Comparison of Angle-Spread in Outdoor-to-Outdoor and Outdoor-to-Indoor Cases in an Urban Macro-Cell (original) (raw)
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Propagation characteristics of wideband MIMO channel in urban micro- and macrocells
2008 IEEE 19th International Symposium on Personal, Indoor and Mobile Radio Communications, 2008
The wideband channel measurements at 580 MHz, 2.35 GHz and 4.90 GHz have been performed in the urban microand macrocell scenarios in the cities of China with multipleinput multiple-output (MIMO) channel sounder. The measured cases include line-of-sight (LOS) and non-line-of-sight (NLOS) propagation. Statistical results and comparative analysis for both scenarios are presented in this paper, including path loss (PL), root mean square (rms) delay spread (DS) and maximum excess delay (maxED), and angular spread (AS). In NLOS case, the frequency dependent factor (FDF) is observed as 32.1 by fitting the PL model of 2.35 GHz and 4.90 GHz. Moreover, a larger rms DS in urban microcell and AS at both base station (BS) and mobile subscriber (MS) are found for the denser and higher buildings in the cities of China. As the frequency varying from 580 MHz to 4.90 GHz, the median rms DS is decreased from 330 ns to 130 ns for NLOS case.
Outdoor-to-Indoor Office MIMO Measurements and Analysis at 5.2 GHz
IEEE Transactions on Vehicular Technology, 2000
The outdoor-to-indoor wireless propagation channel is of interest for cellular and wireless local area network applications. This paper presents the measurement results and analysis based on our multiple-input-multiple-output (MIMO) measurement campaign, which is one of the first to characterize the outdoor-to-indoor channel. The measurements were performed at 5.2 GHz; the receiver was placed indoors at 53 different locations in an office building, and the transmitter was placed at three "base stations" positions on a nearby rooftop. We report on the root-mean-square (RMS) angular spread, building penetration, and other statistical parameters that characterize the channel. Our analysis is focused on three MIMO channel assumptions often used in stochastic models. 1) It is commonly assumed that the channel matrix can be represented as a sum of a line-of-sight (LOS) contribution and a zero-mean complex Gaussian distribution. Our investigation shows that this model does not adequately represent our measurement data. 2) It is often assumed that the Rician K-factor is equal to the power ratio of the LOS component and the other multipath components (MPCs). We show that this is not the case, and we highlight the difference between the Rician K-factor often associated with LOS channels and a similar power ratio for the estimated LOS MPC. 3) A widespread assumption is that the full correlation matrix of the channel can be decomposed into a Kronecker product of the correlation matrices at the transmit and receive array. Our investigations show that the direction-of-arrival (DOA) spectrum noticeably depends on the direction-of-departure (DOD); therefore, the Kronecker model is not applicable, and models with less-restrictive assumptions on the channel, e.g., the Weichselberger model or the full correlation model, should be used.
Statistical Evaluation of Outdoor-to-Indoor Office MIMO Measurements at 5.2 GHz
2005 IEEE 61st Vehicular Technology Conference, 2005
In this paper, we present a statistical evaluation of an outdoor-to-indoor Multiple-Input Multiple-Output (MIMO) measurement campaign performed at 5.2 GHz. 159 measurement locations in an office building are analyzed. Our analysis pays special attention to two key assumptions that are widely used in stochastic channel models. An assumption that is used in practically every channel model is that the channel can be represented as a sum of a line-of-sight (LOS) component plus a (possibly correlated) zero-mean complex Gaussian distribution. Our investigation shows that this model does NOT adequately represent our measurement data. Our analysis also highlights the difference between the LOS power factor and the Ricean K-factor. We show that the direction-ofarrival (DOA) sprectrum depends noticeably on the direction-of-departure (DOD). Therefore, the popular Kronecker model is not applicable, and the more general Weichselberger model should be used.
IEEE Antennas and Propagation Magazine, 2014
The goal of this paper was to fi nd the differences of the channel models and parameters as well as the correlations of the channel parameters between 2 GHz and 5 GHz with 100 MHz bandwidth when measurements were performed in exactly the same routes for urban microcells. The path-loss models and shadow fading were studied by using singleinput single-output (SISO) measurements. The channel characteristics were investigated by the rms delay spread, rms angular spread, shadow fading, Ricean factor, number of clusters, cross-polar discrimination, and channel capacities using both SISO and MIMO (multiple-input multiple-output) measurements. Static measurements at 5 GHz were also performed in both micro-and macro-cells to investigate clustering effects. Moreover, the channel models and parameters were compared with WINNER work to test their validity in urban microcells. The main conclusion was that the 2 GHz and
Universal Journal of Electrical and Electronic Engineering, 2015
Building penetration loss at 1900 MHz bands in suburban environment is evaluated. The measurements are conducted in real Global System for Mobile Communications (GSM) and Universal Mobile Telecommunications System (UMTS) networks. Four buildings are studied aiming to provide first-order statistics of mobile signal coverage inside buildings. Results show that, on average, no significant signal band dependency has been confirmed. In general, UMTS-1900 MHz signals exhibit slight higher penetration loss values than GSM-1900 MHz signals. Analysis shows that the mean building penetration loss for all measured signals at the ground floor is about 16 dB. The standard deviation of building penetration loss was about 4.5 dB for wideband signals and 2.5 dB for narrowband signals. Results show also that, the average rate of change in building penetration loss with height is 0.95 dB per meter for wideband signals versus 0.65 dB per meter for narrowband signals.
Wireless Personal Communications, 2007
We present a geometric channel model to study the effect of antenna directivity on angular power distribution at the mobile terminal in urban macrocells. The methodology reviewed in this paper integrates the antenna effect into the model geometry, thereby facilitating a system-dependent channel characterization. As each device is limited in terms of measurement sensitivity, the effective scatterer distribution is essentially dependent on the antenna beam pattern. Subsequently, two heuristic rules are proposed to establish the underlying relationship between the model geometry and the corresponding wave-propagation processes. It is shown that the influence of directional antenna is twofold. First, it alters the spatial distribution of scatterers by providing a different sample space for the random field, and secondly, it distributes signal components into the angles-of-departure or collects them from the angles-of-arrival by weighted combination. Important channel parameters measured at the mobile terminal such as the angular power distribution, Doppler spectrum, and multipath shape factors are also investigated to further exemplify the usefulness of the proposed model.
Realistic channel data have shown to be a mandatory pre-request for performance studies of recent mobile system designs beyond 3G, in particular when considering novel multiantenna techniques. Channel models conceived in IST-WINNER, COST273 or standardisation bodies are based on real-field measurement data. This paper presents analysis results of so called large-scale parameters derived from an extensive multiuser and multi-base station MIMO measurement campaign in an urban macro cell scenario. The focus is on the parameters of the delay and power domains, their distribution as well as auto and crosscorrelations. Parameters from WINNER II channel model could be verified, furthermore missing gaps among them could be closed. A third contribution shows strong variations of the parameters depending on the base station position. Parts of the considered measurement data are free accessible and can be used for free research.
IEEE Transactions on Antennas and Propagation, 2000
Distributing the construction of a multiple-input multiple-output (MIMO) system among more than one mobile user reduces hardware and processing complexity at the terminal end, and may result in improved channel propagation conditions, particularly those related to spatial correlation. In this paper, measured outdoor propagation data is used to study a 4 4 virtual MIMO system formed from a two-user 4 4 classical system using only two of the antennas at each constituent user. The capacity, -factor, and spatial correlation are evaluated for 226 possible pairs of users whose channels were measured while standing and walking. The proportions of pairings that result in a capacity increase and -factor and correlation reductions over both, only one and neither of the constituents are determined. It is found that correlation reduction appears to be a stronger sign of capacity improvement than is -factor reduction. When choosing a partner for a given single user, results show that it is easier to improve these three parameters as the user's value of them becomes poorer, so thresholds applicable to this data set are found which specify values of capacity, , and correlation beyond which at least 50% of possible pairings improve matters.
Outdoor to indoor office MIMO measurements at 5.2 GHz
IEEE 60th Vehicular Technology Conference, 2004. VTC2004-Fall. 2004, 2004
In this paper, we present a statistical evaluation of an outdoor-to-indoor Multiple-Input Multiple-Output (MIMO) measurement campaign performed at 5.2 GHz. 159 measurement locations in an office building are analyzed. Our analysis pays special attention to two key assumptions that are widely used in stochastic channel models. An assumption that is used in practically every channel model is that the channel can be represented as a sum of a line-of-sight (LOS) component plus a (possibly correlated) zeromean complex Gaussian distribution. Our investigation shows that this model does NOT adequately represent our measurement data. Our analysis also highlights the difference between the LOS power factor and the Ricean K-factor. We show that the direction-ofarrival (DOA) spectrum depends noticeably on the direction-ofdeparture (DOD). Therefore, the popular Kronecker model is not applicable, and the more general Weichselberger model should be used.
Observed Relation Between The Relative MIMO Gain And Distance
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This paper shows some results for the relative MIMO capacity (compared with a SISO system) with respect to the number of antennas, antenna orientation and distance. The MIMO capacity is extracted from the Geometrically Based Single Bounce Channel Model, where the channel is modelled by propagation in an environment composed of clusters of scatterers. The results are shown for the pico-, micro-and macro-cell environments. For picoand micro-cells, an increase in the number of antennas has a larger impact on capacity gain than the one for macro-cells. For micro-cell scenarios, a 20% variation in performance is obtained, depending on the orientation of the antennas of both transmitter and receiver. For the macro-cell, a similar variation is seen, but only for the orientation of base station antennas. For both the picoand micro-cell, the relative MIMO gain is very similar for both the up-and downlinks. The gain increases from macro-to pico-cell scenarios, on a ratio that can reach 1 to 3.