Search Results (12)

Advanced Metering Infrastructure (AMI) systems require bidirectional wireless communication for remote meter reading, disconnection, and demand–response management across large numbers of smart meters. LoRaWAN Class-C is a strong candidate for large-scale AMI due to its long range, low infrastructure cost, and native downlink support, but gateway placement must simultaneously satisfy uplink Pure-ALOHA capacity, downlink duty-cycle limits, and geographic coverage constraints in non-uniform device distributions. This paper proposes a greedy capacity-coverage gateway placement algorithm for a LoRaWAN AMI deployment serving 89,350 smart meters in Lam Luk Ka district, Pathum Thani Province, Thailand, based on real GIS building-footprint data. The algorithm seeds each gateway at the densest unserved 300 m grid cell, assigns devices within the planning radius R up to the tighter of the uplink Pure-ALOHA capacity and the downlink duty-cycle capacity, both computed for a 98% packet delivery target, and continues placing gateways until 98% of devices are geographically covered. For the 15 min reporting interval, the algorithm yields K* = 137 gateways, only 7% above the arithmetic lower bound of 128, and requires 2.9× fewer gateways than iterative K-means under the same placement constraints. An interval-based scenario analysis across five reporting periods of 5, 10, 15, 30, and 60 min reveals that 15 min is the crossover design point where longer intervals are limited by the downlink duty-cycle and shorter intervals are limited by uplink Pure-ALOHA collision, making 15 min the point at which both constraints are simultaneously near-binding. RX2 reconfiguration from SF10 to SF7 is shown to be essential: the default configuration yields only 121 devices/gateway at the 15 min rate, requiring 739 gateways, while SF7 reconfiguration raises capacity 5.8x to 702 devices/gateway. A Monte Carlo simulation with Urban Okumura–Hata path loss and log-normal shadowing validates the placement, achieving a mean uplink PDR of 98.0% and downlink PDR of 99.9%, both meeting the 98% design target. Class-C energy consumption is 112,787 mJ per 900 s reporting cycle, 434x that of Class-A (260 mJ), confirming that Class-C bidirectional AMI is feasible only for mains-powered smart meters. Full article

This paper investigates the quality of mobile network coverage along the Riga–Tukums railway corridor with a focus on the performance of 4G and 5G technologies. Ensuring reliable mobile connectivity along suburban railway corridors remains a significant technical challenge due to mixed forest–urban propagation conditions, macro-cell-dominated LTE infrastructure, mobility-induced channel variability, and fluctuating passenger density. Unlike high-speed railway environments that are extensively studied in dedicated 5G-R scenarios, suburban railway systems often rely on existing macro-cell deployments, where coverage continuity, signal quality stability, and capacity constraints must be addressed simultaneously. This study presents a measurement-based evaluation of 4G and 5G radio performance along the Riga–Tukums railway corridor under real operational conditions (50–90 km/h). Classical propagation models (Okumura–Hata and COST231-Hata) are quantitatively validated using MAE and RMSE metrics, followed by correlation analysis between RSSNR and QoS indicators. A theoretical Doppler sensitivity assessment (80–200 km/h) is conducted to evaluate mobility robustness across LTE and 5G frequency bands. Mobility transition regions and handover-related time windows are geometrically estimated, and passenger density-based capacity modeling is applied to assess throughput degradation under peak occupancy scenarios. Based on these results, a multi-layer network planning strategy integrating 700 MHz macro coverage, 1700 MHz capacity enhancement, and 3500 MHz 5G NR deployment is proposed. The optimization strategy resulted in an estimated 22–28% increase in stable service coverage in previously weak-signal zones and demonstrated that propagation model deviations remain within ranges comparable to recent railway studies (≈15–25 dB RMSE). These findings provide a structured framework for suburban railway communication optimization and support the gradual modernization of railway infrastructure toward FRMCS-ready architectures. The study illustrates the applicability of modern modelling tools for assessing and improving mobile communication systems and contributes to the broader development of digital infrastructure within Latvia’s transport sector. Full article

The global shift to digital terrestrial television broadcasting (DTTB) from the conventional analogue has significantly transformed television culture, necessitating comprehensive technical and infrastructural evaluations. This study addresses the limitations of existing path-loss models for accurately predicting path loss in digital terrestrial television broadcasting in the UHF bands, motivated by the need for reliable, location-specific models that account for seasonal, meteorological, and topographical variations in Abeokuta, Nigeria. The study focuses on path-loss prediction in the UHF band using Ogun State Television (OGTV), Abeokuta, Nigeria, as the transmission source. Eight receiving sites, spaced 2 kilometers apart, were selected along a 16.7 km transmission contour. Daily measurements of received signal strength (RSS) and weather conditions were collected over one year. Seasonal path-loss models ${PL}_{wet}$ for the wet season and ${PL}_{dry}$. For the dry season, models were developed using multiple regression analysis and further optimized using least squares (LS) and gradient descent (GD) techniques, resulting in six refined models: ${PL}_{wet}$, ${PL}_{dry}$, ${PL}_{wet-LS}$, ${PL}_{dry-LS}$, ${PL}_{wet-GD}$, and ${PL}_{dry-GD}$. Model performance was evaluated using Mean Absolute Error, Root Mean Square Error, Coefficient of Correlation, and Coefficient of Multiple Determination. Results indicate that the Okumura model provided the closest approximation to measured RSS for all the receiving sites, while the Hata and COST-231 models were unsuitable. Among the developed models, ${PL}_{wet}$ ($RMSE- 1.2633$, $MAE - 0.9968$, $MSE - 1.5959$, $R - 0.9935$, $R^2 - 0.9871$) and ${PL}_{dry-LS}$($RMSE- 1.1884$, $MAE - 0.7692$, $MSE - 1.4124$, $R - 0.9942$, $R^2 - 0.9883$) were found to be the most suitable models for the wet and dry seasons, respectively. The major influence of location-based elevation and meteorological data on path-loss prediction over digital terrestrial television broadcasting communication lines in Ultra-High-Frequency bands was evident. Full article

This paper introduces a theoretical large-scale radio channel model for the downlink in cellular systems, aimed at estimating variations in received signal power at the user terminal as a function of device mobility. This enables applications such as direction-of-arrival (DoA) estimation, estimating power at subsequent points based on received power, and detection of coverage anomalies. The model is validated using real-world measurements from urban and suburban environments, achieving a maximum estimation error of 7.6%. In contrast to conventional models like Okumura–Hata, COST-231, Third Generation Partnership Project (3GPP) stochastic models, or ray-tracing techniques, which estimate average power under static conditions, the proposed model captures power fluctuations induced by terminal movement, a factor often neglected. Although advanced techniques such as wave-domain processing with intelligent metasurfaces can also estimate DoA, this model provides a simpler, geometry-driven approach based on empirical traces. While it does not incorporate infrastructure-specific characteristics or inter-cell interference, it remains a practical solution for scenarios with limited information or computational resources. Full article

The widespread adoption of the Internet of Things (IoT) has driven major advancements in wireless communication, especially in rural and suburban areas where low population density and limited infrastructure pose significant challenges. Accurate Path Loss (PL) prediction is critical for the effective deployment and operation of Wireless Sensor Networks (WSNs) in such environments. This study explores the use of Convolutional Neural Networks (CNNs) for PL modeling, utilizing a comprehensive dataset collected in a smart campus setting that captures the influence of terrain and environmental variations. Several CNN architectures were evaluated based on different combinations of input features—such as distance, elevation, clutter height, and altitude—to assess their predictive accuracy. The findings reveal that CNN-based models outperform traditional propagation models (Free Space Path Loss (FSPL), Okumura–Hata, COST 231, Log-Distance), achieving lower error rates and more precise PL estimations. The best performing CNN configuration, using only distance and elevation, highlights the value of terrain-aware modeling. These results underscore the potential of deep learning techniques to enhance IoT connectivity in sparsely connected regions and support the development of more resilient communication infrastructures. Full article

Fifth-generation (5G) networks are the fifth generation of mobile networks and are regarded as a global standard, following 1G, 2G, 3G, and 4G networks. Fifth-generation, with its large available bandwidth provided by mmWave, not only provides the end user with higher spectrum efficiency, massive capacity, low latency, and high speed but is also a network designed to connect virtually everyone and everything together, including machines, objects, and devices. Therefore, studies of such systems’ performance evaluation and capacity bounds are critical for the research community. Furthermore, the performance of these systems should be investigated in realistic contexts while considering signal strength and restricted uplink power to maintain system coverage and capacity, which are also affected by the environment and the value of the service factor parameter. However, any proposed application should include a multiservice case to reflect the true state of 5G systems. As an extension of previous work, the capacity bounds for 5G networks are derived and analyzed in this research, considering both single and multiservice cases with mobility. In addition, the influence of different parameters on network performance, such as the interference, service factor, and non-orthogonality factors, and cell radii, is also discussed. The numerical findings and analysis reveal that the type of environment and service factor parameters have the greatest influence on system capacity and coverage. Subsequently, it is shown that the investigated parameters have a major impact on cell performance and therefore can be considered key indicators for mobile designers and operators to consider in planning and designing future networks. To validate these findings, some results are evaluated against ITU-T standards, while others are compared with related studies from the literature. Full article

It is well known that existing empirical models cannot fit perfectly into environments other than those they were formulated in due to differences in terrain and climate. The Okumura–Hata family of models are gaining acceptability over the VHF/UHF channels. However, it is imperative to investigate their reliability and to use the one most suited to each environment. This study investigated the reliability of the Okumura–Hata, COST-231, ECC-33, and Ericsson models over digital UHF channels in Ikorodu and Akure, Southwestern Nigeria. The drive test protocol was used for data collection at intervals of 1 km along different routes from the experimental stations up to maximums of 10 and 16 km in Ikorodu and Akure, respectively. This was carried out for both wet and dry season months using a digital Satlink meter with a spectrum (WS-6936), GPS Map 78s and a field vehicle. The uniqueness of this study is that it used real-world data with a seasonal scope, and the mean values were employed in the analysis to strengthen the reliability of the results. The measured path loss (MPL) and predicted path loss (PPLM) were computed, with error margin analysis carried out between them. The results reveal a mean MPL of 110.42 dB in Ikorodu, while the PPLMs were 121.90, 123.55, 158.42, and 291.01 dB for the Hata, COST-231, Ericsson, and ECC-33 models, respectively. In Akure, the mean MPL was 123.157 dB, while the PPLMs were 121.922, 130.179, 198.979, and 313.494 dB. The results further indicate that the Hata model had the best performance with the lowest RMSE of 10.812 in Ikorodu, while COST-231 had the best performance in Akure, with the lowest RMSE of 9.877. The optimized Hata and COST-231 models were developed with improved RMSEs of 5.895 and 7.815 for the Ikorodu and Akure environments, respectively. The optimized models had higher degrees of reliability and will provide a valuable approach to wireless communication planning in tropical urban and suburban environments for achieving quality of transmission and reception (QoTnR) over UHF channels in Nigeria and similar environments in Africa. Full article

5G is the fifth-generation technology standard for the new generation of cellular networks. Combining 5G and millimeter waves (mmWave) gives tremendous capacity and even lower latency, allowing you to fully enjoy the 5G experience. 5G is the successor to the fourth generation (4G) which provides high-speed networks to support traffic capacity, higher throughput, and network efficiency as well as supporting massive applications, especially internet-of-things (IoT) and machine-to-machine areas. Therefore, performance evaluation and analysis of such systems is a critical research task that needs to be conducted by researchers. In this paper, a new model structure of an urban-suburban environment in a 5G network formed of seven cells with a central urban cell (Hot spot) surrounded by six suburban cells is introduced. With the proposed model, the end-user can have continuous connectivity under different propagation environments. Based on the suggested model, the related capacity bounds are derived and the performance of 5G network is studied via a simulation considering different parameters that affect the performance such as the non-orthogonality factor, the load concentration in both urban and suburban areas, the height of the mobile, the height of the base station, the radius, and the distance between base stations. Blocking probability and bandwidth utilization are the main two performance measures that are studied, however, the effect of the above parameters on the system capacity is also introduced. The provided numerical results that are based on a network-level call admission control algorithm reveal the fact that the investigated parameters have a major influence on the network performance. Therefore, the outcome of this research can be a very useful tool to be considered by mobile operators in the network planning of 5G. Full article

Vehicular ad hoc networks (VANETs) have the potential to reduce car accidents by facilitating connectivity and warning message exchange between vehicles, both on roads and in parking lots. This research endeavored to accomplish three primary goals: conducting a field measurement in the parking lot of a large shopping mall in Auckland, developing an OPNET-based simulation model to analyze and validate the system performance, and analyzing the compatibility between five selected radio propagation models (Free-space, Shadowing Path-loss, Egli, Hata, and COST231). These models were selected based on their popularity and relevance to our study. We found that the “Free Space” model outperforms in the scenario in which measurements were conducted from the Level-1 car park to the Roadside. The received signal strengths in the parking lot ranged from −45 dBm to −92 dBm. This research also examines the coverage distance for the successful transmission of warning messages, achieving up to 57 m, 17.5 m, 9.4 m, and 68 m at parking levels 1, 2, 3, and the roadside, respectively. Research findings reveal that a low-cost Wi-Fi-based VANET system can be utilized to prevent car accidents in parking lots. Finally, we provide guidelines for network planners to deploy Wi-Fi-based VANET systems in parking lots. Full article

Recently, IQRF has emerged as a promising technology for the Internet of Things (IoT), owing to its ability to support short- and medium-range low-power communications. However, real world deployment of IQRF-based wireless sensor networks (WSNs) requires accurate path loss modelling to estimate network coverage and other performances. In the existing literature, extensive research on propagation modelling for IQRF network deployment in urban environments has not been provided yet. Therefore, this study proposes an empirical path loss model for the deployment of IQRF networks in a peer-to-peer configured system where the IQRF sensor nodes operate in the 868 MHz band. For this purpose, extensive measurement campaigns are conducted outdoor in an urban environment for Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) links. Furthermore, in order to evaluate the prediction accuracy of well-known empirical path loss models for urban environments, the measurements are compared with the predicted path loss values. The results show that the COST-231 Walfisch–Ikegami model has higher prediction accuracy and can be used for IQRF network planning in LoS links, while the COST-231 Hata model has better accuracy in NLoS links. On the other hand, the effects of antennas on the performance of IQRF transceivers (TRs) for LoS and NLoS links are also scrutinized. The use of IQRF TRs with a Straight-Line Dipole Antenna (SLDA) antenna is found to offer more stable results when compared to IQRF (TRs) with Meander Line Antenna (MLA) antenna. Therefore, it is believed that the findings presented in this article could offer useful insights for researchers interested in the development of IoT-based smart city applications. Full article

LoRaWAN has become popular as an IoT enabler. The low cost, ease of installation and the capacity of fine-tuning the parameters make this network a suitable candidate for the deployment of smart cities. In northern Sweden, in the smart region of Skellefteå, we have deployed a LoRaWAN to enable IoT applications to assist the lives of citizens. As Skellefteå has a subarctic climate, we investigate how the extreme changes in the weather happening during a year affect a real LoRaWAN deployment in terms of SNR, RSSI and the use of SF when ADR is enabled. Additionally, we evaluate two propagation models (Okumura-Hata and ITM) and verify if any of those models fit the measurements obtained from our real-life network. Our results regarding the weather impact show that cold weather improves the SNR while warm weather makes the sensors select lower SFs, to minimize the time-on-air. Regarding the tested propagation models, Okumura-Hata has the best fit to our data, while ITM tends to overestimate the RSSI values. Full article

Due to rapid development in mobile communication technology in recent years, the demand for high quality and high capacity networks with thorough coverage has become a major necessity. Several models have been developed for predicting wireless signal coverage in urban areas, but these models suffer from inadequately calculating certain conditions, such as weather and building materials, especially window size. In this paper, we propose a new path loss prediction model based on the measurement of new indicators, such as window size, temperature, and humidity conditions, after which an extensive statistical analysis using a linear regression technique was implemented in order to validate the new indicators. As the new indicators were incorporated into the Okumura model to derive a new path loss model, the results showed that the proposed model provides an accurate prediction of the received signal strength in a given propagation environment. Our model enhanced the prediction of path loss by 10% when compared to the Okumura and by 15% when compared to the COST-Hata. Full article