Coverage Performance Research Articles

Communication-enabled deep reinforcement learning to optimise energy-efficiency in UAV-assisted networks

Unmanned aerial vehicles (UAVs) are increasingly deployed to provide wireless connectivity to static and mobile ground users in situations of increased network demand or points of failure in existing terrestrial cellular infrastructure. However, UAVs are energy-constrained and experience the challenge of interference from nearby UAV cells sharing the same frequency spectrum, thereby impacting the system's energy efficiency (EE). Recent approaches focus on optimising the system's EE by optimising the trajectory of UAVs serving only static ground users and neglecting mobile users. Several others neglect the impact of interference from nearby UAV cells, assuming an interference-free network environment. Furthermore, some works assume global spatial knowledge of ground users' location via a central controller (CC) that periodically scans the network perimeter and provides real-time updates to the UAVs for decision-making. However, this assumption may be unsuitable in disaster scenarios since it requires significant information exchange between the UAVs and CC. Moreover, it may not be possible to track users' locations in a disaster scenario. Despite growing research interest in decentralised control over centralised UAVs' control, direct collaboration among UAVs to improve coordination while optimising the systems' EE has not been adequately explored. To address this, we propose a direct collaborative communication-enabled multi-agent decentralised double deep Q-network (CMAD–DDQN) approach. The CMAD–DDQN is a collaborative algorithm that allows UAVs to explicitly share their telemetry via existing 3GPP guidelines by communicating with their nearest neighbours. This allows the agent-controlled UAVs to optimise their 3D flight trajectories by filling up knowledge gaps and converging to optimal policies. We account for the mobility of ground users, the UAVs' limited energy budget and interference in the environment. Our approach can maximise the system's EE without hampering performance gains in the network. Simulation results show that the proposed approach outperforms existing baselines in terms of maximising the systems' EE without degrading coverage performance in the network. The CMAD–DDQN approach outperforms the MAD-DDQN that neglects direct collaboration among UAVs, the multi-agent deep deterministic policy gradient (MADDPG) and random policy approaches that consider a 2D UAV deployment design while neglecting interference from nearby UAV cells by about 15%, 65% and 85%, respectively.

Coexisting Terahertz and RF Finite Wireless Networks: Coverage and Rate Analysis

Wireless communications over Terahertz (THz)-band frequencies are vital enablers of ultra-high rate applications and services in sixth-generation (6G) networks. However, THz communications suffer from poor coverage because of inherent THz features such as high penetration losses, significant molecular absorption, and severe path loss. To surmount these critical challenges and fully exploit the THz band, we explore a coexisting radio frequency (RF) and THz finite indoor network in which THz small cells are deployed to provide high data rates, and RF macrocells are deployed to satisfy coverage requirements. Using stochastic geometry tools, we assess the performance of coexisting RF and THz networks and derive tractable analytical expressions for the coverage probability and average achievable rate. The analytical results are validated with Monte-Carlo simulations. Several insights are devised for accurate tuning and optimization of THz system parameters, including the THz bias, and the fraction of THz access points (APs) to deploy. The obtained results recognize a clear coverage/rate trade-off where a high fraction of THz AP improves the rate significantly but may degrade the coverage performance. Furthermore, the location of a user in the finite area highly affects the fraction of THz APs that optimizes its quality of service.

Traffic based power consumption and node deployment in green LTE-A cellular networks

The energy utilization of green cellular networking or cellular network infrastructure has become a popular research domain due to the economic concern of network operators and global climate change. Also, due to the growth of wireless networks, the authors are attracted to finding novel relay station (RS) deployment techniques to support high user density and high data rate services by reducing energy consumption and operating costs of different network elements. The location of RSs greatly influences how far a cell can be covered. Therefore, the deployment RS plays a major role in widening the cell's coverage radius. To solve these challenges and to realize greener network designs by maximizing power consumption with QoS assurance, a novel traffic-based power consumption minimization and node deployment for the LTE green cellular networks is proposed. Also, key performance indicators (KPIs) are required to monitor and improve network performance. KPIs control the accomplished resource utilization and the quality of services. The research work comprises RS deployment, traffic estimation, and power allocation. Power losses, communication delays, increased implementation costs, and decreased throughput are the effects of an inappropriate RS deployment. Therefore, this research introduces the transmission area-based relay stations deployment scheme (TARSD) with multiobjective functions maximization of coverage area, minimization of the overlapping area and the minimization of power consumption cost. Traffic estimation helps to identify future capacity requirements and enables improved planning and decision-making. The deployment scheme of a cellular network relies on network traffic estimation. Nash traffic entropy learning algorithm (NTEL) is used to estimate traffic, and based on the estimated eNB switching ON/OFF is performed to minimize power consumption. Inter cell interference (ICI) can impact the performance of the LTE network. Several power allocation schemes can be used to improve system performance to achieve the optimal trade-off between interference and signal-to-noise ratio (SINR). Because of its good performance and efficacy in improving the mutual information between a channel's input and output, the enhanced low complexity water-filling (ELCWF) has been widely employed in power allocation with its increment and decrement properties. This research studies the statistical behaviour of a Received Signal Strength Indicator (RSSI), Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ) and Signal to Interference noise Ratio (SINR). Also, statistical analysis of cost versus coverage performance is examined. Under the 7th simulation run, the proposed method yields the best coverage of 88.81 and 98.74%, respectively. In the experimental scenario, for the proposed model, the deployment cost for BS and RS are 27 and 13 units, the average throughput per user is 9.74 Mbps, and the coverage ratio is 97% under the BS candidate location of 20, respectively. In the power allocation stage, if the number of users is 50, the proposed model yields a power consumption of 0.2 W, throughput of 23.4 Mbps, delay of 15 ms and SINR of 20.12 dB, respectively.

Interference mitigation in intentional jammers aided non-uniform heterogeneous cellular networks.

Coverage and capacity are optimized in fifth generation (5G) networks by small base station (SBS) distribution in the coverage realm of macro base station (MBS). However, system performance is significantly reduced by inter-cell interference (ICI) because of the orthogonal frequency division multiple access assumption. In addition to ICI, this work considers intentional jammers' interference (IJI) due to the presence of jammers. These Jammers try to inject undesirable energies into the legitimate communication band, which significantly degrade uplink (UL) signal-to-interference ratio (SIR). To reduce ICI and IJI, in this work, we employ SBS muting, where the SBSs near MBS are switched off. To further mitigate ICI and IJI, we use one of the effective interference management schemes a.k.a reverse frequency allocation (RFA). We presume that due to mitigation in ICI and IJI, the UL coverage performance of the proposed network model can be further improved.

A time-varying acoustic channel-aware topology control mechanism for cooperative underwater sonar detection network

Topology control plays a vital role in cooperative underwater sonar detection networks (USDNs) to ensure detection applications. By incorporating spatio-temporal uncertainties in acoustic channel and limited resources in underwater networks, a large number of node deployment schemes have been proposed to overcome these difficulties through topology control. However, the channel uncertainty has not been solved fundamentally. In order to effectively deal with the time-space-frequency uncertainty of the acoustic channel, this paper proposes a time-varying acoustic channel-aware (TVAC) topology control mechanism to deploy sonar nodes in USDNs. Initially, a time-varying acoustic channel state information (CSI) estimation algorithm and a prediction algorithm are proposed by applying three sub-modules: a feature stitching deep neural network-based temperature prediction model that we designed, a deep neural network-based sound velocity estimation model, and a BELLHOP-based propagation loss calculation model. After that, a TVAC topology control mechanism is adopted to adaptively deploy nodes based on the CSI estimation and prediction results. Using the improved weighted set covering algorithm, the current CSI is adopted to determine the working modes of sonar nodes, and the predicted CSI is adopted to obtain the cycle of topology update. What is more, we develop an offline–online CSI prediction model to improve the accuracy and stability of node working modes management. Simulation results show that compared with other topology control schemes, TVAC can obtain superior coverage performance, full connectivity, and prolong the network lifetime with guaranteed coverage and connectivity.

Complete coverage planning with clustering method for autonomous mobile robots

SummaryComplete coverage planning (CCP) is a task to cover the entire area on the map, according to the job description of the autonomous mobile robot. The most widely used method for CCP in the literature is the grid‐based coverage method. In this method, the problem is processing the partially filled cell as completely filled, which reduces the coverage performance. The ability to use the clustering method, which will be created by considering the characteristics of the environment, was determined as a research question to solve this problem. In this direction, it is aimed to use K‐means++ algorithm, which is a widely used clustering algorithm and segmentation technique. In this context, an offline K‐means++ complete coverage planning (Km++CCP) method, in which the navigable area on the map of the indoor where a mobile robot will navigate is clustered using the K‐means++ algorithm and the centroids can be used as waypoints, is proposed. To test the proposed method, 2 simulations and 36 real‐world experiments were conducted. The indoor coverage ratio of Km++CCP was calculated higher than the grid‐based method in all experiments.

Spectrum Sharing Between High Altitude Platform Network and Terrestrial Network: Modeling and Performance Analysis

Achieving seamless global coverage is one of the ultimate goals of space-air-ground integrated network, as a part of which High Altitude Platform (HAP) network can provide wide-area coverage. However, deploying a large number of HAPs will lead to severe congestion of existing frequency bands. Spectrum sharing improves spectrum utilization. The coverage performance improvement and interference caused by spectrum sharing need to be investigated. To this end, this paper analyzes the performance of spectrum sharing between HAP network and terrestrial network. We firstly generalize the Poisson Point Process (PPP) to curves, surfaces and manifolds to model the distribution of terrestrial Base Stations (BSs) and HAPs. Then, the closed-form expressions for coverage probability of HAP network and terrestrial network are derived based on differential geometry and stochastic geometry. We verify the accuracy of closed-form expressions by Monte Carlo simulation. The results show that HAP network has less interference to terrestrial network. Low height and suitable deployment density can improve the coverage probability and transmission capacity of HAP network.

Secure Transmission Design for Aerial IRS Assisted Wireless Networks

Combining intelligent reflecting surface (IRS) and unmanned aerial vehicle (UAV) offers a new degree of freedom to improve the coverage performance. However, it is more challenging to secure the air-ground transmission, due to the line-of-sight (LoS) links established by UAV. Since IRS is a promising solution for wireless environment reconfiguration, in this paper, we propose an aerial IRS-assisted secure transmission design in wireless networks. In particular, an access point (AP) equipped with a uniform planar array serves several single-antenna legitimate users in the presence of multiple single-antenna eavesdroppers, whose precise positions are unknown. An IRS is mounted on the UAV to help establish desired virtual LoS links between the AP and legitimate users, while ensuring their security. We aim to maximize the worst-case sum secrecy rate by jointly optimizing the hovering position of UAV, the transmit beamforming of AP and the phase shifts of IRS, subject to the requirement of minimum rate for legitimate users. To tackle this non-convex problem, we first decompose it into three subproblems, which are transformed into convex ones via successive convex approximation. An alternating algorithm is then proposed to solve them iteratively. Simulation results show the effectiveness of the proposed scheme and the security improvement by the joint optimization.

Pretraining technology of adversarial generative networks based data selection

The effectiveness of adversarial generative networks (GANs) has been shown in many works. However, the convergence of the GANs should be granted mathematically with proper hyperparameters and some network structures. Considering the difficulties of constructing a large and clean dataset, GANs are required in many cases to extract enough information from a relatively small dataset. This could result in bad coverage performance of GANs, and one way to tackle this problem is pretraining. Since this method relies on importing external information from the pretraining dataset, this paper does a necessary discussion on the effect that the pretraining dataset has on the model and makes a simple assumption on the standard of choosing a pretraining dataset. Specifically, this paper focuses on the case where the target is to generate animation-style characters, and it has been proven that when providing a pretraining dataset with higher diversity, the performance of the GAN is better. The result of this paper should reveal the possibility that using a specially chosen pretraining dataset can improve the result of GAN.

DHD-MEPO: A Novel Distributed Coverage Hole Detection and Repair Method for Three-Dimensional Hybrid Wireless Sensor Networks

A coverage hole is a problem that cannot be completely avoided in three-dimensional hybrid wireless sensor networks. It can lead to hindrances in monitoring tasks and adversely affect network performance. To address the problem of coverage holes caused by the uneven initial deployment of the network and node damage during operation, we propose a distributed hole detection and multi-objective optimization emperor penguin repair algorithm (DHD-MEPO). In the detection phase, the monitoring region is zoned as units according to the quantity of nodes and the sensing range, and static nodes use the sum-of-weights method to campaign for group nodes on their terms, determining the location of holes by calculating the coverage of each cell. In the repair phase, the set of repair nodes is determined by calculating the mobile node coverage redundancy. Based on the characteristics of complex environments, the regions of high hole levels are prioritized. Moreover, the residual energy homogeneity of nodes is considered for the design of multi-objective functions. A lens-imaging mapping learning strategy is introduced to perturb the location of repair nodes for the optimization of the emperor penguin algorithm. Experimental results illustrate that the DHD-MEPO, compared with the C-CICHH, 3D-VPCA, RA, EMSCOLER, and IERP algorithms, can balance the uniformity of the residual energy of each node while satisfying the network coverage requirements and network connectivity, which effectively improves the network coverage performance.

Simultaneous Sensing of Cd(II), Pb(II), and Cu(II) Using Gold Nanoparticle-Modified APTES-Functionalized Indium Tin Oxide Electrode: Effect of APTES Concentration.

In this work, indium tin oxide (ITO) electrodes were functionalized with varying 3-aminopropyltriethoxysilane (APTES) concentration percentages (0.5, 0.75, 1.0, and 2.0 wt %) to obtain the optimum conditions for the assembly of the as-synthesized gold nanoparticles (AuNPs). The AuNP coverage, wettability, and electrochemical performance of the modified electrodes were evaluated. The AuNP/0.75% APTES-ITO-modified electrode exhibited uniform coverage of AuNPs and high electrochemical performance for the simultaneous detection of Cd(II), Pb(II), and Cu(II) ions. Under the optimum conditions, the AuNP/0.75% APTES-ITO-modified electrode showed a linear detection range of 5-120 ppb and limit of detection of 0.73, 0.90, and 0.49 ppb for the simultaneous detection of Cd(II), Pb(II), and Cu(II) ions, respectively, via square wave anodic stripping voltammetry. The modified electrode demonstrated good anti-interference toward other heavy metal ions, good reproducibility, and suitability for application in environmental sample analysis.

Lower Inclination Orbit Concept for Direct-Communication-To-Satellite Internet-Of-Things Using Lean Satellite Standard in Near-Equatorial Regions

This research proposes a low-inclined orbit concept and design for the Internet-of-Things (IoT) using lean satellite standards in near-equatorial regions. The study aims to evaluate the coverage of various inclination angles at various latitudes and inclination angles in order to determine the most suitable satellite design for providing IoT coverage in these regions. The main methods applied in the study included analyzing the coverage performance of different inclination angles, the link budget analysis using simulations and the definition of the mission criteria. The results of the study show that the overall coverage performance decreases with an increase in the inclination angle. Satellites with lower inclination angles have ground tracks that are more closely aligned with the equator, while satellites with higher inclination angles have ground tracks that are inclined further toward the poles. In addition, the results show that the fraction of orbits with coverage (expressed as a percentage) declines with increasing latitude. Based on these findings, a low-inclined orbit of 24° provides the best coverage for IoT in near-equatorial regions within ±20 and 26° latitude, with a peak coverage of 27% at 24° latitude and a minimum coverage of 10% in the region spanning from 0° to ±27° latitude. This design offers more coverage time and a shorter revisit time to the selected regions for communication missions.

Telecommunications regulation, mobile money innovations and financial inclusion

PurposeThis study aims to assess how corporate telecommunication (telecom) policies follow telecom sector regulation in mobile money innovation for financial inclusion in developing countries.Design/methodology/approachTelecom policies are understood in terms of mobile subscriptions, mobile connectivity coverage and mobile connectivity performance, whereas mobile money innovations represent mobile money accounts, the mobile used to send money and the mobile used to receive money. The empirical evidence is based on Tobit regressions.FindingsTelecom sector regulation positively influences mobile money innovations. From net influences, mobile subscriptions and connectivity policies moderate telecom sector regulation to positively influence mobile money innovations, exclusively within the remit of mobile money accounts because the corresponding net influences on the mobile used to send money and the mobile used to receive money are negative. The interactive influences are consistently negative, and hence, thresholds for complementary policies are provided to maintain the positive influence of telecom sector regulation on mobile money innovations.Originality/valueThis study has complemented the extant literature by assessing how corporate telecommunication policies follow telecommunication sector regulation in mobile money innovations for financial inclusion.

An improved differential evolution algorithm for quantifying fraudulent transactions

Identification of fraudulent credit card transactions is a complex problem mainly due to the following factors: 1) The relative behavior of customers and fraudsters may alter over time. 2) The ratio of legitimate to fraudulent transactions is highly imbalanced, and 3) Investigators examine a small segment of transactions in a reasonable time frame. Researchers have proposed various algorithms to identify potential fraud in a new incoming transaction. However, these approaches require significant human investigator effort and are sometimes misleading. To address this issue, this paper proposes an improved multiobjective differential evolution (DE) algorithm to estimate the distribution of fraudulent transactions in a set of new incoming transactions, referred to as quantifying fraudulent transactions. Our paper has three major novelties. First, we present the problem formulation of cost-based feature selection with maximum quantification ability. Second, we improve the DE by applying effective trial vector generation algorithms to the random control parameter settings to exploit the advantage of individual DE variants. Third, we develop the maximum-relevancy-minimum-redundancy-based Pareto refining operator to enhance the self-learning ability of individuals in Pareto solutions. We compare our approach against four other modifications of DE and five state-of-the-art evolutionary algorithms on real-time credit datasets in streaming and non-streaming frameworks using hyper-volume, two-set coverage, and spread performance metrics.

Examining the Impact of Key Factors on COVID-19 Vaccination Coverage in India: A PLS-SEM Approach.

During the coronavirus disease 2019 (COVID-19) pandemic, numerous factors determined the performance of COVID-19 vaccination coverage. The purpose of this study is to examine the influence of factors such as government stewardship, planning and implementation, and community participation on COVID-19 vaccination coverage. This study applied partial least square structured equation modeling (PLS-SEM) by analyzing 187 responses from the stakeholders involved in vaccination programs in four select states of India. This study empirically validates a framework for improving vaccination coverage by confirming the significant impact of planning and implementation on vaccination coverage followed by government stewardship and community participation. Additionally, this study highlights the individual impact of each factor on vaccination coverage. Based on the findings, strategic recommendations were proposed that can be utilized for formulating policy-level actions to facilitate the vaccination program.

Near-Field Beam Training in Terahertz Communications with Hybrid Beamforming Architecture

Terahertz (THz) communication has a large available bandwidth, which is expected to be deployed in future communication networks. As THz wave suffers from severe propagation loss in wireless transmission, we consider a THz near-field scenario where a base station (BS) is equipped with a large-scale antenna array with a low-cost hybrid beamforming architecture to serve mobile users nearby. However, the large-scale array and the user mobility incur difficulty in channel estimation. To tackle this issue, we propose a near-field beam training scheme that can align a beam to the user in a fast way by searching the codebook. Specifically, the BS employs a uniform circular array (UCA), and the radiation pattern of the beams in our proposed codebook appears as ellipsoids. To cover the serving zone with the minimum codebook size, we develop a near-field codebook by tangent arrangement approach (TAA). To reduce the time overhead, we leverage the hybrid beamforming architecture to realize multi-beam training concurrently since each RF chain can enable a codeword whose element has a constant magnitude. Numerical results validate that our proposed UCA near-field codebook achieves less time cost while achieving a comparable coverage performance compared to the conventional near-field codebook.

Evaluating the Transition from Targeted to Exome Sequencing: A Guide for Clinical Laboratories.

The transition from targeted to exome or genome sequencing in clinical contexts requires quality standards, such as targeted sequencing, in order to be fully adopted. However, no clear recommendations or methodology have emerged for evaluating this technological evolution. We developed a structured method based on four run-specific sequencing metrics and seven sample-specific sequencing metrics for evaluating the performance of exome sequencing strategies to replace targeted strategies. The indicators include quality metrics and coverage performance on gene panels and OMIM morbid genes. We applied this general strategy to three different exome kits and compared them with a myopathy-targeted sequencing method. After having achieved 80 million reads, all-tested exome kits generated data suitable for clinical diagnosis. However, significant differences in the coverage and PCR duplicates were observed between the kits. These are two main criteria to consider for the initial implementation with high-quality assurance. This study aims to assist molecular diagnostic laboratories in adopting and evaluating exome sequencing kits in a diagnostic context compared to the strategy used previously. A similar strategy could be used to implement whole-genome sequencing for diagnostic purposes.

Simultaneous tolerance intervals for linear regression models using an adjusted product set method

Pointwise tolerance intervals for linear regression models are well established in the literature, however, there is no gold standard for a procedure to construct simultaneous tolerance intervals for linear regression models. The existing methods vary considerably with respect to their finite sample performance in terms of coverage probabilities. Moreover, the numerical investigations into their coverage performance have only been conducted under very limited settings. In this work, we investigate the coverage performance for the major procedures available for the construction of simultaneous tolerance intervals for linear regression models, with increasing complexity in the structure of the models considered. We further show that an adjustment made to the confidence level for a product-set-based method provides coverages close to nominal for the majority of models considered, and this adjusted method is nearly always better than each of the other existing methods. Simultaneous tolerance intervals are then constructed and interpreted for a chemical oxygen demand regression model and two models for the strength of polymer-modified cement mortar.

Electrochemical performance study of proton exchange membrane electrolyzer considering the effect of bubble coverage

Bubble dynamics are closely related to the electrochemical performance of a proton exchange membrane electrolyzer (PEMEC). However, tiny bubbles need to be clustered together to affect the electrochemical performance of PEMEC significantly. In this paper, the effect of microscopic bubbles on macroscopic electrochemical properties were assessed by bubble coverage. The bubble dynamics, two-phase flow, and electrochemical performance were captured under different conditions using a high-speed, microscopic visualization experimental system. The results show that various factors influence the two-phase flow pattern. At 60 °C, 1.5 A/cm2 and 5 mL/min, the annular flow occupied 76.8% of the gas phase area, and when the water flow increased to 80 mL/min, the annular flow ratio decreased substantially to 2.7%. The two-phase flow of bubbles in the flow channel showed different flow patterns over time. Under the experimental conditions (60 °C, 20 mL/min, 0.8 A/cm2), the bubble flow pattern experienced the emergence of bubbles, bubble flow, segmental plug flow, annular flow, and final steady state with the occurrence times of 0.15 s, 1.5 s, 5.0 s, 10.5 s, and 21.2 s, respectively. The bubble coverage increased with current density and temperature, while it decreased with the increase of water velocity. In addition, the effects of temperature and water velocity on bubble coverage and PEMEC performance vary in principle. Specifically, higher temperature mainly improves the bubble coverage by increasing the electrochemical performance of PEMEC. In contrast, higher water velocity mainly improves the electrochemical performance of PEMEC by decreasing the bubble coverage. This study elucidates the relationship between microscopic bubbles and macroscopic electrochemical performance, contributing to a better understanding of the processes and principles of bubble effects on the electrochemical performance of PEMEC. The results may provide a theoretical basis and experimental data for operating condition optimization, operating efficiency improvement, multiphase flow study, gas diffusion layer structure, and flow field design of PEMEC.

Exploring the Pattern of Immunization Dropout among Children in India: A District-Level Comparative Analysis.

The dropout rate is one of the determinants of immunization coverage and program performance, program continuity, and follow-up. The dropout rate refers to the proportion of vaccine recipients who did not finish their vaccination schedules, and it is determined by comparing the number of infants who started the schedule to the number who completed it. It is the rate difference between the first and final dosage or the rate difference between the first vaccination and the last vaccine dropout; thus, it denotes that the first recommended dose of vaccine was received, but that the subsequently recommended dose was missed. In India, immunization coverage has shown significant improvements over the last two decades, but full immunization coverage has remained stagnant at 76.5%, of which 19.9% are partially immunized, and 3.6% are children who have been left out. In India, the Universal Immunization Programme (UIP) is challenged with cases related to dropout in immunization. Although immunization coverage in India is improving, the program is challenged by vaccination dropouts. This study provides an analysis of the determinants of vaccination dropout in India using data from two rounds of the National Family Health Survey. The finding shows that the mother's age, education, family wealth, antenatal care visit, and place of delivery were some of the variables that significantly contributed to reducing the dropout rate of immunization among children. The findings of this paper show that the dropout rate has reduced over a certain period of time. The overall improvement in the rates of dropout and increase in full immunization coverage could be attributed to various policy measures taken in the last decade in India, which brought structural changes with a positive impact on full immunization coverage and its components.