69 research outputs found

    In-vehicle channel sounding in the 5.8-GHz band

    Get PDF
    The article reports vehicular channel measurements in the frequency band of 5.8 GHz for IEEE 802.11p standard. Experiments for both intra-vehicle and out-of-vehicle environments were carried out. It was observed that the large-scale variations (LSVs) of the power delay profiles (PDPs) can be best described through a two-term exponential decay model, in contrast to the linear models which are suitable for popular ultra-wideband (UWB) systems operating in the 3- to 11-GHz band. The small-scale variations (SSVs) are separated from the PDP by subtracting the LSV and characterized utilizing logistic, generalized extreme value (GEV), and normal distributions. Two sample Kolmogorov-Smirnov (K-S) tests validated that the logistic distribution is optimal for in-car, whereas the GEV distribution serves better for out-of-car measurements. For each measurement, the LSV trend was used to construct the respective channel impulse response (CIR), i.e., tap gains at different delays. Next, the CIR information is fed to an 802.11p simulation testbed to evaluate the bit error rate (BER) performance, following a Rician model. The BER results strongly vouch for the suitability of the protocol for in-car as well as out-of-car wireless applications in stationary environments.The article reports vehicular channel measurements in the frequency band of 5.8 GHz for IEEE 802.11p standard. Experiments for both intra-vehicle and out-of-vehicle environments were carried out. It was observed that the large-scale variations (LSVs) of the power delay profiles (PDPs) can be best described through a two-term exponential decay model, in contrast to the linear models which are suitable for popular ultra-wideband (UWB) systems operating in the 3- to 11-GHz band. The small-scale variations (SSVs) are separated from the PDP by subtracting the LSV and characterized utilizing logistic, generalized extreme value (GEV), and normal distributions. Two sample Kolmogorov-Smirnov (K-S) tests validated that the logistic distribution is optimal for in-car, whereas the GEV distribution serves better for out-of-car measurements. For each measurement, the LSV trend was used to construct the respective channel impulse response (CIR), i.e., tap gains at different delays. Next, the CIR information is fed to an 802.11p simulation testbed to evaluate the bit error rate (BER) performance, following a Rician model. The BER results strongly vouch for the suitability of the protocol for in-car as well as out-of-car wireless applications in stationary environments

    Small-Scale Fading Analysis of the Vehicular-to-Vehicular Channel inside Tunnels

    Get PDF
    [EN] We present a small-scale fading analysis of the vehicular-to-vehicular (V2V) propagation channel at 5.9 GHz when both the transmitter (Tx) and the receiver (Rx) vehicles are inside a tunnel and are driving in the same direction. This analysis is based on channel measurements carried out at different tunnels under real road traffic conditions. The Rice distribution has been adopted to fit the empirical cumulative distribution function (CDF). A comparison of the K factor values inside and outside the tunnels shows differences in the small-scale fading behavior, with the K values derived from the measurements being lower inside the tunnels. Since there are so far few published results for these confined environments, the results obtained can be useful for the deployment of V2V communication systems inside tunnels.The authors want to thank J. A. Campuzano, D. Balaguer, and L. Morag on for their support during the measurement campaign, as well as B. Bernardo-Clemente and A. VilaJimenez for their support and assistance in the laboratory activities. This work has been funded in part by Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia delMinisterio de Economia y Competitividad, Spain, TEC2013-47360-C3-3-P, and Departamento Administrativo de Ciencia, Tecnologia e Innovacion COLCIENCIAS en Colombia.Loredo, S.; Del Castillo, A.; Fernandez, H.; Rodrigo Peñarrocha, VM.; Reig, J.; Rubio Arjona, L. (2017). Small-Scale Fading Analysis of the Vehicular-to-Vehicular Channel inside Tunnels. Wireless Communications and Mobile Computing (Online). 2017:1-6. https://doi.org/10.1155/2017/1987437S16201

    Mobility Management for Cellular Networks:From LTE Towards 5G

    Get PDF

    Dual-Band Non-Stationary Channel Modeling for the Air-Ground Channel

    Get PDF
    Multiple air-to-ground (AG) radio propagation channels are experimentally characterized for two frequency bands, C-band and L-band. These characterizations are aimed to support the specification of the control and non-payload communication (CNPC) links being designed for civil unmanned aircraft systems (UAS). The use of UAS is expected to grow dramatically in the coming decades. In the United States, UAS will be monitored and guided in their operation within the national airspace system (NAS) via the CNPC link. The specifications of the CNPC link are being designed by government, industries, academia and standards bodies such as the Radio Technical Commission for Aeronautics (RTCA). Two bands have been allocated for the CNPC applications: from 5030 to 5091 MHz in C-band and a portion of the aeronautical L-band from 960 to 1215 MHz. The project under which this work was conducted is entitled “Unmanned Aircraft Systems Research: The AG Channel, Robust Waveforms, and Aeronautical Network Simulations”, and this is a sub-project of a NASA project entitled “Unmanned Aircraft Systems Integration in the National Airspace System.” Measurements and modeling for radio propagation channels play an essential role in wireless communication system design and performance evaluation; such models estimate attenuation, delay dispersion, and antenna diversity in wireless channels. The AG channel differs significantly from classic cellular, ground-to-satellite, and other terrestrial wireless channels, particularly in terms of antenna heights and velocity. The previous studies about the AG channels are reviewed and the significant gaps are indicated. NASA Glenn Research Center has conducted an AG channel measurement campaign for multiple ground station local environments, including over sea, over freshwater, desert, suburban, near urban, hilly and mountainous settings. In this campaign, over 316 million power delay profiles (PDPs) or channel impulse responses (CIRs), over 82 flight tracks, have been collected. The measurement equipment was a dual-band single-input multiple-output (SIMO) wideband channel sounding system with bandwidth of 50 MHz in C-band and 5 MHz in L-band. Given the dynamic nature of the AG environments, the channels are statistically non-stationary, meaning that the channel’s statistical parameters change over time/space. We have estimated, via two distinct methods, that the stationarity distance is approximately 15 m—this is the distance over which the channel characteristics can be assumed to be wide sense stationary. The AG channel attenuation is considered as a combination of large scale path loss, small scale fading, and airframe shadowing. The large scale path loss is modeled by both the log-distance model and two-ray models. The theoretical flat earth and curved earth two-ray models are presented, along with their limitations, boundaries and some enhancements. Numerous propagation effects in the AG channels are discussed, and this includes earth spherical divergence, atmospheric refraction, atmospheric gas and hydrometeor attenuations, and ducting. The small scale fading is described by the Ricean distribution, which for unit-energy normalizations are completely characterized by Ricean K-factors; these K-factors are approximately 28.7 dB in C-band and 13.1 dB in L-band. The line-of-sight (LOS) signal can be obstructed by the airplane itself in some specific maneuvers, and this is termed airframe shadowing. For the specific flights and NASA aircraft used in our measurements, the shadowing duration was found to be on average 30 seconds, and the shadowing loss can be as large as 40 dB. The statistics, models and simulation algorithm for the airframe shadowing are provided. The wideband characteristics of the AG channel are quantified using root-mean-square delay spread (RMS-DS), and illustrated by sequences of PDPs. Tapped delay line (TDL) models are also provided. Doppler effects for over water channels are also addressed. Given the sparsity of the diffuse multipath components (MPCs) in the AG channels and generally short lifetime of these MPCs, the CIRs are modeled by the two-ray model plus intermittent 3rd, 4th or 5th “rays.” Models for intermittent ray probability of occurrence, duration, relative power, phase, and excess delay are provided. The channels at C-band and L-band were found to be essentially uncorrelated; this conclusion holds for the specific antenna locations used in our experiments (the aircraft underside), but is not expected to change for arbitrary antenna locations. For the aircraft antenna locations employed, intra-band signals are highly correlated, and this is as expected for channels with a dominant LOS component; analytical correlation computations show interesting two-ray effects that also appear in measurements. Multiple aircraft antennas and carefully selected locations are recommended for mitigating airframe shadowing for the CNPC link. Future work for AG channel modeling includes characterization of L-band delay dispersion and L-band TDL models, estimation of building and/or tree shadowing for small UAS that fly at very low altitudes, evaluation of multiple ground site(s) antenna diversity, and AG channel modeling via geometric techniques, e.g., ray-tracing

    Building Decentralized Fog Computing-Based Smart Parking Systems: From Deterministic Propagation Modeling to Practical Deployment

    Get PDF
    [Abstract] The traditional process of finding a vacant parking slot is often inefficient: it increases driving time, traffic congestion, fuel consumption and exhaust emissions. To address such problems, smart parking systems have been proposed to help drivers to find available parking slots faster using latest sensing and communications technologies. However, the deployment of the communications infrastructure of a smart parking is not straightforward due to multiple factors that may affect wireless propagation. Moreover, a smart parking system needs to provide not only accurate information on available spots, but also fast responses while guaranteeing the system availability even in the case of lacking connectivity. This article describes the development of a decentralized low-latency smart parking system: from its conception, design and theoretical simulation, to its empirical validation. Thus, this work first characterizes a real-world scenario and proposes a fog computing and Internet of Things (IoT) based communications architecture to provide smart parking services. Next, a thorough analysis on the wireless channel properties is carried out by means of an in-house developed deterministic 3D-Ray Launching (3D-RL) tool. The obtained results are validated through a real-world measurement campaign and then the communications architecture is implemented by using ZigBee sensor nodes. The implemented architecture also makes use of Bluetooth Low Energy beacons, an Android app, a decentralized database and fog computing gateways, whose performance is evaluated in terms of response latency and processing rate. Results show that the proposed system is able to deliver information to the drivers fast, with no need for relying on remote servers. As a consequence, the presented development methodology and communications evaluation tool can be useful for future smart parking developers, which can determine the optimal locations of the wireless transceivers during the simulation stage and then deploy a system that can provide fast responses and decentralized services.Xunta de Galicia; ED431G2019/01Agencia Estatal de InvestigaciĂłn of Spain; TEC2016-75067-C4-1-RAgencia Estatal de InvestigaciĂłn of Spain; RED2018-102668-TAgencia Estatal de InvestigaciĂłn of Spain; PID2019-104958RB-C42Ministerio de Ciencia, InnovaciĂłn y Universidades; RTI2018-095499-B-C3
    • …
    corecore