Towards Reproducible Wireless Experiments Using R2lab

Reproducibility is key in designing wireless systems and evaluating their performance. Trying to reproduce wireless experimentsallowed us to identify some pitfalls and possible ways to simplify the complex task of avoiding them. In this paper, we expose a fewconsiderations that we learned are instrumental for ensuring the reproducibility of wireless experiments. Œen we describe the stepswe have taken to make our experiments easy to reproduce. We speci€cally address issues related to wireless hardware, as well asvarying propagation channel conditions. We show that extensive knowledge of the used hardware and of its design is required toguarantee that the inner state of the system has no negative impact on performance evaluation and experimental results. As for variabilityof channel conditions, we make the case that a special setup or testbed is necessary so that one can control the ambient wireless propagation environment, using for instance, an anechoic chamber like R2lab

A New Downlink Scheduling Algorithm Proposed for Real Time Traffic in LTE System

The Third Generation Partnership Project (3GPP) has developed a new cellular standard based packet switching allowing high data rate, 100 Mbps in Downlink and 50 Mbps in Uplink, and having the flexibility to be used in different bandwidths ranging from 1.4 MHz up to 20 MHz, this standard is termed LTE (Long Term Evolution). Radio Resource Management (RRM) procedure is one of the key design roles for improving LTE system performance, Packet scheduling is one of the RRM mechanisms and it is responsible for radio resources allocation, However, Scheduling algorithms are not defined in 3GPP specifications. Therefore, it gets a track interests for researchers. In this paper we proposed a new LTE scheduling algorithm and we compared its performances with other well known algorithms such as Proportional Fairness (PF), Modified Largest Weighted Delay First (MLWDF), and Exponential Proportional Fairness (EXPPF) in downlink direction. The simulation results shows that the proposed scheduler satisfies the quality of service (QoS) requirements of the real-time traffic in terms of packet loss ratio (PLR), average throughput and packet delay. This paper also discusses the key issues of scheduling algorithms to be considered in future traffic requirements

A step towards runnable papers using R2lab

In this paper, we present R2lab, an open, electromagnetically insulated research testbed dedicated to wireless networking. We describe the hardware capabilities currently available in terms of Software Defined Radio, and the software suite made available to deploy experiments. Taking as a pretext a dummy experiment, we show how it all fits into a notebook-based approach to getting closer to runnable papers

Deploy a 5G network in less than 5 minutes: Demo Abstract

International audienceWe describe a demonstration run on R2lab, an anechoic chamber located at Inria Sophia Antipolis, France. e demonstration consists in deploying a standalone 5G network in less than 5 minutes. All the network components (base station, subscriber management, serving and packet gateways, network traac analyzers) were run automatically using the nepi-ng experiment orchestration tool. Download and upload performance to the Internet from a commercial phone located in the anechoic chamber are shown

nepi-ng: an efficient experiment control tool in R2lab

International audienceExperimentation is an essential step for realistic evaluation of wireless network protocols. The evaluation methodology entails controllable environment conditions and a rigorous and efficient experiment control and orchestration for a variety of scenarios. Existing experiment control tools such as OMF often lack in efficiency in terms of resource management and rely on abstractions that hide the details about the wireless setup. In this paper, we propose nepi-ng, an efficient experiment control tool that leverages job oriented programming model and efficient single-thread execution of parallel programs using asyncio. nepi-ng provides an efficient and modular fine grain synchronization mechanism for networking experiments with light software dependency footprint. We present and discuss our design choices and compare to the state of the art tools, mainly OMF

R2Lab Testbed Evaluation for Wireless Mesh Network Experiments

International audienceWe have provided critical evaluations of new potential testbeds for the evaluation of SDN-based WMNs. We evaluated the R2Lab wireless testbed platform at INRIA Sophia-Antipolis, France. This testbed has 37 customisable wireless devices in an anechoic chamber for reproducible research in wireless WiFi and 4G/5G networks. Our work presents the first initial evaluation of the testbed for wireless multi-hop experiments , using traditional WMN routing protocols. Our results demonstrate the potential for SDN experiments. We believe this is an important contribution in its own right, since experimental validation is a key research methodology in this context, and trust in the validity of experimental results is absolutely critical

Open-Source 4G Experimental Setup

International audienceIn this work, we show how to set up a cellular LTE network for wireless experimentation and measurement, relying on commodity hardware and open-source software. We first deploy a complete LTE network on a single laptop and an SDR hardware. Then, we use it to evaluate the reception performance of a commercial smartphone. In the end, we propose a calibration technique which allows using a smartphone as a wireless power measurement tool by reducing the root-mean-square error of the RSSI, with respect to a reference power measured with a specialized spectrum analyzer, from 6.4 dBm to 2.4 dBm

How far can we go? Towards Realistic Software-Defined Wireless Networking Experiments

International audienceSoftware-Defined Wireless Networking (SDWN) is an emerging approach based on decoupling radio control functions from the radio data plane through programmatic interfaces. Despite diverse ongoing efforts to realize the vision of SDWN, many questions remain open from multiple perspectives such as means to rapid prototype and experiment candidate software solutions applicable to real world deployments. To this end, emulation of SDWN has the potential to boost research and development efforts by re-using existing protocol and application stacks while mimicking the behavior of real wireless networks. In this article, we provide an in-depth discussion on that matter focusing on the Mininet-WiFi emulator design to fill a gap in the experimental platform space. We showcase the applicability of our emulator in an SDN wireless context by illustrating the support of a number of use cases aiming to address the question on how far we can go in realistic SDWN experiments, including comparisons to the results obtained in a wireless testbed. Finally, we discuss the ability to replay packet-level and radio signal traces captured in the real testbed towards a virtual yet realistic emulation environment in support of SDWN research

Evaluating Smartphone Accuracy for RSSI Measurements

International audienceSmartphones are today affordable devices, capable of embedding a large variety of sensors such as magnetometers or orientation sensors, but also the hardware needed to connect them to most wireless communication technologies such as Wi-Fi, Bluetooth, or cellular networks. Therefore, they are handy devices able to perform Received Signal Strength Indicator (RSSI) measurements for a wide variety of applications such as cellular coverage maps, indoor localization, or proximity tracking. However, to the best of our knowledge, the accuracy of such measurements has never been rigorously assessed. The goal of this paper is to assess the accuracy of the RSSI measurements made with a Commercial Off-The-Shelf (COTS) smartphone in a variety of conditions, and how possible inaccuracies can be corrected. We primarily focus on the LTE RSSI, but we also extend our results to the Bluetooth RSSI. In this paper, we build a controlled experimental setup based on commodity hardware and on open-source software. We evaluate the granularity and limitations of the Android API that returns the RSSI. We explore how reliable the measurements in a controlled environment with a monopolarized antenna are. We show that the orientation of the smartphone, the position or orientation of the source, and the transmission power have a significant impact on the accuracy of the measurements. We introduce several correction techniques based on radiation matrix manipulations and on machine learning in order to improve measurement accuracy to less than 5 dBm RMSE, as compared to a professional equipment. We also explore the reliability of measurements made in an outdoor realistic environment. We show that whereas transmission diversity available in LTE base stations significantly improves the measured RSSI regardless of the smartphone orientation, the Bluetooth RSSI remains largely sensitive to the smartphone orientation

Evaluating Smartphone Accuracy for LTE Power Measurement

Smartphones are today relatively cheap devices that embed a large variety of sensors such as magnetometers or orientation sensors, but also the hardware to connect to most wireless communication technologies such as Wi-Fi, Bluetooth, or cellular networks. For this reason, companies, such as OpenSignal or Tutela use smartphones to make crowd-based measurements of the received power from the cellular infrastructure to help operators manage their infrastructure. However, to the best of our knowledge, the accuracy of such measurements has never been rigorously assessed. The goal of this paper is to assess how accurate are measurements of received power from a 4G (LTE) antenna when performed from a Commercial Off-The-Shelf~(COTS) smartphone in different environments. We first evaluate the granularity and limitations of the Android API that returns the received power. We explore how reliable are the measurements from a mono-polarized antenna in a fully controlled environment. We show that the orientation of the smartphone, the position of the source, and the distance to the source has a significant impact on the accuracy of the measurements. We introduce several calibration techniques based on radiation matrices manipulations and machine learning to calibrate the measurements, that is, to improve the accuracy to less than 5 dBm RMSE compared to a professional equipment. Finally, we explore how reliable are measurements in an outdoor environment, in the context of a multi-polarized antenna