arXiv subjects

M. Umar Khan

Publications and source records attributed to M. Umar Khan.

6 recordsLinked to original sources

arXiv2026

Data-Driven Optimization of Multi-Generational Cellular Networks: A Performance Classification Framework for Strategic Infrastructure Management

The exponential growth in mobile data demand necessitates intelligent management of telecommunications infrastructure to ensure Quality of Service (QoS) and operational efficiency. This paper presents a comprehensive analysis of a multigenerational cellular network dataset, sourced from the OpenCelliD project, to identify patterns in network deployment, utilization, and infrastructure gaps. The methodology involves geographical, temporal, and performance analysis of 1,818 cell tower entries, predominantly Long Term Evolution (LTE), across three countries with a significant concentration in Pakistan. Key findings reveal the long-term persistence of legacy 2G/3G infrastructure in major urban centers, the existence of a substantial number of under-utilized towers representing opportunities for cost savings, and the identification of specific "non-4G demand zones" where active user bases are served by outdated technologies. By introducing a signal-density metric, we distinguish between absolute over-utilization and localized congestion. The results provide actionable intelligence for Mobile Network Operators (MNOs) to guide strategic LTE upgrades, optimize resource allocation, and bridge the digital divide in underserved regions.

cs.NI↗

arXiv2025

MILP-driven Network Planning Framework for Energy Efficiency and Coverage Maximization in IoT Mesh Networks

In the era of digital transformation, the global deployment of internet of things (IoT) networks and wireless sensor networks (WSNs) is critical for applications ranging from environmental monitoring to smart cities. Large-scale monitoring using WSNs incurs high costs due to the deployment of sensor nodes in the target deployment area. In this paper, we address the challenge of prohibitive deployment costs by proposing an integrated mixed-Integer linear programming (MILP) framework that strategically combines static and mobile Zigbee nodes. Our network planning approach introduces three novel formulations, including boundary-optimized static node placement (MILP-Static), mobile path planning for coverage maximization (MILP-Cov), and movement minimization (MILP-Mov) of the mobile nodes. We validated our framework with extensive simulations and experimental measurements of Zigbee power constraints. Our results show that boundary-optimized static placement (MILP-Static) achieves 53.06% coverage compared with 33.42% of the random approach. In addition, MILP-Cov for path planning reaches 97.95% coverage, while movement minimization (MILP-Mov) reduces traversal cost by 40%. Our proposed framework outperforms the benchmark approaches to provide a foundational solution for cost-effective global IoT deployment in resource constrained environments.

cs.NI↗

arXiv2025

A Framework for Detection and Classification of Attacks on Surveillance Cameras under IoT Networks

The increasing use of Internet of Things (IoT) devices has led to a rise in security related concerns regarding IoT Networks. The surveillance cameras in IoT networks are vulnerable to security threats such as brute force and zero-day attacks which can lead to unauthorized access by hackers and potential spying on the users activities. Moreover, these cameras can be targeted by Denial of Service (DOS) attacks, which will make it unavailable for the user. The proposed AI based framework will leverage machine learning algorithms to analyze network traffic and detect anomalous behavior, allowing for quick detection and response to potential intrusions. The framework will be trained and evaluated using real-world datasets to learn from past security incidents and improve its ability to detect potential intrusion.

cs.CR↗

arXiv2025

Transforming Next-generation Network Planning assisted by Data Acquisition of Top Three Spanish MNOs

In this paper, we address the necessity of data related to mobile traffic of the legacy infrastructure to extract useful information and perform network dimensioning for 5G. These data can help us achieve a more efficient network planning design, especially in terms of topology and cost. To that end, a real open database of top three Spanish mobile network operators (MNOs) is used to estimate the traffic and to identify the area of highest user density for the deployment of new services. We propose the data acquisition procedure described to clean the database, to extract meaningful traffic information and to visualize traffic density patterns for new gNB deployments. We present the state of the art in Network Data. We describe the considered network database in detail. The Network Data Acquisition entity along with the proposed procedure is explained. The corresponding results are discussed, following the conclusions.

cs.NI↗

arXiv2025

Fundamentals of Next-generation Network Planning

The fifth-generation (5G) of cellular communications is expected to be deployed in the next years to support a wide range of services with different demands of peak data rates, latency and quality of experience (QoE). To support higher data rates and latency requirements third-generation partnership project (3GPP) has introduced numerology and bandwidth parts (BWPs), via new radio (NR) for service-tailored resource allocation. Legacy 4G networks have generated extensive data, which combined with crowd-sourced LTE infrastructure insights, enables identification of high-traffic 5G deployment area (5GDA) for planning new services. Given the mission-critical nature of 5G services, QoE is a big challenge for MNOs to guarantee peak data rates for a defined percentage of time. This work studies the fundamentals of 5G network planning methods that reconciles coverage-capacity trade-offs through balanced radio network dimensioning (RND), leveraging pragmatic NR modeling, and data-driven strategies to minimize deployment costs and reduce cost-per-bit.

cs.NI↗

arXiv2023

Tunneling and thermodynamics evolution of the magnetized Ernst-like black hole

We investigate the tunneling phenomenon of particles through the horizon of a magnetized Ernst-like black hole. We employ the modified Lagrangian equation with the extended uncertainty principle for this black hole. We determine a tunneling rate and the related Hawking temperature for this black hole by using the WKB approach in the field equation. In addition, we examine the graph behavior of the Hawking temperature in relation to the black hole event horizon. We explore the stability analysis of this black hole by taking into account the impact of quantum gravity on Hawking temperatures. The temperature for a magnetized Ernst-like black hole rises as the correction parameter is decreased. Moreover, we analyze the thermodynamics quantities such as Hawking temperature, heat capacity and Bekenstein entropy by using the different approach. We obtain the corrected entropy to study the impact of logarithmic corrections on the different thermodynamic quantities. It is shown that these correction terms makes the system stable under thermal fluctuations.

gr-qc↗