Measurement-Based Wideband Analysis of Dynamic Multipath Propagation in Vehicular Communication Scenarios

Mahler, Kim; Keusgen, Wilhelm; Tufvesson, Fredrik; Zemen, Thomas; Caire, Giuseppe

doi:10.1109/tvt.2016.2621239

Cited by 21 publications

(17 citation statements)

References 24 publications

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“…undergo birth-death processes. The MPC lifetimes when tracked along a straight-line segment at the mobile station (MS) side have been found to obey certain probability distributions, e.g., the exponential distribution [23], [28], [31], the lognormal distribution [26], [29]- [31], and the Birnbaum-Sanders distribution [30], [31]. More important, closely-located users only a few wavelengths apart can encounter different MPCs [19].…”

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confidence: 99%

“…More important, closely-located users only a few wavelengths apart can encounter different MPCs [19]. Channel models that incorporate birthdeath processes at the level of individual MPCs are of interest to emerging applications such as MaMi communications [32], [33], vehicle-to-vehicle communications [28], [30], or multipathassisted localization [26], [27], [31], [34], which can use this information to improve their performance. To the best of our knowledge, none of the earlier referred 5G channel models currently considers birth-death processes for individual MPCs.…”

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confidence: 99%

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Massive MIMO Extensions to the COST 2100 Channel Model: Modeling and Validation

Flordelis

Edfors

et al. 2020

IEEE Trans. Wireless Commun.

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To enable realistic studies of massive multiple-input multiple-output systems, the COST 2100 channel model is extended based on measurements. First, the concept of a base station-side visibility region (BS-VR) is proposed to model the appearance and disappearance of clusters when using a physically-large array. We find that BS-VR lifetimes are exponentially distributed, and that the number of BS-VRs is Poisson distributed with intensity proportional to the sum of the array length and the mean lifetime. Simulations suggest that under certain conditions longer lifetimes can help decorrelating closely-located users. Second, the concept of a multipath component visibility region (MPC-VR) is proposed to model birth-death processes of individual MPCs at the mobile station side. We find that both MPC lifetimes and MPC-VR radii are lognormally distributed. Simulations suggest that unless MPC-VRs are applied the channel condition number is overestimated. Key statistical properties of the proposed extensions, e.g., autocorrelation functions, maximum likelihood estimators, and Cramer-Rao bounds, are derived and analyzed. ). 2 I. INTRODUCTION Massive multiple-input multiple-output (MIMO), MaMi for short, has since its inception [3] attracted considerable attention in the wireless communication community [4]-[6]. Within the last few years, an abundant body of theoretical [5], [6] and experimental [7], [8] research has shown that MaMi systems can improve the energy and spectral efficiencies of today's wireless communication systems by one to two orders of magnitude. For this reason, MaMi is considered a crucial component of the new radio (NR) air interface of the fifth generation (5G) wireless communication standard [9].As is well known, the radio propagation channel ultimately dictates the performance of any wireless communication system. The availability of sufficiently accurate propagation channel models is therefore of critical importance to the design and evaluation of new wireless systems. The 3GPP SCM-3D [10], the WINNER II/WINNER+ [11], [12], and the COST 2100 [13], [14] are examples of channel models customarily used in the development and validation of 5G networks. Among them, the Quasi Deterministic Radio Channel Generator (QuaDRiGa) [15], [16], an extension of the 3GPP-3D and WINNER II/WINNER+ channel models, is especially popular because of its enhanced spatiotemporal consistency. Moreover, it possesses certain features that enable MaMi simulations, namely support for spherical wavefronts, dispersion of clusters in elevation, and independence of arrival/departure angles [16]. Experiment shows [17]-[19] that at least two additional aspects of the MaMi propagation channel need to be considered. The first of them [17], [18] is the presence at the base station (BS) of non-stationarities caused by the appearance and disappearance of clusters along physicallylarge arrays (PLAs). Such BS-side non-stationarities have been addressed in theoretical MaMi channel models [20], [21] in which the appearances and disappearances o...

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Massive MIMO Extensions to the COST 2100 Channel Model: Modeling and Validation

Flordelis

Edfors

et al. 2020

IEEE Trans. Wireless Commun.

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“…The LOS component between vehicles together with contributions from discrete scatterers are useful for relative positioning, whereas diffuse scattering acts as noise from a positioning point of view and deteriorates the positioning performance [4]. In [7], the scattering behavior for V2V communication is analyzed in detail for urban intersections and highway scenarios. The measurements show that, with sufficiently large bandwidth, there are typically 10-20 multipath components that can be tracked and used for positioning.…”

Section: Channel Modeling and Waveform Designmentioning

confidence: 99%

5G mmWave Positioning for Vehicular Networks

Wymeersch

Seco‐Granados

Destino

et al. 2017

IEEE Wireless Commun.

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AbstrAct5G technologies present a new paradigm to provide connectivity to vehicles, in support of high data-rate services, complementing existing inter-vehicle communication standards based on IEEE 802.11p. As we argue, the specific signal characteristics of 5G communication turn out to be highly conducive for vehicle positioning. Hence, 5G can work in synergy with existing on-vehicle positioning and mapping systems to provide redundancy for certain applications, in particular automated driving. This article provides an overview of the evolution of cellular positioning and discusses the key properties of 5G as they relate to vehicular positioning. Open research challenges are presented. requirements for VehiculAr PositioningWith the increase of automated driving in various forms (highway assistance driving, automatic cruise control, self-parking, up to fully autonomous driving) comes a need for precise positioning information. Positioning of vehicles is achieved through a variety of technologies, as illustrated in Fig. 1, including global navigation satellite-based systems (GNSS), radar, mono and stereo cameras, and laser scanners (lidar), which are fused to give the vehicle an understanding of the environment and its location within this environment. The environment is encoded through a map, which is either stored offline or computed online. The process of learning the environment and building detailed maps using onboard sensors is known as mapping. Different positioning applications have different requirements, which are expressed in terms of accuracy, latency, reliability, and cost. On one hand, standard vehicular navigation applications require only a few meters of absolute positioning accuracy, second-level latency, and low reliability (frequent outages are tolerable), but must rely on low-cost sensors. On the other hand, the safety-critical application of autonomous driving will require centimeter-level absolute and relative positioning accuracy, latencies on the order of tens of milliseconds, and high reliability, but can rely on a more expensive suite of sensors. An overview of the accuracy requirements for several key applications is shown in Table 1.GNSS, which has been the workhorse for vehicular absolute positioning in military, professional, and personal navigation, leads to uncertainties on the order of a few meters. Complemented by dedicated base stations, real-time kinematic GNSS further improves the accuracy down to the centimeter level. However, GNSS fails to work in certain common conditions, such as under tree canopies, in the presence of GNSS jammers, and in dense urban environments, due to the blocking of GNSS signals by buildings. Moreover, GNSS is limited by significant latency and low refresh rate, which are key requirements for guaranteeing safety.For relative positioning, onboard sensors such as cameras, radars, and lidars can generally operate well under these GNSS-challenged conditions, and provide very precise information. However, these sensors are costly in terms of computational ...

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“…The mmWave Group at the Fraunhofer Heinrich Hertz Institute in Berlin, Germany has carried out important experimental vehicular communication work, although its studies focus on the IEEE 802.11p standard operated at 5.9 GHz, well below the millimeter band. Among others, in V2V scenarios ranging from highway, to urban, to tunnel convoy [5], as well as at vehicular urban intersections [6] discrete MPCs, their correlation and their dynamics in terms of lifetime, distribution of excess delay and relative Doppler frequency are identified and characterized.…”

Section: Introductionmentioning

confidence: 99%

“…As mentioned in [5], a distinctive characteristic of vehicular communication channels is the position of the antennas. For practical reasons, the antennas in that study were positioned on the car's roof and behind the front windscreen, however, the General knowledge on typical path loss and the presence of MPCs in such environments can be leveraged to refine existing V2V and V2X channel models.…”

Section: Introductionmentioning

confidence: 99%

Investigating the V2V Millimeter-Wave Channel Near a Vehicular Headlight in an Engine Bay

2018

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Measurement-Based Wideband Analysis of Dynamic Multipath Propagation in Vehicular Communication Scenarios

Cited by 21 publications

References 24 publications

Massive MIMO Extensions to the COST 2100 Channel Model: Modeling and Validation

Massive MIMO Extensions to the COST 2100 Channel Model: Modeling and Validation

5G mmWave Positioning for Vehicular Networks

Investigating the V2V Millimeter-Wave Channel Near a Vehicular Headlight in an Engine Bay

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