Cellular Network Coverage Research Articles

Joint Metrics for EMF Exposure and Coverage in Real-World Homogeneous and Inhomogeneous Cellular Networks

Joint Metrics for EMF Exposure and Coverage in Real-World Homogeneous and Inhomogeneous Cellular Networks

A network-based mobile positioning system using an optimization model

The expansion of cellular network coverage facilitates the advancement of research on network-based positioning. We are interested in the signal fingerprinting method to predict the location of a mobile device. By this method, the device must be within the fingerprint coverage to have a successful location prediction. However, any disturbance in the signal propagation would decrease the prediction accuracy. We propose an optimization model based on generalized triangulation combined with a signal fingerprint which is treated more adaptively in responding to any signal disturbance. The triangulation method determines the most likely region where the device is located. The solution provides the estimated longitude and latitude of the device. An illustration of the implementation of the model is presented. The model is assessed using the Indosat cellular network in three distinct testbeds in Indonesia, which are: South Jakarta, a metropolitan area; South Tangerang, a buffer area adjacent to the metropolitan area; and Malang, a city surrounded by rural areas. The most favorable outcome yields an average prediction error of 39.6 m, a maximum error of 197.08 m, a minimum error of 0.05 m, and a standard deviation of error of 39.22 m.

AHA-BV: Access and handover authentication protocol with batch verification for satellite–terrestrial integrated networks

At present, the rapid development of satellite capabilities has prompted the proposal of satellite–terrestrial integrated networks (STIN), which solves the problem of limited signal coverage of terrestrial cellular networks, further promotes the globalization process, and realizes global data sharing and on-demand use. However, due to the high openness of satellite-to-ground links in STIN, users are vulnerable to attacks such as eavesdropping, replay, tampering, and impersonation when requesting access to satellite nodes and obtaining subscription services. To ensure the security and reliability, many authentication protocols have been proposed, but there are still some shortcomings, such as high authentication overhead, vulnerability to certain attacks. In addition, for inter-satellite handovers caused by the highly dynamic topology of satellites, the computational overhead of existing handover authentication mechanisms is too high to be applied to frequent inter-satellite handover scenarios in STIN. To address the above issues, in this paper, we propose a new access and handover authentication protocol with batch verification for STIN, namely the AHA-BV protocol. The AHA-BV protocol not only realizes mutual authentication and key negotiation between users and satellite access points without the participation of the network control center, but also ensures the conditional anonymity of users during the access authentication phase. Furthermore, the lightweight batch verification mechanism reduces the risk of computing bottlenecks when resource-constrained satellites receive a large number of access authentication requests. Not only that, the AHA-BV protocol can also achieve sustained trust in subscription services from STIN with low computational overhead during the inter-satellite handover authentication phase. Formal and heuristic security analysis show that the AHA-BV protocol can meet the security requirements of STIN. Performance analysis indicates that the AHA-BV protocol has low authentication overhead while ensuring security, and is more suitable for users under satellite dynamic topology to access and obtain subscription services from STIN.

Design and implementation of an edge computing-based GPS tracking system

This paper presents a comprehensive study on the technical aspects of edge-enabled GPS tracking systems, their evolution, and an analysis of existing problem-solving approaches. It introduces mathematical models that simulate the operation of the hardware and software components of these systems. An adaptive user interface has been developed, enabling usage across various platforms such as smartphones and personal computers. The paper also explores innovative methods for visualizing the trajectory of a moving object on an electronic map. Specialized software for the Atmega162-16PU microcontroller has been developed to control the GSM module and GPS receiver. A novel method for data transmission from a GPS tracker to a web server is proposed. The paper concludes with the successful testing of two experimental GPS-tracker prototypes under uncertain conditions. The developed edge-enabled GPS tracking software and hardware show potential for monitoring moving objects within the coverage of GSM cellular networks.

Acceptability, Usefulness, and Ease of Use of an Enhanced Video Directly Observed Treatment System for Supporting Patients With Tuberculosis in Kampala, Uganda: Explanatory Qualitative Study.

In tuberculosis (TB) control, nonadherence to treatment persists as a barrier. The traditional method of ensuring adherence, that is, directly observed therapy, faces significant challenges that hinder its widespread adoption. Digital adherence technologies such as video directly observed therapy (VDOT) are emerging as promising solutions. However, as these novel technologies gain momentum, a critical gap is the lack of comprehensive studies evaluating their efficacy and the unique experiences of patients in Africa. The aim of this study was to assess patients' experiences that affected acceptability, usefulness, and ease of use with an enhanced VDOT system during monitoring of TB treatment. We conducted individual open-ended interviews in a cross-sectional exit qualitative study in Kampala, Uganda. Thirty participants aged 18-65 years who had completed the VDOT randomized trial were purposively selected to represent variability in sex, adherence level, and HIV status. We used a hybrid process of deductive and inductive coding to identify content related to the experience of study participation with VDOT. Codes were organized into themes and subthemes, which were used to develop overarching categories guided by constructs adapted from the modified Technology Acceptance Model for Resource-Limited Settings. We explored participants' experiences regarding the ease of use and usefulness of VDOT, thereby identifying the facilitators and barriers to its acceptability. Perceived usefulness refers to the benefits users expect from the technology, while perceived ease of use refers to how easily users navigate its various features. We adapted by shifting from assessing perceived to experienced constructs. The participants' mean age was 35.3 (SD 12) years. Of the 30 participants, 15 (50%) were females, 13 (43%) had low education levels, and 22 (73%) owned cellphones, of which 10 (45%) had smartphones. Nine (28%) were TB/HIV-coinfected, receiving antiretroviral therapy. Emergent subthemes for facilitators of experienced usefulness and ease of VDOT use were SMS text message reminders, technology training support to patients by health care providers, timely patient-provider communication, family social support, and financial incentives. TB/HIV-coinfected patients reported the added benefit of adherence support for their antiretroviral medication. The external barriers to VDOT's usefulness and ease of use were unstable electricity, technological malfunctions in the app, and lack of cellular network coverage in rural areas. Concerns about stigma, disease disclosure, and fear of breach in privacy and confidentiality affected the ease of VDOT use. Overall, participants had positive experiences with the enhanced VDOT. They found the enhanced VDOT system user-friendly, beneficial, and acceptable, particularly due to the supportive features such as SMS text message reminders, incentives, technology training by health care providers, and family support. However, it is crucial to address the barriers related to technological infrastructure as well as the privacy, confidentiality, and stigma concerns related to VDOT.

5G Cellular Network Coverage with KUD-based SC Deployment

5G Cellular Network Coverage with KUD-based SC Deployment

Monitoring and control of smart urban drainage systems using NB-IoT cellular sensor networks.

Narrow-Band-Internet of Things (NB-IoT) is a promising wireless communication technology for harsh signal propagation conditions, including underground connectivity. The purpose of this study is to evaluate the real-life signal quality of an NB-IoT cellular sensor network for the monitoring and control of urban drainage systems. The implementation of the control systems toward smart infrastructures increase the need for low-cost and low-energy-consumption underground sensing devices. A measurement campaign was conducted in Rakvere, Estonia, to analyze the NB-IoT network's coverage and underground signal quality. The results were used to analyze the flood risk reduction potential in the pilot catchment, considering the quality of NB-IoT cellular network coverage. The signal measurements showed that in the case of plastic manholes, the best cellular transmitter placement is 0.5 m below the manhole cover, and at the ground level NB-RSRP reference signal strength of -104 dBm or better must be ensured to have a link budget with optimal energy consumption for communication. The results of flood risk reduction showed that excluding control locations with insufficient signal strength increased the number of major flooding nodes by 3.9% and mean flooding duration by 4.2% which is a relatively small change and characterizes the flexibility of the developed control system.

Data-Driven Self-Organization With Implicit Self-Coordination for Coverage and Capacity Optimization in Cellular Networks

Coverage and Capacity Optimization (CCO) and Inter-Cell Interference Coordination (ICIC) are two tightly coupled and conflicting Self-Organizing Network (SON) functions that are responsible for ensuring optimal coverage and capacity in any cellular network. While executing currently, these functions may modify the same RF and antenna parameters, resulting in severe performance deteriorations. In this context, a centralized optimization and coordination approach may be impractical considering the large sizes of network clusters and the dynamics involved between the several other defined SON use cases. In this work, an implicitly coordinated and scalable self-organizing architecture is followed such that when a carefully defined multi-objective utility function for CCO-ICIC joint optimization is optimized locally by each RAN node, a desired balance between the two conflicting network targets of coverage and capacity is ensured globally. Pareto analysis of three variants of the proposed Local Multi-Objective KPI (LMO KPI) has been conducted to implicitly coordinate the two SON functions in a distributed self-organized manner. In order to recommend appropriate network configurations dynamically to quickly adapt to altering network environments, two collaborative filtering-based Recommender Systems (RecSys), one using a Deep Autoencoder and another based on Singular Value Decomposition, have been employed along with a neural network regressor to improve recommendations for cold-start scenarios. The two proposed hybrid-RecSys-based SON coordination solutions, while adopting an appropriate Local Multi-Objective KPI (LMO KPI), outperform previous work in coverage by 36% and in capacity by around 2% while reducing power consumption by more than 50%. The study demonstrates that the definition of the LMO KPI is crucial to the performance of this approach. Altogether, the work shows that the adopted self-organization and implicit SON-coordination approach is not only feasible and performant but also scales well if implemented meticulously.

Integrating social and ecological information to identify high-risk areas of human-crocodile conflict in the Indonesian Archipelago

Crocodile attacks on humans and subsequent retaliations are a pressing issue for saltwater crocodile conservation. As human-crocodile conflict is complex, integrating social and ecological information better explains the drivers and patterns of these interactions. Our study aims to incorporate ecological factors associated with the intensity of crocodile attacks together with social factors of mass media reports to identify high-risk areas of human-crocodile conflict in Indonesia. We compiled reports of crocodile attacks in the 2010–2019 period from media reports, field surveys, and local informants. The presence of attack was estimated by evaluating the influence of habitat, climate, human, and reporting effort. As tone of media coverage can reflect and shape reader's tolerance about a certain issue, we assessed the headline's tone from each media article that reported crocodile attacks from 2017 to 2019. A total of 665 crocodile attacks were recorded and mainly distributed in western and central Indonesia. The estimated number of crocodile attacks was higher in areas with lower forest biomass and human density, and wider cellular network coverage. Negative media coverages were frequently reported in western Indonesia. By combining social information of negative media reporting and the ecological information of crocodile attacks hotspots, we identified 170,500 km2 priority risk areas in the western part of Indonesia, a notable 65.8 % reduction in area size compared to the attack hotspots. We highlight the application of socio-ecological information in risk prioritization to address the rising trends of negative human-wildlife interactions.

Impact of various System Design Parameters on Coverage in Heterogeneous Cellular Networks

Recent cellular systems are moving towards heterogeneous cellular networks (HCNs) that consist of a mixture of miniature cells and legacy macro-cells to meet the requirements of wireless data traffic, owing to the immense amount of multi-purpose mobile applications. The inclusion of small cells is a cost-effective solution for enhancing the size and coverage of the existing macro-cellular network. This article assumes a heterogeneous cellular network consisting of two tiers of base stations (BSs): large-scale (macro) and small-scale (pico) BSs. The users are evenly distributed, and each tier of BSs and users create a uniform Poisson point process (PPP). Practical third-generation partnership project (3GPP) models for path loss are considered, and three camping/association criteria are utilized to relate user equipment (UEs) to large or small-scale BSs, including coupled and decoupled camping criteria to study coverage. The impact of several system design parameters on coverage is investigated using the aforementioned heterogeneous cellular network, association criteria, and 3GPP path loss models. Our simulation results provide insights into the effect of infrastructure sharing between macro and pico-cells and user density on coverage. We also explore the impact of fractional power control (FPC) and signaling limits on coverage under all considered association strategies. Finally, we investigate the effect of open-loop UE transmission power, pico-density, and biasing on coverage. Specifically, we thoroughly explore the effect of empty BSs on coverage under all system design parameters.

Trajectory Optimization and Power Allocation for Cell-Free Satellite-UAV Internet of Things

Massive access outside the coverage of terrestrial cellular networks will be the main feature of the sixth generation (6G) networks. In order to cope with it, the cognitive satellite-UAV network (CSUN) has drawn a lot of attentions. In this paper, we investigate the UAV trajectory optimization and power allocation for the cell-free CSUN consisting of one satellite and a swarm of UAVs. Indeed, due to the on-board energy constraints of UAVs, both the trajectory optimization and the power allocation can significantly save the energy to improve the energy efficiency. The joint trajectory optimization and power allocation problem is formulated as a mixed-integer non-convex optimization problem which is extremely difficult to solve. In order to reduce the computational complexity, we decompose the original optimization problem into two subproblems in terms of the trajectory optimization and power allocation. For the trajectory optimization subproblem, we model it as a Traveling Salesman Problem (TSP), and the PSO is adopted to solve it. When the trajectory variables are fixed, the power allocation subproblem is still difficult to tackle due to its non-convexity and large scale. Firstly, we present a kind of centralized algorithm in which the DC (difference of two convex functions) algorithm is applied to optimize it, and then a distributed algorithm based on auxiliary variables is proposed to reduce the signaling overhead and computational complexity. The simulation results demonstrate the effectiveness of the proposed joint trajectory optimization and power allocation algorithm for the cell-free CSUN.

Double Deep Q-Network Method for Energy Efficiency and Throughput in a UAV-Assisted Terrestrial Network

Increasing the coverage and capacity of cellular networks by deploying additional base stations is one of the fundamental objectives of fifth-generation (5G) networks. However, it leads to performance degradation and huge spectral consumption due to the massive densification of connected devices and simultaneous access demand. To meet these access conditions and improve Quality of Service, resource allocation (RA) should be carefully optimized. Traditionally, RA problems are nonconvex optimizations, which are performed using heuristic methods, such as genetic algorithm, particle swarm optimization, and simulated annealing. However, the application of these approaches remains computationally expensive and unattractive for dense cellular networks. Therefore, artificial intelligence algorithms are used to improve traditional RA mechanisms. Deep learning is a promising tool for addressing resource management problems in wireless communication. In this study, we investigate a double deep Q-network-based RA framework that maximizes energy efficiency (EE) and total network throughput in unmanned aerial vehicle (UAV)-assisted terrestrial networks. Specifically, the system is studied under the constraints of interference. However, the optimization problem is formulated as a mixed integer nonlinear program. Within this framework, we evaluated the effect of height and the number of UAVs on EE and throughput. Then, in accordance with the experimental results, we compare the proposed algorithm with several artificial intelligence methods. Simulation results indicate that the proposed approach can increase EE with a considerable throughput.

Approaches and mechanisms for increasing capacity and internal building radio coverage of mobile cellular networks

The aim of the report is to study different approaches and mechanisms for increasing the capacity and internal building radio coverage of mobile cellular networks. Various methods for indoor radio coverage are presented by comparing and analyzing the proposed solutions. On the occasion of overcoming the limitation of external microcells, other differentiated approaches are proposed for direct increase of the internal building signal. The approach consists of implementing antennas and equipment directly inside the buildings. These indoor antennas and radio engineering is defined as a distributed antenna system. With the development of mobile cellular networks of the fourth 4G and the introduction of the fifth 5G generation increasingly began to rely on a new optimal and effective approach to improving internal radio coverage and capacity in buildings and premises of end users presented as home base stations or femtocells.

Deep Learning-Based Coverage and Rate Manifold Estimation in Cellular Networks

This article proposes Convolutional Neural Network based Auto Encoder (CNN-AE) to predict location dependent rate and coverage probability of a network from its topology. We train the CNN utilising BS location data of India, Brazil, Germany and the USA and compare its performance with stochastic geometry (SG) based analytical models. In comparison to the best-fitted SG-based model, CNN-AE improves the coverage and rate prediction errors by a margin of as large as 40% and 25% respectively. As an application, we propose a low complexity, provably convergent algorithm that, using trained CNN-AE, can compute locations of new BSs that need to be deployed in a network in order to satisfy pre-defined spatially heterogeneous performance goals.

H-HOME: A learning framework of federated FANETs to provide edge computing to future delay-constrained IoT systems

In 6G systems, it will be mandatory that the network is able to support edge computing powered by Artificial Intelligence (AI) to provide mobile devices with the opportunity of job offloading for computation, so implementing the new paradigm of Intelligent Internet of Intelligent Things (IIoIT). In areas that are very difficult to be covered by the structured networks, Flying Ad-Hoc Networks (FANET) can be considered one of the most promising technologies to enhance coverage, capacity, reliability, and energy efficiency of wireless cellular networks, also providing edge-computing services. The goal of this paper is to propose a two-layer Hierarchical Horizontal-Offload ManagEment (H-HOME) framework for horizontal offload among the Unmanned Aerial Vehicles (UAV) of the same FANET, in order to minimize processing delay and jitter. The framework exploits Federated Reinforcement Learning in order to take advantage of knowledge sharing without incurring into problems of privacy and network overloading. A Markov Decision Process (MDP) is also defined to optimize decisions of the FANET Orchestrator (FO). Simulation results, obtained from two different analyses, demonstrate that H-HOME outperforms the traditional local training approach, based on simple recursive learning, and it can be effectively used to reduce power consumption and increase FANETs flight autonomy.

IRS-Based UAV-Assisted Low-Altitude Passive Relaying: SER Performance Analysis of Optimal Deployment

This paper presents a method for analyzing the symbol error rate (SER) of a UAV cellular data network based on an IRS. This method can change the distances between the BS, IRS, UAV, and the elevation of the UAV to observe their impacts on the SER; then, a two-dimensional curve diagram of the parameters and the SER is drawn to determine the deployment position of the UAV. Finally, by flexibly changing the maximum angle of the UAV’s transmitted signal and the height of the UAV, the coverage area of the UAV’s cellular data network can be obtained through three-dimensional imaging, function fitting, and the extreme value of the condition function. As a relay of a cellular data network, a UAV can be flexibly deployed at any effective position at a low altitude to compensate for the limited coverage of the base station and the poor quality of the user’s received signal. In particular, an IRS deployed on the surface of a building can generate an equivalent line-of-sight channel to reflect the signal from the base station to the UAV. Using an IRS to change the signal phase and amplitude with the flexible deployment of UAVs can give priority to solving the problems of cellular data network coverage and signal quality in urban scenarios.

Scheduling Out-of-Coverage Vehicular Communications Using Reinforcement Learning

Performance of vehicle-to-vehicle (V2V) communications depends highly on the employed scheduling approach. While centralized network schedulers offer high V2V communication reliability, their operation is conventionally restricted to areas with full cellular network coverage. In contrast, in out-of-cellular-coverage areas, comparatively inefficient distributed radio resource management is used. To exploit the benefits of the centralized approach for enhancing the reliability of V2V communications on roads lacking cellular coverage, we propose VRLS (Vehicular Reinforcement Learning Scheduler), a centralized scheduler that proactively assigns resources for out-of-coverage V2V communications \textit{before} vehicles leave the cellular network coverage. By training in simulated vehicular environments, VRLS can learn a scheduling policy that is robust and adaptable to environmental changes, thus eliminating the need for targeted (re-)training in complex real-life environments. We evaluate the performance of VRLS under varying mobility, network load, wireless channel, and resource configurations. VRLS outperforms the state-of-the-art distributed scheduling algorithm in zones without cellular network coverage by reducing the packet error rate by half in highly loaded conditions and achieving near-maximum reliability in low-load scenarios.

Routing and Resource Allocation for IAB Multi-Hop Network in 5G Advanced

Integrated access and backhaul (IAB) is a novel feature for extending the network coverage in 5G cellular networks, based on sharing/efficient allocation of owner’s spectrum traditionally reserved for access. However, since ultra-reliability and low latency (URLLC) requirements are a key component of 5G advanced services, provisioning such services present stringent challenges for IAB multi-hop network design. To fulfill the URLLC requirements in the IAB network, we propose a cross-layer design on routing and resource allocation under the current 3rd Generation Partnership Project (3GPP) 5G standards. We first formulate a routing problem for the IAB multi-hop network, which minimizes the latency while satisfying the reliability requirement. Subsequently, we present a reinforcement learning (RL) framework to solve the resource allocation and routing problem based on the local information of each agent (IAB node) in the environment. Afterward, we propose a novel entropy-based RL algorithm with federated learning (FL) mechanism to improve the overall performance as well as accelerate the convergence speed. Via the simulation, the proposed algorithm outperforms baseline algorithms from the latency and reliability perspective, respectively. Meanwhile, the convergence speed with the proposed algorithm also improves by using FL.

Coverage and Rate in MIMO Cellular Networks With Location-Aware Transmission Rank Selection

In this letter, we study the performance of a multiple-input multiple-output (MIMO) cellular network with location-aware transmission rank selection, where the number of transmit streams for every base-station (BS) and user equipment (UE) link is determined based on the average received power at the UE (hence, the link distance). We assume open-loop zero-forcing beam-forming (OL-ZFBF) at the receiver. We first develop a stochastic geometry based analytical framework for this setup by including the adaptive rank as a mark of each BS. Using this framework, we derive the coverage probability and spectral efficiency (SE) as a function of the rank selection threshold. Our analysis demonstrates that selecting transmission rank for each link on the basis of distance improves SE. We also investigate the tradeoff between coverage probability and SE.

Cellular Network Capacity and Coverage Enhancement with MDT Data and Deep Reinforcement Learning

Recent years witnessed a remarkable increase in the availability of data and computing resources in communication networks. This contributed to the rise of data-driven over model-driven algorithms for network automation. This paper investigates a Minimization of Drive Tests (MDT)-driven Deep Reinforcement Learning (DRL) algorithm to optimize coverage and capacity by tuning antennas tilts on a cluster of cells from TIM’s cellular network. We jointly utilize MDT data, electromagnetic simulations, and network Key Performance indicators (KPIs) to define a simulated network environment for the training of a Deep Q-Network (DQN) agent. Some tweaks have been introduced to the classical DQN formulation to improve the agent’s sample efficiency, stability and performance. In particular, a custom exploration policy is designed to introduce soft constraints at training time. Results show that the proposed algorithm outperforms baseline approaches like DQN and best-first search in terms of long-term reward and sample efficiency. Our results indicate that MDT-driven approaches constitute a valuable tool for autonomous coverage and capacity optimization of mobile radio networks.