Base Station Research Articles

Joint path planning and power allocation of a cellular-connected UAV using apprenticeship learning via deep inverse reinforcement learning

This paper investigates an interference-aware joint path planning and power allocation mechanism for a cellular-connected unmanned aerial vehicle (UAV) in a sparse suburban environment. The UAV’s goal is to fly from an initial point and reach a destination point by moving along the cells to guarantee the required quality of service (QoS). In particular, the UAV aims to maximize its uplink throughput and minimize interference to the ground user equipment (UEs) connected to neighboring cellular base stations (BSs), considering both the shortest path and limitations on flight resources. Expert knowledge is used to experience the scenario and define the desired behavior for the sake of the agent (i.e., UAV) training. To solve the problem, an apprenticeship learning method is utilized via inverse reinforcement learning (IRL) based on both Q-learning and deep reinforcement learning (DRL). The performance of this method is compared to learning from a demonstration technique called behavioral cloning (BC) using a supervised learning approach. Simulation and numerical results show that the proposed approach can achieve expert-level performance. We also demonstrate that, unlike the BC technique, the performance of our proposed approach does not degrade in unseen situations.

RL-based mobile edge computing scheme for high reliability low latency services in UAV-aided IIoT networks

The prevailing adoption of Internet of Things paradigm is giving rise to a wide range of use cases in various vertical industries including remote health, industrial automation, and smart agriculture. However, the realization of such use cases is mainly challenged due to their stringent service requirements of high reliability and low latency. This challenge grows further when the service entails processing collected data for informed decision making. In this work, we consider a field of industrial Internet of Things devices that generate computational tasks and are covered by a nearby base station equipped with an edge server. The edge server offers fast processing to the devices’ tasks to help in meeting their latency requirement. Due to statistical wireless variability, the task data may not be correctly delivered in time for processing. To this end, we utilize an unmanned aerial vehicle as a supplemental edge server that tailors its trajectory and flies closer to the IIoT devices to ensure a highly reliable task delivery based on the given task reliability constraints. We formulate the problem as a Markov Decision Process, and propose a deep reinforcement learning-based approach using proximal policy optimization to optimize the unmanned aerial vehicle trajectory and scheduling devices to offload their data for processing. We present simulation results for various system scenarios to illustrate the effectiveness of the proposed solution as compared to several baseline approaches.

Performance analysis of joint transmission in TDD-based mm-wave networks with BS heights

Dynamic-time division duplexing (D-TDD) is used in millimeter-wave (mm-wave) networks to allocate time-frequency resources for both downlink (DL) and uplink (UL) transmissions. This scheduling mechanism adapts to varying data traffic to meet the different quality of service (QoS) requirements of heterogeneous services. However, blockage is the major bottleneck to millimeter wave (mm-Wave) networks since it causes signal outages. Moreover, the dense deployment of base stations (BSs) to reduce the signal outage problem may lead to severe interference due to solid line-of-sight (LoS) BSs. To solve the problem of less coverage, this paper utilizes a joint transmission mechanism in the DL of D-TDD-based mm-Wave networks. Further, examining the network performance also entails considering the elevation disparity between the Base Station (BS) and User Equipment (UE). The primary focus of this work is to study the effects of joint transmission in D-TDD-based DL mm-Wave networks with different BS heights and directional beamforming using the stochastic geometry framework. For D-TDD with Joint Transmission, numerical evaluations indicate that the SINR coverage probability reaches approximately 0.7 when the coverage radius (rc) is 100 m. Conversely, at a coverage radius of 200 m, the SINR coverage probability drops to around 0.25. The simulation results demonstrate using MATLAB 2019b tool, that D-TDD-based mm-Wave networks achieve superior coverage using the joint transmission scheme compared to single BS association. Besides, results show that optimal UL and DL split and load balancing are required to achieve better DL coverage.

Design of a 240 elements antenna system for 5G massive MIMO applications with an inclined beam and two operating modes

A massive MIMO (multiple-input-multiple-output) antenna system for 5G base stations is developed, featuring 240 elements and 60 ports. The system supports two modes: individual port operation and network operation. It has a rectangular shape with dimensions of 28×16.8×0.32 cm³ and consists of three layers per side, with 15 ports per layer. Each port contains a 2×2 patch sub-array operating at 3.5 GHz, with beam tilting to generate uncorrelated radiation patterns. The system provides a measured bandwidth of 252 MHz, covering the 3.39–3.64 GHz band. Gain and envelope correlation coefficients are evaluated according to mMIMO parameters. A prototype is fabricated, and the results obtained through CST simulations are validated by experimental measurements.

Enhancing Transmission Efficiency in Non-IID Federated Learning by Integrating Quantization into an Existing Trustworthiness Model

Federated Learning (FL) is a distributed machine learning paradigm that enables multiple clients to collaboratively train a model without sharing their data, thus preserving privacy. In this approach, data is not transmitted between the client and the base station. Instead, a model is sent to each device, where it is trained locally, and then retransmitted back to the base station. Each iteration of this process is known as a communication round. However, FL faces challenges, especially when data is not independently and identically distributed (Non-IID). Non-IID data means that the data across clients can vary significantly in distribution, leading to situations where certain classes or features are overrepresented in some clients and underrepresented in others. This lack of uniformity quickly leads to biased model updates and reduced performance. To address this, previous studies have introduced trustworthiness metrics to ensure more reliable model aggregation, minimizing accuracy losses associated with Non-IID data. Another significant challenge is the high transmission load, as model updates between clients and the base station are resource-intensive. Model parameters are transmitted between clients and base stations, which can strain communication channels and slow down the entire process, especially when dealing with larger models and datasets. This communication overhead is a bottleneck that limits the scalability of FL, particularly in resource-constrained environments. Our research addresses these challenges by integrating quantization into the trustworthiness model specifically in a Non-IID scenario. Quantization reduces the precision of model parameters to minimize data transmission, resulting in more efficient communication. We applied different levels of quantization intensity to a model trained on the CIFAR-10 dataset and found that certain methods can significantly reduce transmission overhead without substantial accuracy loss. Our findings suggest that combining quantization with trustworthiness metrics can significantly enhance the efficiency and potentially improve the adoption of Federated Learning in resource-constrained environments.

Implementing Autonomous Control in the Digital-Twins-Based Internet of Robotic Things for Remote Patient Monitoring.

Conventional patient monitoring methods require skin-to-skin contact, continuous observation, and long working shifts, causing physical and mental stress for medical professionals. Remote patient monitoring (RPM) assists healthcare workers in monitoring patients distantly using various wearable sensors, reducing stress and infection risk. RPM can be enabled by using the Digital Twins (DTs)-based Internet of Robotic Things (IoRT) that merges robotics with the Internet of Things (IoT) and creates a virtual twin (VT) that acquires sensor data from the physical twin (PT) during operation to reflect its behavior. However, manual navigation of PT causes cognitive fatigue for the operator, affecting trust dynamics, satisfaction, and task performance. Also, operating manual systems requires proper training and long-term experience. This research implements autonomous control in the DTs-based IoRT to remotely monitor patients with chronic or contagious diseases. This work extends our previous paper that required the user to manually operate the PT using its VT to collect patient data for medical inspection. The proposed decision-making algorithm enables the PT to autonomously navigate towards the patient's room, collect and transmit health data, and return to the base station while avoiding various obstacles. Rather than manually navigating, the medical personnel direct the PT to a specific target position using the Menu buttons. The medical staff can monitor the PT and the received sensor information in the pre-built virtual environment (VE). Based on the operator's preference, manual control of the PT is also achievable. The experimental outcomes and comparative analysis verify the efficiency of the proposed system.

Performance analysis of shared relay CR-NOMA network based on SWIPT

With the development of wireless communication technology, the number of devices accessing the communication network is increasing. This paper addresses critical issues such as low spectrum resource utilization and the energy constraints of devices. The investigation focuses on the system performance of the shared relay Cognitive Radio Non-Orthogonal Multiple Access (CR-NOMA) network based on Simultaneous Wireless Information and Power Transfer (SWIPT) network model. Unlike the existing CR-NOMA model in which the secondary network user do not participate in the transmission of information from the primary network, we consider the secondary network near user as shared relay. The shared relay utilizes SWIPT technology to harvest energy using a nonlinear energy harvesting model. Additionally, the shared relay assists in transmitting information from the primary network base station to primary user, as well as from the secondary network base station to far user of the secondary network. Subsequently, we conduct a series of simulations to analyze the effects of Signal-to-Noise Ratio (SNR), power distribution factor, interference threshold, and time-switching (TS) factor on system performance. Furthermore, we compare and analyze the performance of our proposed network model against CR-NOMA network across three dimensions: outage probability, throughput, and energy efficiency. Our results demonstrate that the proposed network model exhibits superior outage performance and enhances user throughput compared to the CR-NOMA network. Additionally, it demonstrates improved energy efficiency compared to the shared relay CR-NOMA network, leading to an overall improvement in network performance.

Deep Learning-Based Joint Beamforming Design for Multi-Hop Reconfigurable Intelligent Surface (RIS)-Aided Communication Systems

Reconfigurable intelligent surface (RIS) is one of the promising technologies for sixth generation communications due to its advantages including energy saving, high spectral efficiency, etc. However, the non-convex joint beamforming design is a challenge, especially in the multi-hop RIS-assisted communication system. This paper proposes a deep learning-based joint beamforming (DLBF) design, aiming to maximize the system data rate for multi-hop RIS-aided communication systems. The proposed DLBF design consists of the reflection matrices design of all RISs and the transmit beamforming design at the base station, which has a reduced computational complexity. Numerical results show that the proposed DLBF can achieve 1.8 bit/s/Hz sum rate gain compared to the conventional beamforming method for the two-user scenario, which can be enhanced by large-scale users. The sum rate performance can be improved by increasing the number of RISs due to the reflection gain, and corresponding results provide a guidance of the multi-hop number selection for further investigation.

3D indoor area recognition for personnel security using integrated UWB and barometer approach

Real-time location tracking is essential for personal security in industrial scenes, e.g. monitoring worker safety in power substations. Ultra wideband (UWB) technology is suitable for indoor positioning thanks to its high penetration capability, high ranging accuracy and low power consumption. However, UWB based trilateration positioning requires high workload for field deployment of base stations. Indoor complex topology results in multipath and non-line of sight (NLOS) conditions of UWB signals, and degrades the positioning performance in terms of accuracy and reliability. This paper proposes a three-dimensional (3D) area recognition solution by integrating UWB time of flight (TOF) ranging and barometer measurements. The proposed solution utilizes a multi-tier distributed joint probabilistic inference model, which accomplishes the indoor 3D area recognition exploiting multiple clustering and prediction algorithms of machine learning. The field experiments showed that the proposed method can achieve an accuracy of 3D area recognition of more than 99.2%. The proposed method improves the computing efficiency by 93%. The errors of improved differential barometric height estimation method are less than 1 m, which means a success rate of 100% for floor identification, given a floor separation of 3–4 m. The proposed solution is suitable for personnel security applications of industrial scenes, which requires reliable real-time area information rather than just coordinates.

Dependency-aware online task offloading based on deep reinforcement learning for IoV

The convergence of artificial intelligence and in-vehicle wireless communication technologies, promises to fulfill the pressing communication needs of the Internet of Vehicles (IoV) while promoting the development of vehicle applications. However, making real-time dependency-aware task offloading decisions is difficult due to the high mobility of vehicles and the dynamic nature of the network environment. This leads to additional application computation time and energy consumption, increasing the risk of offloading failures for computationally intensive and latency-sensitive applications. In this paper, an offloading strategy for vehicle applications that jointly considers latency and energy consumption in the base station cooperative computing model is proposed. Firstly, we establish a collaborative offloading model involving multiple vehicles, multiple base stations, and multiple edge servers. Transferring vehicular applications to the application queue of edge servers and prioritizing them based on their completion deadlines. Secondly, each vehicular application is modeled as a directed acyclic graph (DAG) task with data dependency relationships. Subsequently, we propose a task offloading method based on task dependency awareness in deep reinforcement learning (DAG-DQN). Tasks are assigned to edge servers at different base stations, and edge servers collaborate to process tasks, minimizing vehicle application completion time and reducing edge server energy consumption. Finally, simulation results show that compared with the heuristic method, our proposed DAG-DQN method reduces task completion time by 16%, reduces system energy consumption by 19%, and improves decision-making efficiency by 70%.

Energy-Efficient Algorithm for Data Path Selection in High-Density Wireless Sensor Networks

Relevance. Increasing the number of sensor devices per unit area consequently reduces the physical distance between the devices in the sensor network. Such networks are usually deployed over a large area and the sensor device that wants to transmit a data packet is located far away from the base station. In such a case, the source device is challenged to choose a transmission path that consumes the least amount of energy resources and satisfies the delivery time requirements. The objective of this study is to develop and validate the effectiveness of an empirical algorithm for selecting a transmission path that reduces the energy consumption of high-density wireless sensor networks. Methods of system analysis, analytical modeling, geometry and probability theories are used. Solution. It is assumed that the sensor network is deployed in a limited area and is a set of devices that are connected to each other informationally and energetically. When building data transmission routes, any sensor devices can be used as repeaters. At the same time, the increase in the number of repeaters leads to an increase in the time of data delivery. Novelty. It is assumed that the sensor network is deployed in a limited area and is a set of devices that are connected to each other informationally and energetically. Any sensor devices may be used as repeaters when constructing data transmission routes.Significance (theoretical). Dependences of power consumption level on various system parameters affecting the processes of functioning of high-density wireless sensor networks have been obtained. Significance (practical). The proposed empirical algorithm for selecting a rational data transmission route in a wireless sensor network allows us to determine, among all alternatives, the route to the coordinator that requires the least power. The effectiveness of the proposed empirical power-saving algorithm is confirmed by simulation modeling.

A set of Models for Device Positioning in Sixth Generation Networks. Part 1. Methods Survey and Problem Statement

Relevance. Today, terahertz radio systems are considered as a technological basis for integrating methods and means of radio communication and radar in promising sixth-generation networks. If in 4G LTE networks the capabilities of positioning user equipment using the infrastructure of base stations were considered as auxiliary options, then in 5G NR networks, location determination technologies (LDTs) have become full-fledged services, the requirements for which are specified along with communication services. A new trend in positioning in 5G NR networks, compared to 4G LTE networks, has become a single-position assessment of the coordinates and orientation of the user equipment based on signals from a single base station with the ability to distinguish between direct and reflected signals. 6G networks are still in their infancy, but it can already be stated that they mark the next stage in the evolution of digital ecosystems, which is characterized by the convergence of communication technologies, localization and sensing of radio air and the surrounding space by radio engineering means.Purpose. This work opens a research cycle devoted to the review of models, methods and algorithms for positioning devices in 6G networks. The goal of the cycle is to find and justify new radio engineering means for achieving decimeter accuracy in 6G device coordinate estimates. The first part of the cycle provides an overview of the methods and formalization of the model for collecting primary measurements.Method is an analytical review of the state of the problem based on current scientific publications, conceptual modeling, categorical approach, expert combination, comparative analysis, formalization, mathematical and simulation modeling.Results. As a result of the review of device positioning methods during the transition to 6G networks, key performance indicators and LDT scenarios are updated. As a result of the comparative analysis of 5G and 6G networks, new factors, advantages and disadvantages of positioning technologies during the transition from millimeter wave networks to terahertz networks are systematized. A formalized mathematical model for collecting primary measurements is used in the simulation model for assessing the accuracy of device positioning in the second part of the cycle.Novelty. This cycle is the first such study in the Russian scientific segment on network positioning of the sixth generation of the terahertz range, in which the author's version provides an overview of methods and a systematization of a set of new factors of the OMP in communication networks.The theoretical significance of the review-analysis lies in the establishment of both technological obstacles and new opportunities for increasing positioning accuracy during the transition to 6G networks.The practical significance of the formalized mathematical model lies in its subsequent software implementation for numerical justification of the limits of positioning accuracy in 6G networks.

Hybrid Control Strategy for 5G Base Station Virtual Battery-Assisted Power Grid Peak Shaving

With the rapid development of the digital new infrastructure industry, the energy demand for communication base stations in smart grid systems is escalating daily. The country is vigorously promoting the communication energy storage industry. However, the energy storage capacity of base stations is limited and widely distributed, making it difficult to effectively participate in power grid auxiliary services by only implementing the centralized control of base stations. Aiming at this issue, an interactive hybrid control mode between energy storage and the power system under the base station sleep control strategy is delved into in this paper. Grounded in the spatiotemporal traits of chemical energy storage and thermal energy storage, a virtual battery model for base stations is established and the scheduling potential of battery clusters in multiple scenarios is explored. Then, based on the time of use electricity price and user fitness indicators, with the maximum transmission signal and minimum operating cost as objective functions, a decentralized control device is used to locally and quickly regulate the communication system. Furthermore, a multi-objective joint peak shaving model for base stations is established, centrally controlling the energy storage system of the base station through a virtual battery management system. Finally, a simulation analysis was conducted on data from different types of base stations in the region, designing two distinct scheduling schemes for four regional categories. The analysis results demonstrate that the proposed model can effectively reduce the power consumption of base stations while mitigating the fluctuation of the power grid load.

THz band drone communications with practical antennas: Performance under realistic mobility and misalignment scenarios

For 6G non-terrestrial communications, drones will offer uninterrupted connectivity for surveillance, sensing, and localization. They will also serve as drone base stations to support terrestrial base stations, providing large bandwidth, high-rate, and ultra-reliable low latency services. In this paper, for the first time in the literature, we depict the true performance of Terahertz (THz) band communications among drones by applying various channel selection and power allocation schemes with practical THz antennas within (0.75–4.4) THz under realistic mobility and misalignment scenarios. Through numerical simulations, we unveil the capacity of drone links under different channel selection and power allocation schemes within 10s to 100s of Gbps at distances (1–100) m, when drones are in motion and subject to (mis)alignment due to mobility and even under beam misalignment fading. However, when exposed to real drone mobility traces, the performance of all channel selection schemes drops significantly, sometimes by up to six orders of magnitude, due to the occasional reverse orientations of antennas. In addition to the capacity analysis, we report available frequency bands (transmission windows) considering all schemes and mobility patterns. We also identify a band that is commonly available under all considered mobility and misalignment settings, and we evaluate its performance in terms of spectral and energy efficiencies, which can be useful in designing THz transceivers for drone communications. Our findings emphasize the essence of active beam control solutions to achieve the desired capacity potential of THz drone communications, while also highlighting the challenges of utilizing the THz band for drone communications.

Automated PIM Detection in Wireless Networks via Smart Data-Driven Techniques

As the number of base stations keeps increasing due to ever-growing wireless services and pervasive connectivity, the operational costs for network service providers rises proportionally. In addition, with the evolution of wireless technologies and the development of next generation networks such as 5G and 6G, network operators are inevitably expected to encounter more complex challenges. One such network issue is the Passive Intermodulation (PIM) problem that is observed in both 4G and 5G networks. It degrades the user experience and radio resource efficiency while leading to an operational overhead for detecting and mitigating it on the operator side. Although there is a significant body of work regarding PIM detection and cancellation methods, the majority of such studies depends on hardware solutions and manual investigation by network engineers, which is costly in terms of time and labor. In this paper, we propose two methods using unsupervised and semi-supervised Machine Learning (ML) approaches, namely a time-series-based anomaly detection technique and an autoencoder-based one, for identifying PIM problems in network sites. The proposed solutions utilize a set of Key Performance Indicator (KPI) data of base stations obtained from network management systems for a significantly long time interval and detect possible PIM problems without the need for a human in the loop. We measure and analyze the performance of our solutions, with the guidance of experienced network engineers, on our collected dataset.

Tangle- and contact-free path planning for a tethered mobile robot using deep reinforcement learning.

This paper presents a tangle- and contact-free path planning (TCFPP) for a mobile robot attached to a base station with a finite-length cable. This type of robot, called a tethered mobile robot, can endure long-time exploration with a continuous power supply and stable communication via its cable. However, the robot faces potential hazards that endanger its operation such as cable snagging on and cable entanglement with obstacles and the robot. To address these challenges, our approach incorporates homotopy-aware path planning into deep reinforcement learning. The proposed reward design in the learning problem penalizes the cable-obstacle and cable-robot contacts and encourages the robot to follow the homotopy-aware path toward a goal. We consider two distinct scenarios for the initial cable configuration: 1) the robot pulls the cable sequentially from the base while heading for the goal, and 2) the robot moves to the goal starting from a state where the cable has already been partially deployed. The proposed method is compared with naive approaches in terms of contact avoidance and path similarity. Simulation results revealed that the robot can successfully find a contact-minimized path under the guidance of the reference path in both scenarios.

Revolutionizing WBAN communication for public health: Effective Optimization strategy-driven health assessment

This study explores the optimization procedures that are critical to enhancing WBAN communication efficacy in an environment of public health initiatives. In order to observe someone's physiological characteristics continuously and non-invasively, wireless body area networks (WBANs), are an essential piece of innovation for distant health observation. The WBAN individual nodes, yet, might relocate and lose reception from a distant base station over time. Thus, an effective inter-WBAN interaction approach is required to address this issue. In order to lower cost associated with networks and maximize cluster lifespan towards the network's effectiveness, this research develops a clustering-driven routing approach that uses cluster head serving as a gateway. For efficient cluster head selection, a novel customized chicken swarm optimization (CCSO) technique was introduced. Various parameter adjustments are made in multiple trials to verify the suggested approach effectiveness. The research conduct comprehensive comparison analysis of suggested and existing algorithms, several tests are conducted. The results show that the approach technique can increase the effectiveness of WBAN engagement and create clusters more quickly than the other methods in the framework of public health initiatives.

Pathway decisions for reuse and recycling of retired lithium-ion batteries considering economic and environmental functions

Reuse and recycling of retired electric vehicle (EV) batteries offer a sustainable waste management approach but face decision-making challenges. Based on the process-based life cycle assessment method, we present a strategy to optimize pathways of retired battery treatments economically and environmentally. The strategy is applied to various reuse scenarios with capacity configurations, including energy storage systems, communication base stations, and low-speed vehicles. Hydrometallurgical, pyrometallurgical, and direct recycling considering battery residual values are evaluated at the end-of-life stage. For the optimized pathway, lithium iron phosphate (LFP) batteries improve profits by 58% and reduce emissions by 18% compared to hydrometallurgical recycling without reuse. Lithium nickel manganese cobalt oxide (NMC) batteries boost profit by 19% and reduce emissions by 18%. Despite NMC batteries exhibiting higher immediate recycling returns, LFP batteries provide superior long-term benefits through reuse before recycling. Our strategy features an accessible evaluation framework for pinpointing optimal pathways of retired EV batteries.

Fake Base Station Detection and Link Routing Defense

Fake base stations comprise a critical security issue in mobile networking. A fake base station exploits vulnerabilities in the broadcast message announcing a base station’s presence, which is called SIB1 in 4G LTE and 5G NR, to get user equipment to connect to the fake base station. Once connected, the fake base station can deprive the user of connectivity and access to the Internet/cloud. We discovered that a fake base station can disable the victim user equipment’s connectivity for an indefinite period of time, which we validated using our threat prototype against current 4G/5G practices. We designed and built a defense scheme which detects and blacklists a fake base station and then, informed by the detection, avoids it through link routing for connectivity availability. For detection and blacklisting, our scheme uses the real-time information of both the time duration and the number of request transmissions, the features of which are directly impacted by the fake base station’s threat and which have not been studied in previous research. Upon detection, our scheme takes an active measure called link routing, which is a novel concept in mobile/4G/5G networking, where the user equipment routes the connectivity request to another base station. To defend against a Sybil-capable fake base station, we use a history–reputation-based link routing scheme for routing and base station selection. We implemented both the base station and the user on software-defined radios using open-source 5G software (srsRAN v23.10 and Open5GS v2.6.6) for validation. We varied the base station implementation to simulate legitimate vs. faulty but legitimate vs. fake and malicious base stations, where a faulty base station notifies the user of the connectivity disruption and releases the session, while a fake base station continues to hold the session. We empirically analyzed the detection and identification thresholds, which vary with the fake base station’s power and the channel condition. By strategically selecting the threshold parameters, our scheme provides zero errors, including zero false positives, to avoid blacklisting a temporarily faulty base station that cannot provide connectivity at the time. Furthermore, our link routing scheme enables the base station to switch in order to restore the connectivity availability and limit the threat impact. We also discuss future directions to facilitate and encourage R&D in securing telecommunications and base station security.

Affective evaluation and exposure perception of everyday mobile phone usage situations.

To understand citizens' reactions to the 5G rollout, their affective reaction and perception of radiofrequency electromagnetic fields (RF-EMF) exposure are of interest. Although precursor studies on 2G-4G have investigated exposure perception mostly quantitatively, the present study applied a qualitative exploratory approach. A number of 35 individual interviews and 6 focus groups with the same participants were conducted in December 2022. Participants were recruited from several locations in Germany, where 5G rollout was at different stages. Interactive tasks, particularly an affective evaluation task and a ranking task, encouraged participants to consider their affect regarding mobile communications and their exposure perception. This approach allowed the participants to first engage with the topic of mobile communications/5G in an intuitive way, before talking about their specific beliefs on RF-EMF exposure. Several pictures showing a person (1) interacting with a mobile phone, (2) surrounded by other peoples' mobile phones, or (3) in the vicinity of mobile phone base stations (antennas) were used as stimulus materials. Data were analyzed using an exploratory content analysis. In the affective evaluation task participants revealed more negative associations with base stations than with mobile phones. The analysis showed that the reasons for their evaluation were very diverse, whereby exposure to RF-EMF only played a subordinate role. Further, the ranking task indicated that most participants (n=20) felt more exposed from base stations than from mobile devices. Results are mostly in-line with the literature on 2G-4G and do not indicate a substantially different exposure perception for 5G.