power consumption, and phase quantization errors. Additionally, we explore the optimization of WPC scenarios, focusing on the time-switching factor and power consumption of the active RIS. The results validate our analysis, demonstrating that an active RIS significantly enhances WPC performance compared to a passive RIS.

cs.NI↗

arXiv2024

Active Reconfigurable Intelligent Surface-Aided Terahertz Wireless Communications

Terahertz (THz) communication is expected to be a key technology for future sixth-generation (6G) wireless networks. Furthermore, reconfigurable intelligent surfaces (RIS) have been proposed to modify the wireless propagation environment and enhance system performance. Given the sensitivity to blockages and limited coverage range, RIS is particularly promising for THz communications. Active RIS can overcome the multiplicative fading effect in RIS-aided communications. In this paper, we explore active RIS-assisted THz communications. We formulate the ergodic rate, considering factors associated with active RIS, including active noise and signal amplification, and THz signals, including molecular absorption and beam misalignment

cs.NI↗

arXiv2024

Adversarial Attacks and Defenses in 6G Network-Assisted IoT Systems

The Internet of Things (IoT) and massive IoT systems are key to sixth-generation (6G) networks due to dense connectivity, ultra-reliability, low latency, and high throughput. Artificial intelligence, including deep learning and machine learning, offers solutions for optimizing and deploying cutting-edge technologies for future radio communications. However, these techniques are vulnerable to adversarial attacks, leading to degraded performance and erroneous predictions, outcomes unacceptable for ubiquitous networks. This survey extensively addresses adversarial attacks and defense methods in 6G network-assisted IoT systems. The theoretical background and up-to-date research on adversarial attacks and defenses are discussed. Furthermore, we provide Monte Carlo simulations to validate the effectiveness of adversarial attacks compared to jamming attacks. Additionally, we examine the vulnerability of 6G IoT systems by demonstrating attack strategies applicable to key technologies, including reconfigurable intelligent surfaces, massive multiple-input multiple-output (MIMO)/cell-free massive MIMO, satellites, the metaverse, and semantic communications. Finally, we outline the challenges and future developments associated with adversarial attacks and defenses in 6G IoT systems.

cs.IT↗

arXiv2024

Outage Analysis for Active Reconfigurable Intelligent Surface-Enhanced Wireless Powered Communication Networks

Wireless powered communication (WPC) involves the integration of energy harvesting and data transmission. This allows devices to communicate without constant battery replacements or wired power sources. Reconfigurable intelligent surfaces (RISs) can dynamically manipulate radio signals. In this paper, we explore the use of active elements to mitigate double-fading challenges inherent in RIS-aided links. We enhance the reliability performance for an energy-constrained user by combining active RIS and WPC. The theoretical closed-form analysis, which includes transmission rate, harvested energy, and outage probability, provides valuable insights that inform parameter selection.

cs.NI↗

arXiv2024

Efficient UAVs Deployment and Resource Allocation in UAV-Relay Assisted Public Safety Networks for Video Transmission

Wireless communication highly depends on the cellular ground base station (GBS). A failure of the cellular GBS, fully or partially, during natural or man-made disasters creates a communication gap in the disaster-affected areas. In such situations, public safety communication (PSC) can significantly save the national infrastructure, property, and lives. Throughout emergencies, the PSC can provide mission-critical communication and video transmission services in the affected area. Unmanned aerial vehicles (UAVs) as flying base stations (UAV-BSs) are particularly suitable for PSC services as they are flexible, mobile, and easily deployable. This manuscript considers a multi-UAV-assisted PSC network with an observational UAV receiving videos from the affected area's ground users (AGUs) and transmitting them to the nearby GBS via a relay UAV. The objective of the proposed study is to maximize the average utility of the video streams generated by the AGUs upon reaching the GBS. This is achieved by optimizing the positions of the observational and relay UAVs, as well as the distribution of communication resources, such as bandwidth, and transmit power, while satisfying the system-designed constraints, such as transmission rate, rate outage probability, transmit power budget, and available bandwidth. To this end, a joint UAVs placement and resource allocation problem is mathematically formulated. The proposed problem poses a significant challenge for a solution. Considering the block coordinate descent and successive convex approximation techniques, an efficient iterative algorithm is proposed. Finally, simulation results are provided which show that our proposed approach outperforms the existing methods.

cs.NI↗

arXiv2024

Impact of RIS on Outage Probability and Ergodic Rate in Wireless Powered Communication

Wireless powered communication (WPC) combines information and energy transmission for energy-constrained nodes. Reconfigurable intelligent surfaces (RISs) are capable of controlling radio signals in a dynamic and goal-oriented manner. This paper investigates the combination of RIS and WPC to enhance the performance of an energy-constrained user. Using an RIS, a base station, and a wireless user transmit energy and information signals, respectively. We derive closed-form expressions for outage probability and secrecy rate to analyze the performance of the proposed framework. Based on the theoretical analysis and simulation results, valuable insights are revealed and parameter selection is demonstrated.

cs.IT↗