4 research outputs found

    Higher Order Statistics in a mmWave Propagation Environment

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    (c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.[EN] A thorough measurement campaign in an indoor environment at the millimeter-wave band is carried out with an aim at characterizing the short-term fading channel in terms of its higher-order statistics. The measurements are conducted in a variety of scenarios, with frequencies ranging from 55 to 65 GHz, in line-of-sight and non-line-of-sight conditions, and combinations of horizontal and vertical polarizations at both the transmitter and the receiver. A number of fading models are tested, namely Rayleigh, Rice, Nakagami-m, alpha-mu, kappa-mu, eta-mu, and alpha-eta-kappa-mu. The main second-order statistics under analysis are the level crossing rate (LCR) and average fade duration (AFD) both given per distance unit. From the experimental data, the parameters of these statistics are estimated, and the corresponding curves of the theoretical models are compared with the empirical ones and the best model is selected. Additionally, the study of the very general distribution, namely alpha-eta-kappa-mu, is advanced, in which new expressions for time-/distance-domain LCR and Al-ll are derived using an envelope-based approach. Such an approach leads to integral-form formulations with much less computational complexity and computes rapidly compared with the already existing ones presented elsewhere, also given in the integral form. Furthermore, a series of expansion expression for the alpha-eta-kappa-mu time-/distance-domain LCR is then derived that improves even further the computational time.This work was supported in part by the Conselho Nacional de Desenvolvimento Cientico e Tecnologico (CNPq) under Grant 304248/2014-2 and Grant 308365/2017-8, in part by the Rede Nacional de Ensino e Pesquisa (RNP), with resources from Ministerio da Ciencia, Tecnologia, Inovacoes e Comunicacoes (MCTIC), through the Radiocommunication Reference Center [Centro de Referencia em Radiocomunicacoes (CRR)] Project of the National Institute of Telecommunications [Instituto Nacional de Telecomunicacoes (INATEL)], Brazil, under Grant 01250.075413/2018-04, and in part by the Ministerio de Economia, Industria y Competitividad of the Spanish Government through the Agencia Estatal de Investigacion (AEI) and the Fondo Europeo de Desarrollo Regional (FEDER) under Project TEC2017-86779-C2-2-R.Dos Anjos, AA.; Rufino-Marins, TR.; Nogueira Da Silva, CR.; Rodrigo Peñarrocha, VM.; Rubio Arjona, L.; Reig, J.; Amaral De Souza, RA.... (2019). Higher Order Statistics in a mmWave Propagation Environment. IEEE Access. 7:103876-103892. https://doi.org/10.1109/ACCESS.2019.2930931S103876103892

    Path loss modeling for vehicular system performance and communicaitons protocols evaluation

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    Vehicular communications are receiving considerable attention due to the introduction of the intelligent transportation system (ITS) concept, enabling smart and intelligent driving technologies and applications. To design, evaluate and optimize ITS applications and services oriented to improve vehicular safety, but also non-safety applications based on wireless systems, the knowledge of the propagation channel is vital. In particular, the mean path loss is one of the most important parameters used in the link budget, being a measure of the channel quality and limiting the maximum allowed distance between the transmitter (Tx) and the receiver (Rx). From a narrowband vehicular-to-vehicular (V2V) channel measurement campaign carried out at 5.9 GHz in three different urban environments characterized by high traffic density, this paper analyzes the path loss in terms of the Tx-Rx separation distance and fading statistics. Based on a linear slope model, values for the path loss exponent and the standard deviation of shadowing are reported. We have evaluated the packet error rate (PER) and the maximum achievable Tx-Rx separation distance for a PER threshold level of 10% according to the digital short-range communications (DSRC) specifications. The results reported here can be incorporated in an easy way to vehicular networks (VANETs) simulators in order to develop, evaluate and validate new protocols and systems architecture configurations under realistic propagation conditions.Fernández González, HA.; Rubio Arjona, L.; Reig, J.; Rodrigo Peñarrocha, VM.; Valero-Nogueira, A. (2013). Path loss modeling for vehicular system performance and communicaitons protocols evaluation. Mobile Networks and Applications. 18(6):755-765. doi:10.1007/s11036-013-0463-xS755765186Gallager B, Akatsuka H, Suzuki H (2006) Wireless communications for vehicle safety: radio link performance and wireless connectivity. IEEE Veh Technol Mag 1(4):4–24Rubio L, Reig J, Fernández H (2011) Propagation aspects in vehicular networks, Vehicular technologies. Almeida M (ed) InTechWang C-X, Vasilakos A, Murch R, Shen SGX, Chen W, Kosch T (2011) Guest editorial. Vehicular communications and networks – part I. IEEE J Select Areas Commun 29(1):1–6ASTM E2213-03 (2003) Standard specification for telecommunications and information exchange between roadside and vehicle systems – 5 GHz band Dedicated Short Range Communications (DSRC) Medium Access Control (MAC) and Physical Layer (PHY) specifications. American Society for Testing Materials (ASTM), West ConshohockenIEEE 1609 – Family of Standards for Wireless Access in Vehicular Environments (WAVE). [Online]. Available: http://www.standards.its.dot.govETSI TR 102 492–2 Part 2 (2006) Technical characteristics for Pan European Harminized Communications Equipment Operating in the 5 GHz frequency range intended for road safety and traffic management, and for non-safety related ITS applications, European Telecommunications Standard Institute (ETSI), Technical Report, Sophia Antipolis, FranceThe Car-to-Car Communication Comsortium (C2CC): http:/www.car-to-car.orgMecklenbräuker C, Molisch A, Karedal J, Tufvesson F, Paier A, Bernado L, Zemen T, Klemp O, Czink N (2011) Vehicular channel characterization and its implications for wireless system design and performance. IEEE Proc 99(7):1189–1212Ghafoor KZ, Bakar KA, Lloret J, Khokhar RH, Lee KC (2013) Intelligent beaconless geographical routing for urban vehicular environments. Int J Wireless Netw 19(3):345–362Ghafoor KZ, Lloret J, Bakar KA, Sadiq AS, Mussa SAB (2013) Beaconing approaches in vehicular ad hoc networks: a survey. Int J Wirel Pers Commun. Published Online (May 2013)Michelson DG, Ghassemzadeh SS (2009) New directions in wireless communications, Springer Science+Busines Media (Chapter 1)IEEE 802.11p (2010) Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications Amendment 6: Wireless Access in Vehicular Environments, Institute of Electrical and Electronic Engineers (IEEE), New York, USA.Karedal J, Czink N, Paier A, Tufvesson F, Moisch AF (2011) Path loss modeling for vehicle-to-vehicle communications. IEEE Trans Veh Technol 60(1):323–327Cheng L, Henty B, Stancil D, Bai F, Mudalige P (2007) Mobile vehicle-to-vehicle narrow-band channel measurement and characterization of the 5.9 GHz dedicated short range communication (DSRC) frequency band. IEEE J Select Areas Commun 25(8):1501–1516Cheng L, Henty B, Cooper R, Stancil D, Bai F (2008) Multi-path propagation measurements for vehicular networks at 5.9 GHz. IEEE Wireless Communications and Networking Conference, pp. 1239–1244Tan I, Tang W, Laberteaux K, Bahai N (2008) Measurement and analysis of wireless channel impairments in dsrc vehicular communications. IEEE International Conference on Communications, pp. 4882–4888.Campuzano AJ, Fernández H, Balaguer D, Vila-Jiménez A, Bernardo-Clemente B, Rodrigo-Peñarrocha VM, Reig J, Valero-Nogueira A, Rubio L (2012) Vehicular-to-vehicular channel characterization and measurement results. WAVES 4(1):14–24Kunisch J, Pamp J (2008) Wideband car-to-car radio channel measurements and model at 5.9 GHz. IEEE 68th Vehicular Technology Conference, pp. 1–5Gozalvez J, Sepulcre M (2007) Opportunistic technique for efficient wireless vehicular communications. IEEE Veh Technol Mag 2(4):33–39Zang Y, Stibor L, Orfanos G, Guo S, Reumerman H (2005) An error model for inter-vehicle communications in highway scenarios at 5.9 GHz. Proc. Int. Workshop on performance evaluation of wireless ad hoc, sensor, and ubiquitous networks, pp. 49–5

    Log-moment estimators of the Nakagami-lognormal distribution

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    [EN] In this paper, estimators of the Nakagami-lognormal (NL) distribution based on the method of log-moments have been derived and thoroughly analyzed. Unlike maximum likelihood (ML) estimators, the log-moment estimators of the NL distribution are obtained using straightforward equations with a unique solution. Also, their performance has been evaluated using the sample mean, confidence regions and normalized mean square error (NMSE). The NL distribution has been extensively used to model composite small-scale fading and shadowing in wireless communication channels. This distribution is of interest in scenarios where the small-scale fading and the shadowing processes cannot be easily separated such as the vehicular environment.This work has been funded in part by the Programa de Estancias de Movilidad de Profesores e Investigadores en Centros Extranjeros de Ensenanza Superior e Investigacion of the Ministerio de Educacion, Cultura y Deporte, Spain, PR2015-00151 and by the Ministerio de Economia, Industria y Competitividad of the Spanish Government under the national project TEC2017-86779-C2-2-R, through the Agencia Estatal de Investigacion (AEI) and the Fondo Europeo de Desarrollo Regional (FEDER).Reig, J.; Brennan, C.; Rodrigo Peñarrocha, VM.; Rubio Arjona, L. (2019). Log-moment estimators of the Nakagami-lognormal distribution. EURASIP Journal on Wireless Communications and Networking. 1-10. https://doi.org/10.1186/s13638-018-1328-6S110J. M. Ho, G. L. Stüber, in Co-channel interference of microcellular systems on shadowed Nakagami fading channels. Proc. IEEE 43rd Vehicular Technology Conference, 1993 (VTC 93) (IEEESecaucus, 1993), pp. 568–571.A. A. Abu-Dayya, N. C. Beaulieu, Micro- and macrodiversity NCFSK (DPSK) on shadowed Nakagami-fading channels. IEEE Trans. Commun.42(9), 2693–2702 (1994).X. Wang, W. Wang, Z. Bu, Fade statistics for selection diversity in Nakagami-lognormal fading channels. Electron. Lett.42(18), 1046–1047 (2006).D. T. Nguyen, Q. T. Nguyen, S. C. Lam, Analysis and simulation of MRC diversity reception in correlated composite Nakagami-lognormal fading channels. REV J. Electron. Commun.4(1–2), 44–51 (2014).P. Xu, X. Zhou, D. Hu, in Performance evaluations of adaptive modulation over composite Nakagami-lognormal fading channels. 2009 15th Asia-Pacific Conference on Communications (IEEEShanghai, 2009), pp. 467–470.G. C. Alexandropoulos, A. Conti, P. T. Mathiopoulos, in Adaptive M-QAM systems with diversity in correlated Nakagami-m fading and shadowing. IEEE Global Telecommunications Conference (GLOBECOM 2010) (IEEEMiami, 2010), pp. 1–5.Ö. Bulakci, A. B. Saleh, J. Hämäläinen, S. Redana, Performance analysis of relay site planning over composite fading/shadowing channels with cochannel interference. IEEE Trans. Veh. Technol.62(4), 1692–1706 (2013).W. Cheng, Y. Huang, On the performance of adaptive SC/MRC cooperative systems over composite fading channels. Chin. J. Electron.25(3), 533–540 (2016).M. G. Kibria, G. P. Villardi, W. Liao, K. Nguyen, K. Ishizu, F. Kojima, Outage analysis of offloading in heterogeneous networks: Composite fading channels. IEEE Trans. Veh. Technol.66(10), 8990–9004 (2017).K. Cho, J. Lee, C. G. Kang, Stochastic geometry-based coverage and rate analysis under Nakagami & log-normal composite fading channel for downlink cellular networks. IEEE Commun. Lett.21(6), 1437–1440 (2017).R. Singh, M. Rawat, Closed-form distribution and analysis of a combined Nakagami-lognormal shadowing and unshadowing fading channel. J Telecommun. Inf. Technol.4:, 81–87 (2016).J. Reig, L. Rubio, Estimation of the composite fast fading and shadowing distribution using the log-moments in wireless communications. IEEE Trans. Wireless. Commun.12(8), 3672–3681 (2013).S. Atapattu, C. Tellambura, H. Jiang, A mixture gamma distribution to model the SNR of wireless channels. IEEE Trans. Wireless Commun.10(12), 4193–4203 (2011).Q. Wang, H. Lin, P. Kam, Tight bounds and invertible average error probability expressions over composite fading channels. J. Commun. Netw.18(2), 182–189 (2016).J. M. Holtzmann, On using perturbation analysis to do sensitivity analysis: derivatives versus differences. IEEE Trans. Autom. Control. 37(2), 243–247 (1992).H. Suzuki, A statistical model for urban radio propagation. IEEE Trans. Commun.25(7), 673–680 (1977).M. D. Yacoub, The α- μ distribution: a physical fading model for the Stacy distribution. IEEE Trans. Veh. Technol.56(1), 122–124 (2007).P. M. Shankar, Error rates in generalized shadowed fading channels. Wirel. Pers. Commun.28(3), 233–238 (2004).J. -M. Nicolas, Introduction aux statistiques de deuxième espèce: applications des logs-moments et des logs-cumulants à l’analyse des lois d’images radar. Traitement du Signal. 19(3), 139–167 (2002). Translation to English by S. N. Anfinsen.C. Withers, S. Nadarajah, A generalized Suzuki distribution. Wirel. Pers. Commun.62(4), 807–830 (2012).M. Abramowitz, Handbook of Mathematical Functions, with Formulas, Graphs, and Mathematical Tables, 9th edn. (Dover, New York, NY, 1972).M. K. Simon, M. S. Alouini, Digital Communication over Fading Channels, 2nd edn. (Wiley, Hoboken, NY, 2005).Z. Sun, J. Du, in Proc. 10th International Conference, ICIC 2014, ed. by D. -S. Huang, V. Bevilacqua, and P. Premaratne. Log-cumulant parameter estimator of log-normal distribution. Intelligent computing theory (SpringerNew York, NY, 2014), pp. 668–674.S. Zhang, J. M. Jin, Computation of Special Functions (Wiley, New York, 1996).G. Casella, R. L. Berger, Statistical Inference (Duxbury Thomson Learning, Pacific Grove, CA, 2002).C. Kleiber, S. Kotz, Statistical Size Distributions in Economics and Actuarial Sciences (Wiley, Hoboken, NJ, 2003).L. Devroye, Non-uniform Random Variate Generation (Springer, New York,1986).A. Abdi, M. Kaveh, Performance comparison of three different estimators for the Nakagami m parameter using Monte Carlo simulation. IEEE Commun. Lett.4(4), 119–121 (2000).L. Rubio, J. Reig, N. Cardona, Evaluation of Nakagami fading behaviour based on measurements in urban scenarios. Int. J. Electron. Commun. (AEÜ). 61(2), 135–138 (2007)

    Path Loss Characterization in an Outdoor Corridor Environment for IoT-5G in a Smart Campus University at 850 MHz and 3.5 GHz Frequency Bands

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    The usage scenarios defined in the ITU-M2150-1 recommendation for IMT-2020 systems, including enhanced Mobile Broadband (eMBB), Ultra-reliable Low-latency Communication (URLLC), and massive Machine Type Communication (mMTC), allow the possibility of accessing different services through the set of Radio Interface Technologies (RITs), Long-term Evolution (LTE), and New Radio (NR), which are components of RIT. The potential of the low and medium frequency bands allocated by the Federal Communications Commission (FCC) for the fifth generation of mobile communications (5G) is described. In addition, in the Internet of Things (IoT) applications that will be covered by the case of use of the mMTC are framed. In this sense, a propagation channel measurement campaign was carried out at 850 MHz and 5.9 GHz in a covered corridor environment, located in an open space within the facilities of the Pedagogical and Technological University of Colombia campus. The measurements were carried out in the time domain using a channel sounder based on a Universal Software Radio Peripheral (USRP) to obtain the received signal power levels over a range of separation distances between the transmitter and receiver from 2.00 m to 67.5 m. Then, a link budget was proposed to describe the path loss behavior as a function of these distances to obtain the parameters for the close-in free space reference distance (CI) and the floating intercept (FI) path loss prediction models. These parameters were estimated from the measurements made using the Minimum Mean Square Error (MMSE) approach. The estimated path loss exponent (PLE) values for both the CI and FI path loss models at 850 MHz and 3.5 GHz are in the range of 2.21 to 2.41, respectively. This shows that the multipath effect causes a lack of constructive interference to the received power signal for this type of outdoor corridor scenario. These results can be used in simulation tools to evaluate the path loss behavior and optimize the deployment of device and sensor network infrastructure to enable 5G-IoT connectivity in smart university campus scenarios
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