Search Results (30)

Radio Environment Map (REM) construction is frequently constrained by sparse and unevenly distributed spectrum measurements. While existing completion methods primarily target Power Spectral Density (PSD) data under random missing patterns, the reconstruction of Reference Signal Received Power (RSRP) maps under structured data loss remains underexplored. This study addresses this gap by proposing a fully convolutional densely connected autoencoder(AE) for RSRP map completion. The encoder stacks dense blocks and transition layers, a bottleneck preserves the latent representation, and the decoder restores spatial resolution through transposed convolution. Both global and local skip connections are incorporated to fuse large-scale structure with fine-grained details. A composite loss function supervises observed and missing regions separately, which preserves the fidelity of known measurements while improving inference over unobserved grid points. Experiments on the public DeepREM dataset under random, spatial, and strip-wise missing patterns show that the method achieves the best or comparable completion accuracy in most tested settings, with the most pronounced performance gains over mainstream baselines under the challenging spatial block-missing case. Full article

The evolution of Advanced Metering Infrastructure (AMI) requires reliable, energy-efficient, and scalable communication technologies for connecting large numbers of smart meters and gateways with utility backend systems. Among 3GPP Cellular Internet of Things (CIoT) technologies, Narrowband IoT (NB-IoT) and LTE-M are promising candidates due to their extended coverage, low cost, and power efficiency. However, selecting between them remains challenging because performance depends on deployment environments, spatial distribution, and radio signal conditions. This study addresses the CIoT network selection problem in AMI networks by applying machine learning to predict the appropriate communication technology from smart meter location and Reference Signal Received Power (RSRP). Three supervised learning algorithms, namely Decision Tree, Support Vector Machine, and XGBoost, were evaluated using field measurement datasets from two AMI deployment areas. A spatial holdout strategy was applied to assess performance in unseen geographical regions. Decision Tree achieved the best performance in Area 1, with an accuracy of 0.7143 and an F1-score of 0.6154. In Area 2, XGBoost achieved the highest performance, with an accuracy of 0.9732 and an F1-score of 0.9388. The results demonstrate the feasibility of ML-based CIoT selection under spatially heterogeneous and imbalanced deployment conditions. Full article

The expansion of Internet-of-Things (IoT) deployments in 5G New Radio (NR) networks has made periodic measurement reporting a growing burden for low-mobility devices, which benefit little from frequent updates yet must report as often as highly mobile ones. At present, User Equipment (UE) transmits MeasurementReport messages at a fixed ReportInterval—typically 240 ms—regardless of mobility. This continuous transmission needlessly depletes UE battery energy and consumes critical uplink signaling capacity. This paper proposes a five-level adaptive ReportInterval selection scheme driven by the statistical properties of Reference Signal Received Power (RSRP) and Signal-to-Interference-plus-Noise Ratio (SINR). A low-mobility criterion combines four statistical conditions—the variance and gradient of both RSRP and SINR—through a logical AND, while a two-stage hysteresis mechanism (a 3 dB margin and a 2 s holding timer) suppresses unnecessary level transitions. The scheme is slice-agnostic: By relying on observed signal statistics rather than network-slice labels, it serves low-mobility mMTC and stationary eMBB devices while leaving URLLC and high-mobility UEs at their standard configuration. In Monte Carlo simulations over the 3GPP TR 38.901 Urban Micro (UMi) channel model (200 UEs, 300 s, 100 iterations), the algorithm attains a classification accuracy of 91.32% and a sensitivity of 98.77%. Based on the DRX energy model, it yields an estimated 10.87% reduction in average UE power (from 28.15 to 25.09 mW) together with a 51.09% reduction in the network-wide MeasurementReport count. The hysteresis mechanism cuts level transitions by a factor of 31.33 (from 6852.7 to 218.7 per iteration), substantially lowering RRC reconfiguration signaling. Operating at O(n) complexity on the gNodeB and using only conventional MeasConfig signaling, the scheme requires no protocol additions or UE-side modifications, making it directly deployable on existing 3GPP Release 17 infrastructure as a gNB-side software update. Full article

Low-frequency mobile bands remain essential for wide-area and penetration-limited wireless coverage, but their limited channel bandwidth constrains the achievable capacity. This paper presents a measurement-supported extrapolation framework for assessing how lowband MIMO and future AAS-assisted operation can enhance coverage and single-user throughput-oriented capacity in wireless networks. The motivation is to evaluate whether such deployments can strengthen the lower-frequency layer as a robust coverage-and-capacity support layer for general traffic and reduce the load on midband and higher-frequency resources. Controlled radiated SISO and $2\times 2$ MIMO measurements were performed with a base-station simulator and commercial user equipment in representative lowband and midband frequency bands. Measured RSRP, CQI, BLER, MAC-layer throughput, and IP-layer throughput thresholds for a 25 Mbit/s downlink target were used for coverage estimation and conditional extrapolation. Under the Extended Hata model, the measured $2\times 2$ MIMO thresholds yielded a 43% larger estimated radius at 800 MHz than at 1800 MHz, while the same model indicated a 93% radius increase for a representative 10 dB AAS-related beamforming gain scenario. Conditional $4\times 4$ MIMO extrapolations indicated data rates above 100 Mbit/s in 10 MHz and above 200 Mbit/s with 10 MHz two-component-carrier aggregation under ideal high-CQI conditions. The results support the potential of future lowband AAS deployments. The AAS and higher-order MIMO results are scenario-based estimates rather than direct field validation. Full article

Thailand’s Provincial Electricity Authority (PEA) is rolling out Advanced Metering Infrastructure (AMI) under its smart-grid initiative, requiring a reliable last-mile wireless network across heterogeneous propagation environments. Narrowband IoT (NB-IoT) is a leading candidate, but per-area deployment decisions have lacked a data-driven framework anchored to measured Thai propagation. Building on our sixteen-site composite-channel characterisation, this study presents a machine-learning feasibility-prediction framework integrating measured channel parameters (n, σ_sh, $\widehat{m}$), an OpenStreetMap-derived synthetic meter-density layer, and a benchmark of Random Forest, Gradient Boosting (GB), and Multi-Layer Perceptron classifiers trained on Monte-Carlo coverage labels to predict 95% RSRP-coverage feasibility per spatial cell. Across 411 cells from four Thai sites spanning Urban Dense, Urban Outdoor, Suburban, and Rural environments, GB achieves accuracy 0.971 and F1 0.969 at 1.7 ms inference latency—four orders of magnitude faster than direct Monte-Carlo simulation. The ML predictor approximates the Monte-Carlo engine under the assumed composite-channel model. A theoretical LPWAN comparison places NB-IoT as recommended for Suburban and Rural AMI; Suphan Buri (Rural) is the only RECOMMENDED case (88.5% cells feasible), with hybrid PLC backhaul suggested for dense urban areas. Full article

Narrowband Internet of Things (NB-IoT) is a 3GPP-standardised low-power wide-area network (LPWAN) technology designed for massive machine-type communications in challenging propagation environments. Despite its growing deployment, empirical channel data for Thailand’s diverse terrain—urban dense, urban outdoor, suburban, rural, and forest/mountain—remains limited in the open literature. This paper presents a composite channel characterisation study encompassing sixteen measurement sites across five environment classes in central and western Thailand. A composite channel model combining log-distance path loss, log-normal shadowing, and Nakagami-m fast fading is applied across all sites, yielding 8000 reference signal received power (RSRP) samples. Path loss exponents range from n = 2.2 (rural) to n = 4.0 (forest/mountain), back-calculated Nakagami-m parameters from m = 0.44 to m = 3.51, and shadowing standard deviations from σ_sh = 4.16 to 8.38 dB; ECL distributions are derived for all five environment classes. The back-calculated Nakagami-m parameters reveal a coherence gradient from sub-Rayleigh forest terrain (m < 1) through urban Rayleigh (m = 1.00) to near-Rician rural conditions (m > 2)—a fading hierarchy not previously reported for NB-IoT in Thailand. Results confirm that the composite channel model accurately characterises RSRP distributions and provides actionable network planning parameters for NB-IoT deployment in varied Thai terrain. Full article

Reliable radio coverage is essential for the deployment of 5G for railway (5G-R) communication systems in complex railway-line environments. Previous simulation- and measurement-based studies have mainly focused on main-track railway scenarios, while the propagation characteristics in railway-side obstructed environments remain insufficiently characterized. To address this gap, this paper investigates large-scale propagation characteristics using passive synchronization signal reference signal received power (SS-RSRP) measurements collected from a 5G-R test network. Typical railway-line scenarios, including open line-of-sight (LOS) propagation, building-obstructed railway-side sections, viaduct-blocked regions, and depot-like environments, are analyzed to reveal the influence of railway-side structures on large-scale signal behavior. A floating-intercept (FI) model is adopted to characterize scenario-dependent path loss, and a height-corrected FI refinement is further introduced for building-obstructed sections. The results show that local railway-side structures introduce distinct and quantifiable excess propagation loss beyond conventional distance-dependent path loss. The obtained model parameters can support large-scale propagation modeling, link-budget margin design, coverage-hole identification, and wireless coverage evaluation for 2100 MHz 5G-R systems in obstructed railway-side environments. Full article

A pressing challenge of modern wireless networks is ensuring stable radio coverage inside buildings, where radio signal propagation is significantly complicated by the influence of building structures. Reinforced concrete walls, floor slabs, internal partitions, and energy-efficient windows with metallized coatings create substantial obstacles to the propagation of electromagnetic waves, causing reflection, absorption, and scattering. As a result, areas with weakened coverage are formed inside buildings, leading to deterioration in mobile communication quality and reduced data transmission rates. This study presents an experimental investigation of the received signal strength of mobile operators inside a multi-storey residential complex. An analysis was conducted to evaluate the impact of building height, architectural features, and construction materials on radio signal propagation. In addition, the frequency bands used in 4G LTE and 5G networks by mobile operators were examined. It was found that LTE networks mainly operate in the 1.8–2.1 GHz frequency range, whereas 5G networks operate in the n77 band (3.6–3.7 GHz), which provides higher data throughput but is characterized by greater signal attenuation when propagating inside buildings. To address this issue, a Distributed Antenna System (DAS) based on GPON technology was implemented in the studied building. The placement of antenna equipment on the roof enabled the efficient reception of the signal from the base station and its subsequent distribution inside the building through an internal antenna network. The measurement results demonstrated that the deployment of a GPON-based DAS significantly improves the received signal level and ensures more uniform radio coverage inside indoor environments. The obtained results confirm that the use of distributed antenna systems is an effective solution for compensating signal losses caused by the shielding effect of building structures and can significantly improve the quality of mobile communications in dense urban environments. The results show that the RSRP level in indoor environments without DAS decreases to approximately −100 to −110 dBm, while after deployment of the GPON-based DAS, it improves to −45 to −75 dBm. This corresponds to a signal gain of up to 40–50 dB, ensuring stable connectivity and significantly improved data transmission performance. Full article

In the evolving landscape of next-generation wireless networks, ensuring seamless mobility and high-quality service delivery for millions of devices and end users in dynamic scenarios, where the speed of a wireless device keeps changing with time, is important. The mobility, seamless and continuous connectivity, and ultra-dense deployment of wireless networks pose a significant challenge. Seamless and successful transition of a wireless device from point A to point B in variable-speed scenarios is one of the major challenges in future networks. This paper presents a novel Deep Q-Network (DQN)-based reinforcement learning (RL) framework integrated with Software-Defined Networking (SDN) for intelligent mobility management in hybrid 5G cellular networks consisting of macro and small base stations. The proposed system architecture utilizes a SDN controller to receive real-time user measurement reports, including Reference Signal Received Power (RSRP), Signal-to-Interference Noise Ratio (SINR), and user velocity, thereby classifying user mobility into distinct subclasses and dynamically determining optimal handover parameters. Leveraging the DQN’s capability to learn adaptive strategies, the model enables seamless transitions between macro and small cells based on mobility profiles, thereby enhancing Quality of Service (QoS) metrics such as latency, throughput, and handover efficiency. Simulation results demonstrate consistent performance improvements over baseline and existing models in ultra-dense network environments, with handover success rates 10–15% higher across SINR and different speed scenarios, while maintaining a packet failure rate of 9% across different speed scenarios, allowing more users to transition during various environmental changes seamlessly. Our proposed model is compared with our previous work and Learning-based Intelligent Mobility Management (LIM2) models. Specifically, our previous work focused on adaptive handover management primarily for high-speed train scenarios using a learning-assisted approach tailored to fixed high-mobility scenarios, with a limitation to single mobility conditions. This work contributes to the field of merging SDN’s centralized control with the predictive power of RL, paving the way for more resilient and responsive mobile networks in high-mobility scenarios. The proposed approach incorporates subclass-based mobility action abstraction, joint optimization of TTT and hysteresis margin, and dynamic target cell selection using global network information available at the SDN controller. Full article

Radio network planning is critical for 5G deployments, particularly for temporary installations in rural areas where terrain and vegetation significantly impact signal propagation. While empirical path loss (PL) models characterize propagation environments through scenario-specific parameters—leading to inherently noisy predictions at individual sites—machine learning (ML) approaches can predict site-specific path loss from multiple features simultaneously. This study conducts a systematic literature review of rural path loss prediction methods and introduces a novel dataset collected via a 5G nomadic measurement platform in a vineyard environment, capturing real-world propagation characteristics. We present a comprehensive comparison of machine learning and interpretable machine learning techniques, demonstrating that vegetation dynamics (quantified through the Normalized Difference Vegetation Index, NDVI) is an important driver of path loss variability when combining data across seasonal campaigns—though not within individual campaigns, where distance dominates. Cross-campaign NDVI transfer, however, is sensitive to satellite resolution, which appears to conflate vine canopy with seasonally managed inter-row ground cover. In cross-campaign transfer, XGBoost proves substantially less susceptible to NDVI-induced degradation than Explainable Boosting Machines (EBM), and a hybrid Log-Normal Shadowing (LNS) and XGBoost model confirms that NDVI captures seasonal variability more effectively than empirical path loss parameters alone. Still, the data captured the expected seasonal trend between April and June 2025, from which our interpretable models derived useful propagation insights. Tree-based models like Random Forest and XGBoost achieved the highest prediction accuracy ($R^2$ up to 0.924 on individual campaigns, 0.891 on combined data, and up to 0.945 (individual) and 0.907 (combined) with antenna pattern-corrected path loss), while explainable boosting machines achieved near-parity ($R^2$ up to 0.919; 0.876 on combined data) with the advantage of interpretability. Among individual campaigns, June—with densest canopy cover—yielded the highest $R^2$ values. These findings provide actionable insights for optimizing temporary 5G networks in precision agriculture and other rural applications. Full article

Reliable last-mile connectivity at the cell edge remains a central challenge for Advanced Metering Infrastructure (AMI) in smart grids. This work addresses how to select between LTE-M and NB-IoT communications under weak-coverage conditions by combining field measurements with distribution-based channel modeling. We analyze multi-month Reference Signal Received Power (RSRP) datasets from three areas of a real AMI deployment (N = 30, 35, and 38 m, respectively) and fit canonical fading surrogates—Rayleigh, Rician, and Nakagami—to the normalized measurements. The principal decision statistic is the probability that RSRP falls below a practical threshold (−105 dBm), obtained from empirical and modeled CDF and translated into the predicted number of meters requiring fallback to NB-IoT. Across areas, Nakagami consistently provides the lowest or near-lowest Root Mean Square Error (RMSE) against empirical CDF and the closest agreement with observed fallback counts at −105 dBm, whereas Rayleigh tends to underestimate deep fade tails and Rician degrades when line-of-sight is weak. A threshold sweep sensitivity study (−110 to −89 dBm) using Area 3 illustrates how the predicted fallback population changes monotonically with the decision threshold and supports policy tuning. Overall, a CDF-anchored, Nakagami-guided rule at −105 dBm aligns technology selection with measured channel statistics, improving the robustness of Cellular IoT (CIoT) last-mile communications. Full article

This paper presents a novel, four-port, rectangular microstrip, inset-feed multiple-input and multiple-output (MIMO) antenna array, enhanced with metamaterials for improved gain and isolation, specifically designed for multi-operator 5G open radio access network (ORAN)-based indoor software-defined radio (SDR) applications. ORAN is an open-source interoperable framework for radio access networks (RANs), while SDR refers to a radio communication system where functions are implemented via software on a programmable platform. A 3 × 3 metamaterial (MTM) superstrate is placed above the MIMO antenna array to improve gain and reduce the mutual coupling of MIMO. The proposed MIMO antenna operates over a 300 MHz bandwidth (3.5–3.8 GHz), enabling shared infrastructure for multiple operators. The antenna’s dimensions are 75 × 75 × 18.2 mm³. The antenna possesses a reduced mutual coupling less than −30 dB and a 3.5 dB enhancement in gain with the help of a novel 3 × 3 MTM superstrate 15 mm above the radiating MIMO elements. A performance evaluation based on simulated results and lab measurements demonstrates the promising value of key MIMO metrics such as a low envelope correlation coefficient (ECC) < 0.002, diversity gain (DG) ~10 dB, total active reflection coefficient (TARC) < −10 dB, and channel capacity loss (CCL) < 0.2 bits/sec/Hz. Real-world testing of the proposed antenna for ORAN-based sub-6 GHz indoor wireless systems demonstrates a downlink throughput of approximately 200 Mbps, uplink throughput of 80 Mbps, and transmission delays below 80 ms. Additionally, a walk test in an indoor environment with a corresponding floor plan and reference signal received power (RSRP) measurements indicates that most of the coverage area achieves RSRP values exceeding −75 dBm, confirming its suitability for indoor applications. Full article

In high-mobility non-terrestrial networks (NTN), the reference signal received power (RSRP)-based handover (RBH) mechanism is often unsuitable due to its limitations in handling dynamic satellite movements. RSRP, a key metric in cellular networks, measures the received power of reference signals from a base station or satellite and is widely used for handover decision-making. However, in NTN environments, the high mobility of satellites causes frequent RSRP fluctuations, making RBH ineffective in managing handovers, often leading to excessive ping-pong handovers and a high handover failure rate. To address this challenge, we propose an innovative approach called location-based handover with particle filter and reinforcement learning (LBH-PRL). This approach integrates a particle filter to estimate the distance between user equipment (UE) and NTN satellites, combined with reinforcement learning (RL), to dynamically adjust hysteresis, time-to-trigger (TTT), and handover decisions to better adapt to the mobility characteristics of NTN. Unlike the location-based handover (LBH) approach, LBH-PRL introduces adaptive parameter tuning based on environmental dynamics, significantly improving handover decision-making robustness and adaptability, thereby reducing unnecessary handovers. Simulation results demonstrate that the proposed LBH-PRL approach significantly outperforms conventional LBH and RBH mechanisms in key performance metrics, including reducing the average number of handovers, lowering the ping-pong rate, and minimizing the handover failure rate. These improvements highlight the effectiveness of LBH-PRL in enhancing handover efficiency and service continuity in NTN environments, providing a robust solution for intelligent mobility management in high-mobility NTN scenarios. Full article

The advent of 5G technology has facilitated the adoption of private cellular networks in industrial settings. Ensuring reliable coverage while maintaining certain requirements at its boundaries is crucial for successful deployment yet challenging without extensive measurements. In this article, we propose the leveraging of unmanned aerial vehicles (UAVs) and Gaussian processes (GPs) to reduce the complexity of this task. Physics-informed mean functions, including a detailed ray-tracing simulation, are integrated into the GP models to enhance the extrapolation performance of the GP prediction. As a central element of the GP prediction, a quantitative evaluation of different mean functions is conducted. The most promising candidates are then integrated into an informative path-planning algorithm tasked with performing an efficient UAV-based cellular network mapping. The algorithm combines the physics-informed GP models with Bayesian optimization and is developed and tested in a hardware-in-the-loop simulation. The quantitative evaluation of the mean functions and the informative path-planning simulation are based on real-world measurements of the 5G reference signal received power (RSRP) in a cellular 5G-SA campus network at the Port of Lübeck, Germany. These measurements serve as ground truth for both evaluations. The evaluation results demonstrate that using an appropriate mean function can result in an enhanced prediction accuracy of the GP model and provide a suitable basis for informative path planning. The subsequent informative path-planning simulation experiments highlight these findings. For a fixed maximum travel distance, a path is iteratively computed, reducing the flight distance by up to 98% while maintaining an average root-mean-square error of less than 6 dBm when compared to the measurement trials. Full article

This study introduces a low-cost alternative method for mapping the electric field strength from 4G LTE base stations and identifies areas where this mapping is more accurate. A drive test campaign was conducted in the urban environment of Thessaloniki, Greece, using data obtained from three identical smartphones, each connected to a different mobile operator and an exposimeter. The smartphones used a mobile application to record Reference Signal Received Power (RSRP) values, while the exposimeter measured the electric field strength in selected frequency bands. In the first part, the variability of the received power over different periods within certain areas was studied, and the reasons for this variability were identified. In the second part, a linear factor was calculated to convert RSRP values into electric field strength using data from both the application and the exposimeter. The converted RSRP values were subsequently compared with the exposimeter data for validation. The results indicate that in areas where the variability of the received power is lower, the linear relationship between smartphone and exposimeter data is statistically stronger resulting in calculated electric field strength values are closer to the measured. Full article