Mobile Base Stations Research Articles

Evidence for a health risk by RF on humans living around mobile phone base stations: From radiofrequency sickness to cancer

The objective of this work was to perform a complete review of the existing scientific literature to update the knowledge on the effects of base station antennas on humans. Studies performed in real urban conditions, with mobile phone base stations situated close to apartments, were selected. Overall results of this review show three types of effects by base station antennas on the health of people: radiofrequency sickness (RS), cancer (C) and changes in biochemical parameters (CBP). Considering all the studies reviewed globally (n = 38), 73.6% (28/38) showed effects: 73.9% (17/23) for radiofrequency sickness, 76.9% (10/13) for cancer and 75.0% (6/8) for changes in biochemical parameters. Furthermore, studies that did not meet the strict conditions to be included in this review provided important supplementary evidence. The existence of similar effects from studies by different sources (but with RF of similar characteristics), such as radar, radio and television antennas, wireless smart meters and laboratory studies, reinforce the conclusions of this review. Of special importance are the studies performed on animals or trees near base station antennas that cannot be aware of their proximity and to which psychosomatic effects can never be attributed.

Trajectory Design and Link Selection in UAV-Assisted Hybrid Satellite-Terrestrial Network

Mobile relaying is envisioned as a promising technology to alleviate the masking effect in satellite links. Nevertheless, due to the mobility and limited communication range, the network topology in mobile relay networks becomes highly dynamic and time-varying, resulting in increased difficulty of network optimization. This letter investigates unmanned aerial vehicles (UAVs) aided hybrid satellite-terrestrial network, where UAVs are served as mobile relay base stations to assist the communication between the satellite and ground users. We aim to maximize the number of served users by optimizing UAV trajectory and user link selection. To solve the formulated mixed-integer and non-convex optimization problem, we first find the optimal link selection via a designed graph neural network (GNN), and then adjust the UAV locations by using model-free reinforcement learning (RL), alternately. Numerical results demonstrate that our proposed scheme is superior to the state-of-the-art RL algorithms.

A Practical Approach to Deploying a Drone-Based Message Ferry in a Disaster Situation

Unmanned aerial vehicles (UAVs) are commonly used in disaster recovery efforts to confirm the extent of the damage caused by a large-scale disaster and to ensure that adequate information about the affected area is gathered. UAV usage in a disaster situation is not limited to information gathering, but its functions further extend to network communication functions such as a communication relay, mobile base station, etc. Conversely, using UAVs as communication relays or base stations (e.g., as a temporary mobile base station) is subject to some limitations such as bad weather and limited energy, which affect the timely collection and delivery of information to and from a disaster area. Therefore, an approach to improve the information collection and delivery in a disaster area while ensuring urgent network restoration is necessary. In this paper, we introduce a practical approach to deploying a drone-based message ferry to improve the timely collection and delivery of information in a disaster situation. Specifically, a locally accessible cloud system (LACS), which we previously developed for urgent local network restoration in a disaster affected area, is attached to a drone to form a message ferry. In order to improve the information collection and delivery, the message ferry is deployed from the local headquarters (i.e., local government office or disaster response coordination center) to carry information to and from a standalone LACS installed at each disaster shelter. We measure the achievable data rate of inter-LACS data transfer (i.e., message ferry and a standalone LACS) through experiment. Thereafter, we estimate and confirm the effective transmission speed to transfer data from the local headquarters to the disaster shelters using average achievable data rate obtained from the experiment in order to extend the LACS services and functions. Our experimental results and analysis suggest that the proposed message ferry is more advantageous to transfer a large amount of data (e.g., 300 MB) at a high speed in the hundred meter to ten kilometer range.

Development of a Multilayer Perceptron Neural Network for Optimal Predictive Modeling in Urban Microcellular Radio Environments

Modern cellular communication networks are already being perturbed by large and steadily increasing mobile subscribers in high demand for better service quality. To constantly and reliably deploy and optimally manage such mobile cellular networks, the radio signal attenuation loss between the path lengths of a base transmitter and the mobile station receiver must be appropriately estimated. Although many log-distance-based linear models for path loss prediction in wireless cellular networks exist, radio frequency planning requires advanced non-linear models for more accurate predictive path loss estimation, particularly for complex microcellular environments. The precision of the conventional models on path loss prediction has been reported in several works, generally ranging from 8–12 dB in terms of Root Mean Square Error (RMSE), which is too high compared to the acceptable error limit between 0 and 6 dB. Toward this end, the need for near-precise machine learning-based path loss prediction models becomes imperative. This work develops a distinctive multi-layer perception (MLP) neural network-based path loss model with well-structured implementation network architecture, empowered with the grid search-based hyperparameter tuning method. The proposed model is designed for optimal path loss approximation between mobile station and base station. The hyperparameters examined include the neuron number, learning rate and hidden layers number. In detail, the developed MLP model prediction accuracy level using different learning and training algorithms with the tuned best values of the hyperparameters have been applied for extensive path loss experimental datasets. The experimental path loss data is acquired via a field drive test conducted over an operational 4G LTE network in an urban microcellular environment. The results were assessed using several first-order statistical performance indicators. The results show that prediction errors of the proposed MLP model compared favourably with measured data and were better than those obtained using conventional log-distance-based path loss models.

Spatial Disparity of Visitors Changes during Particulate Matter Warning Using Big Data Focused on Seoul, Korea.

This study examined the changes in the number of visitors to regions during periods of high particulate matter (PM) concentrations in Seoul and analyzed the regional differences of these changes. Further, it examined the spatial characteristics that affect these regional differences. This study mapped the regional differences by conducting a spatial cluster analysis using GIS and examined factors affecting the regional differences using logistic regression analysis. The visiting population data used in this study were obtained from the Big Data on the de facto population measured every hour at mobile base stations, and all analyses were conducted in terms of weekdays and weekends. The results indicated that the visiting population decreases significantly on weekdays when there are high PM concentrations; however, visits increase on weekends, even during periods of high PM concentrations. Moreover, there was a huge regional gap in visiting population changes. Regions with more commercial use, higher bus accessibility, and better pedestrian environment (pedestrian paths, Walk Score) were more likely to be hotspots, whereas regions with high residential and industrial use were more likely to be cold spots. These results can be used as the basic data for PM policies based on regional characteristics.

A study of risk perception of radiofrequency electromagnetic field (RF-EMF) exposure from mobile phones and base stations in India

ABSTRACT Due to the ever-increasing use of mobile communications in India, health risks associated with exposure to radiofrequency electromagnetic field (RF-EMF) transmitted by mobile phones and base stations has become the focus of research. Internationally, there have been numerous studies on RF-EMF risk perception, but this issue is scarcely addressed in India. The present study evaluates the level of risk perception of Indians associated with RF-EMF exposure from mobile phones and base stations and assesses the underlying reasons for their concerns. We conducted a qualitative study on a small group of Indians (n = 25) from varied socioeconomic backgrounds, followed by a large-scale population (n = 500) survey to identify the primary issues of concern. The results show that many participants, irrespective of their social group, have a positive attitude towards mobile phones and are willing to accept the risks of using them, but are against the installation of base stations. Our study confirms that lack of knowledge about mobile communications technology, health concerns about mobile phones and base stations, community concern regarding base station deployment and media influence have a significant impact on risk perception among Indians. Carefully planned communication techniques are required to decrease risk perceptions and increase acceptance of mobile communications infrastructure.

QoE Optimization for Live Video Streaming in UAV-to-UAV Communications via Deep Reinforcement Learning

A challenge for rescue teams when fighting against wildfire in remote areas is the lack of information, such as the size and images of fire areas. As such, live streaming from Unmanned Aerial Vehicles (UAVs), capturing videos of dynamic fire areas, is crucial for firefighter commanders in any location to monitor the fire situation with quick response. The 5G network is a promising wireless technology to support such scenarios. In this paper, we consider a UAV-to-UAV (U2U) communication scenario, where a UAV at a high altitude acts as a mobile base station (UAV-BS) to stream videos from other flying UAV-users (UAV-UEs) through the uplink. Due to the mobility of the UAV-BS and UAV-UEs, it is important to determine the optimal movements and transmission powers for UAV-BSs and UAV-UEs in real-time, so as to maximize the data rate of video transmission with smoothness and low latency, while mitigating the interference according to the dynamics in fire areas and wireless channel conditions. In this paper, we co-design the video resolution, the movement, and the power control of UAV-BS and UAV-UEs to maximize the Quality of Experience (QoE) of real-time video streaming. We applied the Deep Q-Network (DQN) and Actor-Critic (AC) to maximize the QoE of video transmission from all UAV-UEs to a single UAV-BS to learn the dynamic fire areas and communication environment. Simulation results show the effectiveness of our proposed algorithm in terms of the QoE, delay and video smoothness compared to the Greedy algorithm.

Deep-Learning-Based Stream-Sensing Method for Detecting Asynchronous Multiple Signals

In a disaster site, terrestrial communication infrastructures are often destroyed or malfunctioning, and hence it is very difficult to detect the existence of survivors in the site. At such sites, UAVs are rapidly emerging as an alternative to mobile base stations to establish temporary infrastructure. In this paper, a novel deep-learning-based multi-source detection scheme is proposed for the scenario in which an UAV wants to estimate the number of survivors sending rescue signals within its coverage in a disaster site. For practicality, survivors are assumed to use off-the-shelf smartphones to send rescue signals, and hence the transmitted signals are orthogonal frequency division multiplexing (OFDM)-modulated. Since the line of sight between the UAV and survivors cannot be generally secured, the sensing performance of existing radar techniques significantly deteriorates. Furthermore, we discover that transmitted signals of survivors are unavoidably aysnchronized to each other, and thus existing frequency-domain multi-source classification approaches cannot work. To overcome the limitations of these existing technologies, we propose a lightweight deep-learning-based multi-source detection scheme by carefully designing neural network architecture, input and output signals, and a training method. Extensive numerical simulations show that the proposed scheme outperforms existing methods for various SNRs under the scenario where synchronous and asynchronous transmission is mixed in a received signal. For almost all cases, the precision and recall of the proposed scheme is nearly one, even when users’ signal-to-noise ratios (SNRs) are randomly changing within a certain range. The precision and recall are improved up to 100% compared to existing methods, confirming that the proposal overcomes the limitation of the existing works due to the asynchronicity. Moreover, for Intel(R) Core(TM) i7-6900K CPU, the processing time of our proposal for a case is 31.8 milliseconds. As a result, the proposed scheme provides a robust and reliable detection performance with fast processing time. This proposal can also be applied to any field that needs to detect the number of wireless signals in a scenario where synchronization between wireless signals is not guaranteed.

Energy efficiency-driven mobile base station deployment strategy for shopping malls using modified improved differential evolution algorithm

Energy efficiency-driven mobile base station deployment strategy for shopping malls using modified improved differential evolution algorithm

A Secure Downlink Transmission Scheme for a UAV-Assisted Edge Network

As an extension of centralized cloud services in a space-air-ground integrated network, an unmanned aerial vehicle (UAV-)-enabled edge network brings computation as close ground terminals of need as possible. In this scenario, the UAV is considered a mobile base station (MBS) to achieve data forwarding and processing from cloud servers to terminals. Note that downlink signals from the MBS to ground terminals are vulnerable to passive wiretapping by a malicious node. We formulate an average secrecy rate maximization problem to tackle the wiretapping issue by simultaneously transmitting confidential information and artificial noise (AN). We decompose the problem into two subproblems, including the UAV transmit power allocation subproblem and the UAV trajectory subproblem. Then, we adopt the successive convex approximation scheme and the alternating optimization method to develop our iterative algorithm to achieve secure transmission. Simulation results demonstrate that the proposed scheme is significantly better than other benchmarks in UAV-enabled downlink communication secrecy performance.

A closed-loop control architecture of UAV and WSN for traffic surveillance on highways

Excessive speed is the leading cause of accidents on highways. The use of fixed road-side surveillance systems has proven ineffective because people take caution around only those locations. With more advanced technologies and exceptional maneuvering capabilities, Unmanned Aerial Vehicles (UAVs) is a promising candidate for real-time monitoring of vehicles on the highways. The dependency of recently proposed UAV-based architectures on terrestrial networks suffers from high deployment costs. To address this shortcoming, we propose a unique closed-loop control architecture for traffic surveillance on highways that increases the surveillance effectiveness and adapts according to varying traffic patterns. The proposed scheme is comprised of Wireless Sensor Network (WSN) and UAV, named Collaborative Highway Surveillance (CHS), that works without human intervention and enables the capture of over-speeding drivers on a highway. The WSN provides routing services to the UAV and dynamically guides UAV to the best hotspot to position itself. If any vehicle exceeds the speed limit allowed, the UAV immediately informs this event to the Mobile Base Station (MBS). However, to conserve the energy resources of the WSN, low-level speed violations are forwarded to the MBS only when the UAV gets closer to the MBS. Performance results obtained with our proposed architecture show an increase in surveillance efficiency with an improved response time compared with cooperative and stand-alone unguided UAV networks.

Maximizing Channel Capacity of 3D MIMO System via Antenna Downtilt Angle Adaptation Using a Q-Learning Algorithm

3D MIMO introduces the vertical dimension of the antenna downtilt angle to make the direction of signal transmission more accurate to improve system capacity. In this paper, we verify the effect of antenna downtilt angle on channel capacity through simulations of four fixed antenna downtilt angles, 90, 96, 99, and 102 degrees under the conditions that the distance between mobile station (MS) and base station (BS) is 250 m, and the heights of antenna in BS and MS are 25 m and 1.5 m, respectively. The simulation results show that the antenna downtilt angle of 96 degrees has a larger channel capacity than the others. In addition, we proposed an adaptive optimization method by applying the Q-learning algorithm to adaptively optimize the antenna downtilt angles to maximize system capacity. The performance of the proposed method is to investigate the Q-learning algorithm with three different discount rates at 0.9, 0.5, and 0.1, and four different propagation distances on 20 × 1 and 60 × 4 MIMO. We demonstrate that there is only a 1% difference between the adaptively optimized antenna downtilt angle and the ideal optimal antenna downtilt angle when the discount rate of Q-learning algorithm is 0.9, and its channel capacity performance can reach more than 99.72% of the ideal optimal one.

An Adaptive UAV Deployment Scheme for Emergency Networking

In the areas after natural disaster strikes, the ground communication network can be failed, due to the damage of the communication infrastructure. However, during or after the natural disasters such as earthquakes or tsunamis, ground vehicles may not enter the affected areas easily to set up mobile base stations. Unmanned Aerial Vehicles (UAVs) can be an alternate to provide emergency coverage for ground nodes (GNs). Therefore, how to determine the best location for UAV to achieve the maximum coverage is a key issue. In this paper, an adaptive UAV deployment scheme is proposed to solve the coverage problem of UAV- aided GNs communication. The objective is to optimize the location of the UAV to cover as many GNs as possible and reduce communication energy consumption. We construct a unique analysis method assisted by the collected ground information to solve this problem. First, we propose an information collection method based on the communication probability of Line-of-Sight (LoS) to guarantee the integrity of ground information acquisition. Then, based on the results of the information collection, a virtual obstacle model is built around each GN. Meanwhile, the UAV’s coverage problem is decomposed from the horizontal and vertical dimensions to simplify the difficulty of solving the problem. Finally, the best location of the UAV and the optimal transmission power of GNs can be obtained through the iterative methods and the power control, respectively. The extensive simulation results demonstrate that the proposed deployment scheme outperforms its counterparts.

Monitoring and Analysis of the Current Environmental Situation of Electromagnetic Radiation from 5G Application Base Stations

With the rapid development of the 5G era, the concern of human health risks caused by the construction of mobile base stations has also come to light. To understand the current situation of the electromagnetic radiation environment of 5G application base stations is the basis for avoiding the old road of “pollution before treatment” in environmental management and solving the problem of neighbourhood avoidance. At the beginning of the year, we started to monitor the electromagnetic radiation environment of 5G application base stations in major urban roads, logistics centres, residential areas and university gathering areas, etc. According to the analysis of the monitoring data, the electromagnetic radiation environment levels of 5G application base stations at various monitoring points in urban areas all meet the requirements of the Electromagnetic Environment Control Limits (GB8702-2014). With the development of 5G and the application of various scenarios, it is still necessary to continuously monitor the electromagnetic radiation environment in the future in order to grasp its changing trend.

Particle swarm optimization and artificial bee colony algorithm for clustering and mobile based software-defined wireless sensor networks

With the development of the internet of things, people pay more and more attention to wireless sensor networks. Designing the energy efficient routing is an essential objective for wireless sensor networks. Cluster routing is one of the most popular routing protocols to enhance the network lifetime. However, hotspot problem always exists in cluster-based routing protocol. The task of this study is designing a cluster routing protocol with mobile base station which aims at balancing the energy consumption and prolonging the network lifetime. In this article, we design a particle swarm optimization and artificial bee colony algorithm for clustering and mobile based software-defined wireless sensor networks. The software defined network architecture is used to reduce the energy overhead and computation overhead in sensor nodes. Particle swarm optimization-based cluster routing algorithm is used to calculate the cluster heads and the sojourn locations of base station. Artificial bee colony algorithm-based traversal path algorithm is used to design the move path of the base station. Comparing with relevant protocols, the proposed protocol reduces the energy consumption, enhances the network lifetime and reduces the control overhead.

Physiological changes and symptoms associated with short-term exposure to electromagnetic fields: a randomized crossover provocation study

BackgroundThe biological association between electromagnetic fields (EMF) and idiopathic environmental intolerance attributed to EMF (IEI-EMF) has not been established. To assess the physiological changes and symptoms associated with exposure to EMF, we conducted a randomized crossover provocation study.MethodsWe recruited 58 individuals with IEI-EMF (IEI-EMF group) and 92 individuals without IEI-EMF (control group). In a controlled environment, all participants received EMF signals mimicking those from mobile phone base stations in a randomized sequence under the blinded condition. During the course, participants reported their symptoms and whether they perceived EMF, and we monitored their physiological parameters, including blood pressure (BP), heart rate (HR), and HR variability.ResultsThe IEI-EMF and control groups reported similar frequencies of symptoms during both the provocation and sham sessions. No participant could accurately identify the provocation. In both groups, physiological parameters were similar between the two sessions. The control group, but not the IEI-EMF group, had elevated HR when they perceived EMF exposure.ConclusionsNo symptoms or changes in physiological parameters were found to be associated with short-term exposure to EMF, and no participant could accurately detect the presence of EMF. Moreover, the participants in the control group, but not those in the IEI-EMF group, had elevated HR when they perceived EMF.

Energy-Efficient UAV Movement Control for Fair Communication Coverage: A Deep Reinforcement Learning Approach.

Unmanned Aerial Vehicles (UAVs) are considered an important element in wireless communication networks due to their agility, mobility, and ability to be deployed as mobile base stations (BSs) in the network to improve the communication quality and coverage area. UAVs can be used to provide communication services for ground users in different scenarios, such as transportation systems, disaster situations, emergency cases, and surveillance. However, covering a specific area under a dynamic environment for a long time using UAV technology is quite challenging due to its limited energy resources, short communication range, and flying regulations and rules. Hence, a distributed solution is needed to overcome these limitations and to handle the interactions among UAVs, which leads to a large state space. In this paper, we introduced a novel distributed control solution to place a group of UAVs in the candidate area in order to improve the coverage score with minimum energy consumption and a high fairness value. The new algorithm is called the state-based game with actor–critic (SBG-AC). To simplify the complex interactions in the problem, we model SBG-AC using a state-based potential game. Then, we merge SBG-AC with an actor–critic algorithm to assure the convergence of the model, to control each UAV in a distributed way, and to have learning capabilities in case of dynamic environments. Simulation results show that the SBG-AC outperforms the distributed DRL and the DRL-EC3 in terms of fairness, coverage score, and energy consumption.

A Millimeter-Wave Fabry–Perot Cavity Antenna With Unidirectional Beam Scanning Capability for 5G Applications

In this article, a millimeter-wave (mmW) leaky-wave antenna (LWA) with unidirectional beam scanning (UBS) capability based on the Fabry–Perot cavity (FPC) structure is proposed for fifth-generation (5G) applications. The proposed antenna consists of a main radiating element, a metallic wall, a ground plane, and a single-layer partially reflective surface (PRS). To improve the UBS performance, a single radiating element is designed such that it provides a tilted beam with good radiation performance over the scanning angles. The metallic wall is used close to the radiating element as a reflector to suppress the propagation in undesired directions. A general design guide is given by a theoretical analysis of the FPC structures using the ray-tracing method to formulate the beam-steering functionality versus frequency over desired predetermined angles. The PRS layer is designed without any tuning elements to provide the beam scanning over the angles of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10^\circ &lt; \theta &lt; 45 ^\circ $ </tex-math></inline-formula> and the frequency range of 24–30 GHz. The proposed Fabry–Perot cavity antenna (FPCA) structure is fabricated and the measurement results show a beam steering from 19° to 54° over the frequency band from 24 to 30 GHz, which is in good agreement with the calculated results from the theoretical analysis. A maximum measured gain of 14.5 dBi at 24 GHz is achieved. The proposed antenna can be used in the 5G mobile communication systems and 5G base station antennas (BSAs), where beam steering with high-gain and low fabrication cost features over the mmW spectrum is required.

A Comprehensive Evaluation of Generating a Mobile Traffic Data Scheme without a Coarse-Grained Process Using CSR-GAN

Large-scale mobile traffic data analysis is important for efficiently planning mobile base station deployment plans and public transportation plans. However, the storage costs of preserving mobile traffic data are becoming much higher as traffic increases enormously population density of target areas. To solve this problem, schemes to generate a large amount of mobile traffic data have been proposed. In the state-of-the-art of the schemes, generative adversarial networks (GANs) are used to transform a large amount of traffic data into a coarse-grained representation and generate the original traffic data from the coarse-grained data. However, the scheme still involves a storage cost, since the coarse-grained data must be preserved in order to generate the original traffic data. In this paper, we propose a scheme to generate the mobile traffic data by using conditional-super-resolution GAN (CSR-GAN) without requiring a coarse-grained process. Through experiments using two real traffic data, we assessed the accuracy and the amount of storage data needed. The results show that the proposed scheme, CSR-GAN, can reduce the storage cost by up to 45% compared to the traditional scheme, and can generate the original mobile traffic data with 94% accuracy. We also conducted experiments by changing the architecture of CSR-GAN, and the results show an optimal relationship between the amount of traffic data and the model size.

Overview of Energy Consumption Optimization in Mobile Edge Computing

The energy consumption of traditional mobile network is mainly reflected by task calculation and data transmission. The edge server in mobile edge computing is placed at the edge of the network closer to the mobile terminal device. When the computing resources of the mobile terminal device are insufficient, the task can be offloaded to the edge server for processing. This paper discusses the energy consumption optimization in the Mobile Edge Computing (MEC) system, which is composed of the edge server and mobile devices at the edge of the network. Firstly, the system model of MEC is elaborated. Secondly, the energy consumption optimization for mobile devices and base stations is studied. Which oriented to the mobile device energy consumption optimization is divided into multiple users - single MEC server scenarios of energy consumption optimization and multiple users - the energy optimization of MEC server scenario, and enumerate the oriented to the base station and a new method for the optimization of energy consumption for mobile equipment energy consumption optimization method, in addition to the insufficiencies of these methods and the prospect of the future are also explained.