arXiv subjects

Khalid Qaraqe

Publications and source records attributed to Khalid Qaraqe.

At least 37 records · Page 2Linked to original sources

arXiv2025

Physical Limits of Entanglement-Based Quantum Key Distribution over Long-Distance Satellite Links

Entanglement-based quantum key distribution (QKD) protocols, such as E91 and BBM92, offer strong information-theoretic security and are naturally suited for satellite-to-satellite QKD (SatQKD) links. However, implementing these protocols over long-distance inter-satellite free-space optical (FSO) channels poses critical physical-layer challenges that are not addressed in the existing literature. In particular, photon losses due to beam divergence, pointing errors, and background noise can severely degrade the key generation rate and quantum bit error rate (QBER), especially under narrow receiver field-of-view (FoV) constraints. This paper presents a comprehensive performance analysis of entanglement-based inter-satellite QKD, focusing on photon-level modeling and the impact of practical impairments. We develop analytical expressions for signal detection probabilities, background photon influence, multi-pair emissions, and QBER, incorporating key parameters such as link distance, transmitter tracking jitter, receiver misalignment, and photon pair generation rate. Simulation results reveal the nonlinear sensitivity of system performance to tracking error and FoV limitations, and highlight optimal parameter regimes that jointly maximize secret key rate while maintaining QBER below acceptable thresholds. The proposed model provides actionable design insights for reliable and efficient deployment of entanglement-based SatQKD systems.

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arXiv2024

Optimizing UAV Trajectory for Emergency Response Operations under Real 3D Environments: Integrating Priority Levels and LoS Constraints

Unmanned Aerial Vehicles (UAVs) have emerged as a critical component in next-generation wireless networks, particularly for disaster recovery scenarios, due to their flexibility, mobility, and rapid deployment capabilities. This paper focuses on optimizing UAV trajectories to ensure effective communication in disaster-stricken areas using terahertz (THz) links. We address specific challenges such as energy consumption, user priority levels, and navigating complex urban environments to maintain Line of Sight (LoS) connections amidst 3D obstacles. Our contributions include the development of a detailed modeling approach using online 3D map data, the formulation of an optimal trajectory optimization problem, and the proposal of a Genetic Algorithm (GA)-based method alongside an enhanced heuristic algorithm for faster convergence. Through 3D simulations, we demonstrate the trade-off between minimizing total service time and prioritizing higher-weight nodes, showing the impact of different priority weight factors on the trajectory time. The proposed algorithms are evaluated using real-world data from the West Bay area of Doha, Qatar, demonstrating their effectiveness in optimizing UAV trajectories for emergency response.

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arXiv2024

LoS Coverage Analysis for UAV-based THz Communication Networks: Towards 3D Visualization of Wireless Networks

Terahertz (THz) links require a line-of-sight (LoS) connection, which is hard to be obtained in most scenarios. For THz communications, analyses based on LoS probability are not accurate, and a new real LoS model should be considered to determine the LoS status of the link in a real 3D environment. Considering unmanned aerial vehicle (UAV)-based THz networks, LoS coverage is analyzed in this work, where nodes are accurately determined to be in LoS or not. Specifically, by modeling an environment with 3D blocks, our target is to locate a set of UAVs equipped with directional THz links to provide LoS connectivity for the distributed users among the 3D obstacles. To this end, we first characterize and model the environment with 3D blocks. Then, we propose a user-friendly algorithm based on 3D spatial vectors, which determines the LoS status of nodes in the target area. In addition, using 3D modeling, several meta-heuristic algorithms are proposed for UAVs' positioning under 3D blocks in order to maximize the LoS coverage percentage. In the second part of the paper, for a UAV-based THz communication network, a geometrical analysis-based algorithm is proposed, which jointly clusters the distributed nodes and locates the set of UAVs to maximize average network capacity while ensuring the LoS state of distributed nodes among 3D obstacles. Moreover, we also propose a sub-optimal hybrid k-means-geometrical-based algorithm with a low computational complexity that can be used for networks where the topology continuously changes, and thus, users' clustering and UAVs' positioning need to be regularly updated. Finally, by providing various 3D simulations, we evaluate the effect of various system parameters such as the number and heights of UAVs, as well as the density and height of 3D obstacles on the LoS coverage.

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arXiv2024

Modulating Retroreflector-based Satellite-to-Ground Optical Communications: Sensing and Positioning

This paper focuses on the optimal design of a modulated retroreflector (MRR) laser link to establish a high-speed downlink for cube satellites (CubeSats), taking into account the weight and power limitations commonly encountered by these tiny satellites. To this end, first, a comprehensive channel modeling is conducted considering key real channel parameters including mechanical gimbal error, fast steering mirror angle error, laser beamwidth, MRR area, atmospheric turbulence, and channel coherence time. Accordingly, a closed-form expression for the distribution of the received signal is derived and utilized to propose a maximum likelihood based method to sense and estimate the initial position of the satellite. Subsequently, the distribution of the distance estimation error during the sensing phase is formulated as a function of the laser beamwidth and the gimbal error, which enables us to fine-tune the optimal laser beamwidth to minimize sensing time. Moreover, using the sensing and initial satellite distance estimation, two positioning algorithms are proposed. To compare the performance of the proposed positioning method, we obtain the lower bound of the positioning error as a benchmark. Finally, by providing comprehensive simulations, we evaluate the effect of different parameters on the performance of the considered MRR-based system in both the sensing and positioning phases.

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arXiv2023

RIDNet Assisted cGAN Based Channel Estimation for One-Bit ADC mmWave MIMO Systems

The estimation of millimeter wave (mmWave) massive multiple input multiple output (MIMO) channels becomes compelling when one-bit analog to digital converters (ADCs) are utilized. Furthermore, as the number of antenna increases, pilot overhead scales up to provide consistent channel estimation, eventually degrading spectral efficiency. This study presents a channel estimation approach that combines a conditional generative adversarial network (cGAN) with a novel blind denoising network with a sparse feature attention mechanism. Performance analysis and simulations show that using a cGAN fused with a feature attention-based denoising neural network significantly enhances the channel estimation performance while requiring less pilot transmission.

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arXiv2023

Channel Estimation Using RIDNet Assisted OMP for Hybrid-field THz Massive MIMO Systems

The terahertz (THz) band radio access with larger available bandwidth is anticipated to provide higher capacities for next-generation wireless communication systems. However, higher path loss at THz frequencies significantly limits the wireless communication range. Massive multiple-input multiple-output (mMIMO) is an attractive technology to increase the Rayleigh distance by generating higher gain beams using low wavelength and highly directive antenna array aperture. In addition, both far-field and near-field components of the antenna system should be considered for modelling THz electromagnetic propagation, where the channel estimation for this environment becomes a challenging task. This paper proposes a novel channel estimation method using a recursive information distillation network (RIDNet) together with orthogonal matching pursuit (OMP) for hybrid-field THz mMIMO channels, including both far-field and near-field components. The simulation experiments are performed using the ray-tracing tool. The results indicate that the proposed RIDNet-based method consistently provides lower channel estimation errors compared to the conventional OMP algorithm for all signal-to-noise ratio (SNR) regimes, and the performance gap becomes higher at low SNR regimes. Furthermore, the results imply that the same error performance of the OMP can be achieved by the RIDNet-based method using a lower number of RF chains and pilot symbols.

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arXiv2022

A Study of Multihop mmW Aerial Backhaul Links

The main contribution of this paper is to analyze a long networked flying platform (NFP)-based millimeter wave (mmWave) backhaul link that is offered as a cost effective and easy to deploy solution to connect a disaster or remote area to the nearest core network. For this aim, we characterize the backhaul channel as a function of realistic physical parameters such as heights and distances of obstacles along the route, flight altitude and the intensity of NFPs' vibrations, the real 3D antenna pattern provided by 3GPP, etc. For the characterized channel, we derive an analytical closed-form expression for the outage probability. Finally, using the obtained results, we provide a fast algorithm for the optimal parameter design of the considered system that minimizes the cost.

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arXiv2021

On the Effective Capacity of IRS-assisted wireless communication

We consider futuristic, intelligent reflecting surfaces (IRS)-aided communication between a base station (BS) and a user equipment (UE) for two distinct scenarios: a single-input, single-output (SISO) system whereby the BS has a single antenna, and a multi-input, single-output (MISO) system whereby the BS has multiple antennas. For the considered IRS-assisted downlink, we compute the effective capacity (EC), which is a quantitative measure of the statistical quality-of-service (QoS) offered by a communication system experiencing random fading. For our analysis, we consider the two widely-known assumptions on channel state information (CSI) -- i.e., perfect CSI and no CSI, at the BS. Thereafter, we first derive the distribution of the signal-to-noise ratio (SNR) for both SISO and MISO scenarios, and subsequently derive closed-form expressions for the EC under perfect CSI and no CSI cases, for both SISO and MISO scenarios. Furthermore, for the SISO and MISO systems with no CSI, it turns out that the EC could be maximized further by searching for an optimal transmission rate $r^*$, which is computed by exploiting the iterative gradient-descent method. We provide extensive simulation results which investigate the impact of the various system parameters, e.g., QoS exponent, power budget, number of transmit antennas at the BS, number of reflective elements at the IRS etc., on the EC of the system.

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arXiv2019

In Vivo Communications: Steps Toward the Next Generation of Implantable Devices

In vivo wireless medical devices have the potential to play a vital role in future healthcare technologies by improving the quality of human life. In order to fully exploit the capabilities of such devices, it is necessary to characterize and model the in vivo wireless communication channel. Utilization of this model will have a significant role in improving the communication performance of embedded medical devices in terms of power, reliability and spectral efficiency. In this paper, the state of the art in this field is presented to provide a comprehensive understanding of current models. Such knowledge will be used to optimize the design and selection of various in vivo wireless communication methods, operational frequencies, and antenna design. Finally, open research areas are discussed for future studies.

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arXiv2018

Deep Recurrent Electricity Theft Detection in AMI Networks with Random Tuning of Hyper-parameters

Modern smart grids rely on advanced metering infrastructure (AMI) networks for monitoring and billing purposes. However, such an approach suffers from electricity theft cyberattacks. Different from the existing research that utilizes shallow, static, and customer-specific-based electricity theft detectors, this paper proposes a generalized deep recurrent neural network (RNN)-based electricity theft detector that can effectively thwart these cyberattacks. The proposed model exploits the time series nature of the customers' electricity consumption to implement a gated recurrent unit (GRU)-RNN, hence, improving the detection performance. In addition, the proposed RNN-based detector adopts a random search analysis in its learning stage to appropriately fine-tune its hyper-parameters. Extensive test studies are carried out to investigate the detector's performance using publicly available real data of 107,200 energy consumption days from 200 customers. Simulation results demonstrate the superior performance of the proposed detector compared with state-of-the-art electricity theft detectors.

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arXiv2017

Cooperative In-Vivo Nano-Network Communication at Terahertz Frequencies

Nano devices have great potential to play a vital role in future medical diagnostics and treatment technologies because of its non-invasive nature and ability to reach delicate body sites easily as compared to conventional devices. In this paper, a novel concept of cooperative communication for in-vivo nano-network is presented to enhance the communication among these devices. The effect on the system outage probability performance is conducted for various parameters including relay lacement, number of relays, transmit power, bandwidth and carrier frequency. Results show approximately a 10-fold increase in the system outage performance whenever an additional relay is included in the cooperative network, hence show a great potential of using cooperative communication to enhance the performance of nano-network at terahertz frequencies.

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arXiv2016

A Unifying Variational Perspective on Some Fundamental Information Theoretic Inequalities

This paper proposes a unifying variational approach for proving and extending some fundamental information theoretic inequalities. Fundamental information theory results such as maximization of differential entropy, minimization of Fisher information (Cramér-Rao inequality), worst additive noise lemma, entropy power inequality (EPI), and extremal entropy inequality (EEI) are interpreted as functional problems and proved within the framework of calculus of variations. Several applications and possible extensions of the proposed results are briefly mentioned.

cs.IT↗

arXiv2015

A Stochastic Sizing Approach for Sharing-based Energy Storage Applications

In order to foster renewable energy integration, improve power quality and reliability, and reduce hydrocarbon emissions, there is a strong need to deploy energy storage systems (ESSs), which can provide a control medium for peak hour utility operations. ESSs are especially desired at the residential level, as this sector has the most untapped demand response potential. However, considering their high acquisition, operation, and maintenance costs, individual ESS deployment is not economically viable. Hence, in this paper, we propose a \emph{sharing-based} ESS architecture, in which the demand of each customer is modeled stochastically and the aggregate demand is accommodated by a combination of power drawn from the grid and the storage unit when the demand exceeds grid capacity. Stochastic framework for analyzing the optimal size of energy storage systems is provided. An analytical method is developed for a group customers with \emph{single} type of appliances. Then, this framework is extended to any network size with arbitrary number of customers and appliance types. The analytical method provides a tractable solution to the ESS sizing problem. Finally, a detailed cost-benefit analysis is provided, and the results indicate that sharing-based ESSs are practical and significant savings in terms of ESS size can be achieved.

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arXiv2015

Capacity Planning Frameworks for Electric Vehicle Charging Stations with Multi-Class Customers

In order to foster electric vehicle (EV) adoption, there is a strong need for designing and developing charging stations that can accommodate different customer classes, distinguished by their charging preferences, needs, and technologies. By growing such charging station networks, the power grid becomes more congested and, therefore, controlling of charging demands should be carefully aligned with the available resources. This paper focuses on an EV charging network equipped with different charging technologies and proposes two frameworks. In the first framework, appropriate for large networks, the EV population is expected to constitute a sizable portion of the light duty fleets. This which necessitates controlling the EV charging operations to prevent potential grid failures and distribute the resources efficiently. This framework leverages pricing dynamics in order to control the EV customer request rates and to provide a charging service with the best level of quality of service. The second framework, on the other hand, is more appropriate for smaller networks, in which the objective is to compute the minimum amount of resources required to provide certain levels of quality of service to each class. The results show that the proposed frameworks ensure grid reliability and lead to significant savings in capacity planning.

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arXiv2015

Energy Storage System Sizing for Peak Hour Utility Applications

In future smart grids, energy storage systems (ESSs) are expected to play a key role in reducing peak hour electricity generation cost and the associated level of carbon emissions. Considering their high acquisition, operation, and maintenance costs, ESSs are likely to serve a large number of users. Hence, optimal sizing of energy ESSs plays a critical role as over-provisioning ESS size leads to under-utilizing costly assets and under-provisioning it taxes operation lifetime. This paper proposes a stochastic framework for analyzing the optimal size of energy storage systems. In this framework the demand of each customer is modeled stochastically and the aggregate demand is accommodated by a combination of power drawn from the grid and the storage unit when the demand exceed grid capacity. In this framework an analytical method is developed, which provides tractable solution to the ESS sizing problem of interest. The results indicate that significant savings in terms of ESS size can be achieved.

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arXiv2014

A Survey on Energy Trading in Smart Grid

As the distributed energy generation and storage technologies are becoming economically viable, energy trading is gradually becoming a profit making option for end-users. This trend is further supported by the regulators and the policy makers as it aids the efficiency of power grid operations, reduces power generation cost and the Green House Gas (GHG) emissions. To that end, in this paper we provide an overview of distributed energy trading concepts in smart grid. First, we identify the motivation and the desired outcomes of energy trading framework. Then we present the enabling technologies that are required to generate, store, and communicate with the trading agencies. Finally, we survey on the existing literature and present an array of mathematical frameworks employed.

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arXiv2012

An Alternative Proof of an Extremal Entropy Inequality

This paper first focuses on deriving an alternative approach for proving an extremal entropy inequality (EEI), originally presented in [11]. The proposed approach does not rely on the channel enhancement technique, and has the advantage that it yields an explicit description of the optimal solution as opposed to the implicit approach of [11]. Compared with the proofs in [11], the proposed alternative proof is also simpler, more direct, more information-theoretic, and has the additional advantage that it offers a new perspective for establishing novel as well as known challenging results such the capacity of the vector Gaussian broadcast channel, the lower bound of the achievable rate for distributed source coding with a single quadratic distortion constraint, and the secrecy capacity of the Gaussian wire-tap channel. The second part of this paper is devoted to some novel applications of the proposed mathematical results. The proposed mathematical techniques are further exploited to obtain a more simplified proof of the EEI without using the entropy power inequality (EPI), to build the optimal solution for a special class of broadcasting channels with private messages and to obtain a mutual information-based performance bound for the mean square-error of a linear Bayesian estimator of a Gaussian source embedded in an additive noise channel.

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arXiv2012

Gaussian Assumption: the Least Favorable but the Most Useful

This paper focuses on three contributions. First, a connection between the result, proposed by Stoica and Babu, and the recent information theoretic results, the worst additive noise lemma and the isoperimetric inequality for entropies, is illustrated. Second, information theoretic and estimation theoretic justifications for the fact that the Gaussian assumption leads to the largest Cramér-Rao lower bound (CRLB) is presented. Third, a slight extension of this result to the more general framework of correlated observations is shown.

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