Wireless Local Area Networks (WLANs) are commonly deployed in various environments. The WLAN data packets are not transmitted continuously but often worst-case exposure of WLAN is assessed, assuming 100 % activity and leading to huge overestimations. Actual duty cycles of WLAN are thus of importance for time-averaging of exposure when checking compliance with international guidelines on limiting adverse health effects. In this paper, duty cycles of WLAN using Wi-Fi technology are determined for exposure assessment on large scale at 179 locations for different environments and activities (file transfer, video streaming, audio, surfing on the internet, etc.). The median duty cycle equals 1.4 % and the 95th percentile is 10.4 % (standard deviation SD = 6.4%). Largest duty cycles are observed in urban and industrial environments. For actual applications, the theoretical upper limit for the WLAN duty cycle is 69.8% and 94.7% for maximum and minimum physical data rate, respectively. For lower data rates, higher duty cycles will occur. Although counterintuitive at first sight, poor WLAN connections result in higher possible exposures. File transfer at maximum data rate results in median duty cycles of 47.6% (SD = 16%), while it results in median values of 91.5% (SD = 18%) at minimum data rate. Surfing and audio streaming are less intensively using the wireless medium and therefore have median duty cycles lower than 3.2% (SD = 0.5-7.5%). For a specific example, overestimations up to a factor 8 for electric fields occur, when considering 100 % activity compared to realistic duty cycles. We have included the document "Response to reviewers" at the end of the revised manuscript. This is a detailed summary of the changes made in preparing the revised manuscript. We have put an asterisk before our answers and changes. This document contains the detailed summary of the changes made in preparing the revised manuscript. We hope the changes we made according to your suggestions (explanation unit of time, multiple clients, far vs. near exposure vs. duty cycle, etc.) will satisfy you. We have put an asterisk before our answers and changes. We thank you for spending your time reviewing our paper. *We have put an asterisk (*) before every response. Reviewers' comments:Reviewer #1: This is a well written and interesting paper, reporting methods to calculate the duty cycle for WLAN transmitters under different real time scenarios. The data reported will be very useful in realistic assessment of the exposure of people to WLAN sources. *Thank you for appreciating our paper.Here are some comments:1-The main comment is related to the unit of time (relevant time frame) for duty cycle calculations. In Khalid et al 2011, the unit of time was considered as the duration of a typical lesson in a school ( about 30min). However the authors here considered the duration of a file transfer for example ( or any other activity), as the unit of time. This needs to be elaborated further as whether the time to transfer a file (which is very sma...

Over the last years, the ever-growing wireless traffic has pushed the mobile community to investigate solutions that can assist in more efficient management of the wireless spectrum. Towards this direction, the long-term evolution (LTE) operation in the unlicensed spectrum has been proposed. Targeting a global solution that respects the regional requirements, 3GPP announced the standard of LTE licensed assisted access (LAA). However, LTE LAA may result in unfair coexistence with Wi-Fi, especially when Wi-Fi does not use frame aggregation. Targeting a technique that enables fair channel access, the mLTE-U scheme has been proposed. According to mLTE-U, LTE uses a variable transmission opportunity, followed by a variable muting period that can be exploited by other networks to transmit. For the selection of the appropriate mLTE-U configuration, information about the dynamically changing wireless environment is required. To this end, this paper proposes a convolutional neural network (CNN) that is trained to perform identification of LTE and Wi-Fi transmissions. In addition, it can identify the hidden terminal effect caused by multiple LTE transmissions, multiple Wi-Fi transmissions, or concurrent LTE and Wi-Fi transmissions. The designed CNN has been trained and validated using commercial off-the-shelf LTE and Wi-Fi hardware equipment and for two wireless signal representations, namely, in-phase and quadrature samples and frequency domain representation through fast Fourier transform. The classification accuracy of the two resulting CNNs is tested for different signal to noise ratio values. The experimentation results show that the data representation affects the accuracy of CNN. The obtained information from CNN can be exploited by the mLTE-U scheme in order to provide fair coexistence between the two wireless technologies.

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In the upcoming decade and beyond, the Cooperative, Connected and Automated Mobility (CCAM) initiative will play a huge role in increasing road safety, traffic efficiency and comfort of driving in Europe. While several individual vehicular wireless communication technologies exist, there is still a lack of real flexible and modular platforms that can support the need for hybrid communication. In this paper, we propose a novel vehicular communication management framework (CAMINO), which incorporates flexible support for both short-range direct and long-range cellular technologies and offers built-in Cooperative Intelligent Transport Systems’ (C-ITS) services for experimental validation in real-life settings. Moreover, integration with vehicle and infrastructure sensors/actuators and external services is enabled using a Distributed Uniform Streaming (DUST) framework. The framework is implemented and evaluated in the Smart Highway test site for two targeted use cases, proofing the functional operation in realistic environments. The flexibility and the modular architecture of the hybrid CAMINO framework offers valuable research potential in the field of vehicular communications and CCAM services and can enable cross-technology vehicular connectivity.

In the railway industry, there are nowadays different actors who would like to send or receive data from the wayside to an onboard device or vice versa. These actors are e.g., the Train Operation Company, the Train Constructing Company, a Content Provider, etc. This requires a communication module on each train and at the wayside. These modules interact with each other over heterogeneous wireless links. This system is referred to as the Train-to-Wayside Communication System (TWCS). While there are already a lot of deployments using a TWCS, the implementation of quality of service, performance enhancing proxies (PEP) and the network mobility functions have not yet been fully integrated in TWCS systems. Therefore, we propose a novel and modular IPv6-enabled TWCS architecture in this article. It jointly tackles these functions and considers their mutual dependencies and relationships. DiffServ is used to differentiate between service classes and priorities. Virtual local area networks are used to differentiate between different service level agreements. In the PEP, we propose to use a distributed TCP accelerator to optimize bandwidth usage. Concerning network mobility, we propose to use the SCTP protocol (with Dynamic Address Reconfiguration and PR-SCTP extensions) to create a tunnel per wireless link, in order to support the reliable transmission of data between the accelerators. We have analyzed different design choices, pinpointed the main implementation challenges and identified candidate solutions for the different modules in the TWCS system. As such, we present an elaborated framework that can be used for prototyping a fully featured TWCS.