Mobile Base Stations Research Articles

Energy‐efficient power control and altitude planning for UAV‐based vehicular communications in 5G NR‐V2X networks

AbstractUnmanned aerial vehicles (UAVs) are becoming increasingly popular in various industries, including telecommunications. In the realm of 5G new radio vehicle‐to‐everything (NR‐V2X) networks, UAVs can be used as mobile base stations (UAV‐BS) to provide vehicular communications. However, effective power control and altitude planning algorithms are required to maximize the energy efficiency (EE) of these UAV‐BS systems. In this article, we propose an energy‐efficient power control and altitude planning algorithm for UAV‐BS in NR‐V2X networks. We formulate the optimization problem of maximizing the system's EE while considering the reliability requirements of vehicular users, the limitations of UAV‐BS hovering altitude, and the feasibility of transmission power levels. The problem is nonconvex, and we address this by reformulating it into a convex problem using the Dinkelbach and alternating optimization methods. The simulation results demonstrate that the proposed algorithm closely achieves the performance of exhaustive search while it is more computationally efficient. Moreover, our algorithm by jointly optimizing transmission power and hovering altitude performs better than other schemes proposed in the literature, as shown through our simulation results.

A coordinates-based hierarchical computing framework towards spatial data processing

The vehicle-based mobile base station (VMBS) has been developed by NTT a few years ago, which plays an important role in reconstructing an emergency communication network by integrating with mobile edge computing to support the cloud service for data pre-processing tasks. Spatial data processing refers to characteristics, such as fast-changing, massive, and potentially infinite. Considering the capacities of network bandwidth and storage of VMBS, new challenging issues have emerged such as how to quantify information loss and how to optimize data processing in the paradigm of mobile edge computing. To address these two challenges, we design a coordinates-based hierarchical computing framework for spatial data processing, named HierCom. Using HierCom, the optimal position of VMBS can be selected to decrease the transmission delay, thereby reducing information loss in the data processing in an edge computing environment. Furthermore, we also propose a near least neighbor-based algorithm to optimize the information loss. Finally, simulations on the real-world dataset show that the information loss of our method is reduced by 27% compared with the baseline method.

Exposure levels of radiofrequency electromagnetic fields from mobile base stations in Mpumalanga province, South Africa

This study assessed the radiofrequency electromagnetic fields (RF-EMF) exposure levels proximal to primary school structures in the Mpumalanga province of South Africa. A calibrated Acoustimeter was used to measure the RF EMF levels at defined distance points of 50 and 100m away from the three mobile base stations, that provide a 4G network coverage. All the measurements were obtained from three mobile stations, each from one district municipality, using 30 minutes intervals for repeated measurements. The measured peak exposure level at 50m was 887 μW/m2 and 905 μW/m2 at 100m. No statistical difference was found when comparing the measurement data obtained at 50 and 100m from the three district municipalities. However, comparison of measurements obtained at 50 and 100m for all base stations were statistically significant, p-value< .002 and p< .003 respectively. The measured RF EMF exposure levels at both distance points were below the reference levels found under International Commission for Non-ionizing Radiation Protection (ICNIRP) RF guidelines. It is therefore essential for mobile base station companies to consider proximity of residential structures when installing new base stations in the future, and regularly maintain the components of mobile base towers, especially in the current state of power load shedding in South Africa.

Simulation on Methanol Reformed High-Temperature PEM Fuel Cell System and the Experimental Verification

A mathematical model of the SRM-HTPEM power generation system was developed to simulate the effects of parameters such as temperature and molar water to methanol ratio on the performance of the reformer and the power stack in the system. The SRM-HTPEM power generation system was built at a mobile communication base station, and the energy efficiency of the system was 45.5% at an output power of 2.8 kW. The correctness of the simulation results was verified by experiments. It finds that increasing the reaction temperature and decreasing the weight hourly space velocity will enhance the conversion of methanol and increase the CO content in the reforming gas. Increasing the molar water to methanol ratio of the feed can improve the methanol conversion and reduce the CO content. However, if the molar water to methanol ratio is too high, the energy efficiency of the system may reduce. Appropriately increasing the operating temperature of the stack, anode hydrogen concentration and hydrogen excess ratio will increase the performance of the fuel cell.

Electromagnetic environment created by mobile communication base stations in the 5G pilot area

Introduction. In the context of 5G system integration for general public, the change of electromagnetic field background is expected. The electromagnetic field background will change in spectral composition, spatial and temporal distribution, which affects the methodological approaches of instrumental control and hygienic assessment.&#x0D; Materials and methods. In the 5G pilot area the frequency-selective, code-selective and broadband measurements were carried out for the actual and possible electromagnetic field assessment from GSM, UMTS, LTE and 5G/IMT-2020 base station at several points.&#x0D; Results. The research results have shown that the actual electromagnetic levels did not exceed 1.5 μW/cm2, the main part of actual (measured) level includes GSM and LTE base stations (1800 MHz). At some points, the main part of maximum possible electromagnetic field exposure did not exceed 8.5 μW/cm2 and created by the 5G/IMT-2020 traffic transmission beam from base station to the subscriber terminal.&#x0D; Limitations. The limitation of study is determined by the measurement conditions of 5G/IMT-2020 test operation modes in the pilot area, it is difficult to assess the actual EMF exposure from commercial networks user service.&#x0D; Conclusion. In the intensive progress of mobile communication, the frequency- and code-selective measuring instruments are necessary to improve approaches for instrumental control and evaluation for electromagnetic fields from various mobile communication, especially for 5G assessment.

Dosimetric assessment in the brain for downlink EMF exposure in Korean mobile communication networks

Because the position and direction of the human body is not fixed in an actual environment, the incidence direction of the electromagnetic field (EMF) from mobile communication base stations, WiFi access points, broadcasting towers, and other far-field sources is arbitrary. To analyze the overall health effects of radio frequency EMF exposure, the dosimetric assessment for such environmental exposures created from an unspecified number of sources in daily life, along with exposures from specific EMF sources, must be quantified. This study is aimed at numerically evaluating the time-averaged specific absorption rate (SAR) of the human brain for environmental EMF exposure in the frequency range of 50–5800 MHz. Whole-body exposure to EMFs that are evenly incident spatially is considered. By comparing the results of several incidence directions and the number of polarizations, an optimal calculation condition has been derived. Finally, based on the results measured in Seoul at the end of 2021, the SAR and daily specific energy absorption (SA) in the brains of both a child and an adult for downlink exposures from 3G to 5G base stations are reported. Comparison results of the daily brain SA for exposure to DL EMF in all 3G to 5G mobile networks and exposure to a 10-min voice call (uplink EMF) using a mobile phone connected to a 4G network show that the SA from the downlinks is much higher than that from the uplinks.

Multi-UAV Navigation for Partially Observable Communication Coverage by Graph Reinforcement Learning

In this paper, we aim to design a deep reinforcement learning (DRL) based control solution to navigating a swarm of unmanned aerial vehicles (UAVs) to fly around an unexplored target area under partial observation, which serves as Mobile Base Stations (MBSs) providing optimal communication coverage for the ground mobile users. To handle the information loss caused by the partial observability, we introduce a novel network architecture named Deep Recurrent Graph Network (DRGN), which could obtain extra spatial information through graph-convolution based inter-UAV communication, and utilize historical features with a recurrent unit. Based on DRGN and maximum-entropy learning, we propose a stochastic DRL policy named Soft Deep Recurrent Graph Network (SDRGN). In SDRGN, a heuristic reward function is elaborated, which is based on the local information of each UAV instead of the global information; thus, SDRGN reduces the training cost and enables distributed online learning. We conducted extensive experiments to design the structure of DRGN and examine the performance of SDRGN. The simulation results show that the proposed model outperforms four state-of-the-art DRL-based approaches and three heuristic baselines, and demonstrate the scalability, transferability, robustness, and interpretability of SDRGN.

Research on 5G Network Slicing Strategy for Urban Complex Environment

Focusing on the layout of the 5G mobile communication base station in the city center, we design a 5G city network slicing strategy for the three typical application scenarios with enhanced mobile broadband (eMBB), ultrareliable low-latency communications (URLLC), and massive machine type communications (mMTC). The strategy considers multiple important network performance indicators, including user guaranteed bandwidth, maximum bandwidth limit, QoS (quality of service), link delay tolerance, and slicing throughput. The slicing strategy can greatly increase the connections of base station clients and the utilization of network resources, and effectively reduce block radio and handover radio. The simulation experiments adopt the 5G base station dataset of a coastal city layout in Zhejiang province. Our tests show that the 5G network slicing strategy has certain advantages in network transmission performance in urban complex environment. The research can provide an effective reference for 5G infrastructure construction in other cities.

Energy Consumption in Wireless Systems Equipped with RES, UAVs, and IRSs

This paper investigates energy budget characteristics of mobile base stations (BSs) having the form of unmanned aerial vehicles (UAVs) equipped with radio frequency (RF) transceivers, intelligent reconfigurable surfaces (IRSs), and renewable energy sources (RES). The results obtained highlight the benefits and challenges related to using the aforementioned mobile BS, from the energy-related point of view. The specific cases researched involved two types of UAV devices, i.e. multirotor and fixed-wing (airplane-like) aircraft.

Dynamic adaptive workload offloading strategy in mobile edge computing networks

With the increasing popularity of mobile devices and explosive growth of task processing requirements, edge computing attracts more attention from researchers nowadays since it can improve the QoS and utilize resources of the cloudlets, including mobile devices and base stations, as much as possible. In a Mobile Edge Computing (MEC) network, the workload offloading problem is quite important since it directly influences the latency of the task processing, and many efficient algorithms have been proposed to deal with it. However, most of the existing algorithms only consider the static or quasi-static scenario, and are not suitable for a system with several fast-moving devices. Since that more and more mobile devices with high speed are involved in a MEC system, a new workload offloading strategy is required to adapt to such a dynamic scenario. In this paper, we sufficient consider the dynamic properties of a MEC system, and proposed a dynamic adaptive workload offloading algorithm based on Lyapunov theories and an FC-LSTM based schedule determining algorithm to balance the workload of different cloudlets and minimize the weighted average energy and time consumption of mobile devices. Theoretical analysis and extensive experimental results show that all the proposed algorithms achieve high performance in terms of energy consumption, convergence and latency.

Analysis of Electromagnetic Radiation of Mobile Base Stations Co-located with High-Voltage Transmission Towers

This paper presents the analysis of electromagnetic radiation of mobile base stations co-located with high-voltage transmission towers. Although the layout of power poles and towers is uniform and symmetrical, the electromagnetic field radiated to the outside world is asymmetric. Field measurements were conducted in different co-located base station scenarios, and the field strength results in both the vertical and horizontal directions were analyzed in depth. Then, the ray tracing simulation method was used to obtain the electromagnetic field distribution characteristics for the 5G base station co-located high-voltage tower. Finally, the specific absorption rate (SAR) was adopted to evaluate human exposure in co-located base station scenarios, and a physical area-based human exposure assessment method proposed. The obtained results can be useful for inspectors of mobile base stations co-located with high-voltage transmission towers to avoid or reduce the impact of electromagnetic radiation.

Unmanned Aerial Vehicle-Assisted Sparse Sensing in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) suffer from enormous energy consumption when sensor nodes transmit data over long distances. Hence, this paper develops an Unmanned Aerial Vehicle (UAV)-assisted sparse sensing algorithm to address this issue. Specifically, the data from the sensor nodes are locally stored and are sparsely sampled through Compressive Sensing (CS), while a UAV is utilized as a mobile base station to collect data from the partial sensor nodes. In the proposed UAV and WSN system, the energy consumption minimization problem is formulated as a combinatorial optimization problem, which is solved utilizing a joint sparse sensing and Greedy algorithm based on a Neighborhood Search Strategy (GNSS). Extensive experiments demonstrate that the proposed algorithm achieves a sparse measurement with a smaller error and decreases energy consumption.

The Analysis and Verification of IMT-2000 Base Station Interference Characteristics in the FAST Radio Quiet Zone

In this study, we aim to analyze the electromagnetic interference (EMI) regarding the Five-hundred-meter Aperture Spherical radio Telescope (FAST) caused by base stations in the International Mobile Telecommunications-2000 (IMT-2000) frequency band. By analyzing the frequency bands used by the transmitting and receiving devices and the surrounding environmental parameters and utilizing an approach to predicting radio wave propagation loss that is based on deterministic methods, we conclude by comparing the predicted received power at the FAST with its interference protection threshold. Our analysis demonstrates that, currently, only 55.31% of IMT-2000 base stations in the FAST radio quiet zone (RQZ) meet the protection threshold. Additionally, this article verifies the applicability and accuracy of the radio wave propagation model used in the research based on field strength measurements. Overall, this study provides valuable insights for improving the electromagnetic environment surrounding FAST and reducing the EMI caused by mobile communication base stations. It also provides corresponding analysis methods and useful suggestions for analyzing electromagnetic radiation interference in other radio telescopes.

Control-Oriented Power Allocation for Integrated Satellite-UAV Networks

This letter presents a sensing-communication-computing-control (SC3) integrated satellite unmanned aerial vehicle (UAV) network, where UAVs are equipped with sensors, mobile edge computing (MEC) servers, base stations and satellite communication modules. Like a nervous system, this integrated network is capable of organizing multiple field robots in remote areas, so as to perform mission-critical tasks which are dangerous for humans. Aiming at activating this nervous system with multiple SC3 loops, we present a control-oriented optimization problem. Different from traditional studies which mainly focused on communication metrics, we address the power allocation issue to minimize the sum linear quadratic regulator (LQR) control cost of all SC3 loops. Specifically, we show the convexity of the problem and reveal the relationship between the optimal transmit power and intrinsic entropy rates of different SC3 loops. For the assure-to-be-stable case, we derive a closed-form solution for ease of practical applications. After demonstrating the superiority of the control-oriented power allocation, we further highlight its difference from the classic capacity-oriented water-filling method.

Residential exposure to non-ionizing electromagnetic radiation from mobile base stations: a systematic review on biological effects assessment

The effects of non-ionizing electromagnetic radiation from cellular base station exposure on human health are discussed in this review. Because of technological development, electromagnetic emissions are present at high levels in human existence. Due to the health risks linked with exposure to electromagnetic radiation (EMR), its effects are known. The systematic review work was done to identify measured values of non-ionizing radiation (such as power density and electric and magnetic fields) emitted by phones and GSM base stations, along with any potential biological consequences connected with the measured values. In the literature, all measurements of power density, electric fields, and magnetic fields were made using various instruments over a variety of base station distances and times. The findings from the literature showed that the non-ionizing electromagnetic radiation's power density, electric field, and magnetic field varied with distance from the tower and were highest at the closest distances. The findings also demonstrate that, in some situations, home exposure levels to base station-emitted non-ionizing electromagnetic radiation are within the limit. In some instances, however, the situation varies, and persons living close to base stations experience health effects from high levels of non-ionizing electromagnetic field exposure. The biological effects of non-ionizing electromagnetic radiation exposure in homes are summarized in this article. Numerous studies to date have established the dangers of mobile phone base station radiation to people and wildlife. The negative biological effects of exposure to non-ionizing electromagnetic radiation were compiled from numerous articles. Residents who are exposed to higher levels of non-ionizing electromagnetic radiation at home are more likely to experience symptoms like fatigue, nausea, disturbed sleep, discomfort, headache, memory loss, skin problems, visual disturbances, hearing problems, dizziness, muscle pain, DNA damage, and infertility. Some mobile base stations are situated quite close to homes and commercial buildings, putting individuals at risk from the base stations' electromagnetic radiation. Depending on the infrastructure impediment, such as interior obstructions or buildings, the emf changed from location to location. As a result, cellular mobile towers shouldn't be placed closer than 200 meters from residential structures.

Large-Area Monitoring of Radiofrequency Electromagnetic Field Exposure Levels from Mobile Phone Base Stations and Broadcast Transmission Towers by Car-Mounted Measurements around Tokyo

Car-mounted measurements of radiofrequency electromagnetic exposure levels were carried out in a large area around Tokyo. Prior to the electric field (E-field) measurements using a car, the effect of the car body was evaluated in an anechoic chamber. The measurements between May 2021 and February 2022 were carried out within a radius of 100 km centering on Nihonbashi, Tokyo, with a measurement distance of about 13,800 km. The measurement results were averaged in the reference area mesh (1 km2). It was found that the E-field strengths of FM/TV frequency bands are lower than that of mobile phone base stations. It was also found that the E-field strength of only the 5G frequency band is approximately 20–30 dB lower than that of all mobile phone systems. However, note that it is possible to depend on the data traffic of 5G. The E-field strength of all bands is higher in Tokyo than in other prefectures. Additionally, repeated measurements were carried out to investigate the reproducibility of the measured E-field. The standard deviation is less than 3 dB along the same route, and a similar tendency of E-field strength by the car to the time-averaged results of spot measurements in the past was confirmed. Finally, the relationship of E-field strength with population density was investigated. It was found that the E-field strength from mobile phone base stations has a positive relationship with population density.

Data Normalization Methods of Hybridized Multi-Stage Feature Selection Classification for 5G Base Station Antenna Health Effect Detection

It is essential to assess human exposure to Fifth Generation (5G) Radiofrequency Electromagnetic Field (RF-EMF) signal from Base Station (BS) sources operating at Low Band 5G at 700 MHz, Sub-6 Band 5G at 3.5 GHz, and Millimeter Wave (mmWave) 5G at 28 GHz. This assessment will help determine whether 5G technology is safe for people. Inconsistent results were found in previous epidemiological studies on the health effects of radiation exposure from Mobile Phones (MP) and BS when normalization methods were used to prepare the data for Machine Learning (ML), which could lead to misclassification because of the dataset's quality. The effects on adult health are assessed in terms of physiological parameters (body temperature, heart rates, and blood pressure), cognitive performance (brain memory, motor control, and attention), Visual Analogue Scales (VAS) for well-being parameter, and Electromagnetic Field (EMF) perception parameter. The purpose of the research was to identify changes in the physiological parameters of adult individuals before, during, or after exposure to 5G signals, including Sham (No Exposure). 12 normalization methods are selected which are Z-score normalization (z score), Linear scaling (LS), Binary normalization (BNN), Bipolar normalization (BPN), Min-Max scaling (MMS), t-score normalization (t score), Decimal Inverse Logarithmic Scaled Normalization (DILSN), Relative Mean Normalization (RMN), Relative Standard Deviation Normalization (RSDN), Variation Normalization (VN), Robust Normalization (RN) and Relative Interquartile Normalization (RIN) and based on their F-value and p-value analyses, the three best normalization techniques are selected in this research. The original distribution of each parameter data variable is different, these techniques are beneficial for converting data so that it is dimensionless and has equivalent distributions. Based on the selection criteria for the hybridized Multi-Stage Feature Selection (MSFS) classification for 5G base station antenna health effect detection, BNN, MMS, and RSDN were named as the top three normalization techniques.

3-D Deployment and Trajectory Planning for Relay Based UAV Assisted Cooperative Communication for Emergency Scenarios Using Dijkstra's Algorithm

In this work, a relay-based UAV-assisted cooperative communication for emergency applications is proposed for rural and semi-urban scenarios. 3-D deployment of multiple UAVs is resolved by jointly optimizing the height and transmit power of cluster UAVs and the optimal trajectory of relay UAVs is perfected by using the graph theory approach. While the majority of works in literature use UAVs as aerial base stations or as relays, this paper proposes to use UAVs as both relays and aerial base stations. Furthermore, this is one of the few works where UAVs are used as both static base stations (cluster UAVs) and mobile base stations (relay UAVs). The proposed 3-D deployment and trajectory planning algorithm is implemented at the ground base station ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\text {GBS}}$</tex-math></inline-formula> ) and the resultant parameters will be distributed to cluster UAVs either directly or through relay UAVs. A four-step approach is proposed to solve the 2-D location coordinates of cluster UAVs, to find the location of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\text {GBS}}$</tex-math></inline-formula> , the power-height optimization of cluster UAVs, and to optimize the trajectory of relay UAVs. The number of users served, channel capacity achieved, power saved due to optimal deployment, latency reduced, and power saving by optimal trajectory are some of the performance metrics used to evaluate the proposed algorithms. The simulation results are validated to show that the proposed solutions outperform existing similar approaches.

Approximated Assignment Algorithms for Unordered and Ordered Tasks in Data Shared MEC Systems

The appearance of Mobile Edge Computing (MEC) successfully solves the bottlenecks of traditional Cloud based networks. Since mobile edges, e.g., base stations, and mobile devices have certain data processing capabilities, it is not necessary to offload all the tasks to the cloud for handling. Therefore, it is quite important to decide the optimal task assignment in MEC systems, and a series of algorithms have been proposed. However, the existing algorithms ignored the data distribution during task assignment, so that their applied ranges are quite limit. Considering the data sharing is quite important in a MEC system, this paper studies task assignment algorithms in Data Shared Mobile Edge Computing Systems in detail. Specifically, three algorithms are proposed to deal with the unordered and ordered holistic tasks respectively. Meanwhile, the situation that the tasks are divisible is also considered, and two algorithms for rearranging the divisible tasks are proposed for different optimization goals. The hardness of the problem, the correctness, complexities, and ratio bounds of the proposed algorithms are analyzed theoretically. Finally, extensive experimental results are carried out. Both theoretical analysis and experiment results show that all the proposed algorithms have high performance in terms of latency, satisfied rate, and energy consumption.

A new connectivity model for unmanned aerial vehicle communications and flying height optimization

AbstractThis paper presents a new connectivity model for unmanned aerial vehicle (UAV) communications. We present closed‐form formulas for the probability of line‐of‐sight as a function of UAV height and distance to a ground based receiver, based on a two‐dimensional model of building and UAV locations. We show that our formulas exactly match Monte‐Carlo simulated values, whereas the existing approach over‐predicts by a factor of 2 to 3. We use the model to optimize the flying height for a UAV deployed as a mobile base station, where the goal is to maximize the number of possible connected users. We observe that the optimal height has a log‐linear characteristic. We use this to present a closed‐form formula for optimal height. We show that our techniques predict the height within 4% of realistic Monte‐Carlo simulations, and significantly outperform existing approaches which are shown to over‐predict by 40%–50%.