Cramer-Rao bounds in the estimation of time of arrival in fading channels

This paper computes the Cramer-Rao bounds for the time of arrival estimation in a multipath Rice and Rayleigh fading scenario, conditioned to the previous estimation of a set of propagation channels, since these channel estimates (correlation between received signal and the pilot sequence) are sufficient statistics in the estimation of delays. Furthermore, channel estimation is a constitutive block in receivers, so we can take advantage of this information to improve timing estimation by using time and space diversity. The received signal is modeled as coming from a scattering environment that disperses the signal both in space and time. Spatial scattering is modeled with a Gaussian distribution and temporal dispersion as an exponential random variable. The impact of the sampling rate, the roll-off factor, the spatial and temporal correlation among channel estimates, the number of channel estimates, and the use of multiple sensors in the antenna at the receiver is studied and related to the mobile subscriber positioning issue. To our knowledge, this model is the only one of its kind as a result of the relationship between the space-time diversity and the accuracy of the timing estimation.Peer ReviewedPostprint (published version

Error bounds for wireless localization in NLOS environments

An efficient and accurate method to evaluate the fundamental error bounds for wireless sen-sor localization is proposed. While there already exist efficient tools like Cram`er-Rao lower bound (CRLB) and position error bound (PEB) to estimate error limits, in their standard formulation they all need an accurate knowledge of the statistic of the ranging error. This requirement, under Non-Line-of-Sight (NLOS) environments, is impossible to be met a priori. Therefore, it is shown that collecting a small number of samples from each link and applying them to a non-parametric estimator, like the Gaussian kernel (GK), could lead to a quite accurate reconstruction of the error distribution. A proposed Edgeworth Expansion method is employed to reconstruct the error statistic in a much more efficient way with respect to the GK. It is shown that with this method, it is possible to get fundamental error bounds almost as accurate as the theoretical case, i.e. when a priori knowledge of the error distribution is available. Therein, a technique to determine fundamental error limits – CRLB and PEB – onsite without knowledge of the statistics of the ranging errors is proposed

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

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.Comment: Submitted to IEEE Transactions of Wireless Communication

Semi-parametric geolocation estimation in NLOS environments

The position of a stationary target can be determined using triangulation in combination with time of arrival measurements at several sensors. In urban environments, none-line-of-sight (NLOS) propagation leads to biased time estimation and thus to inaccurate position estimates. Here, a semi-parametric approach is proposed to mitigate the effects of NLOS propagation. The degree of contamination by NLOS components in the observations, which result in asymmetric noise statistics, is determined and incorporated into the estimator. The proposed method is adequate for environments where the NLOS error plays a dominant role and outperforms previous approaches that assume a symmetric noise statistic

Time-based vs. Fingerprinting-based Positioning Using Artificial Neural Networks

High-accuracy positioning has gained significant interest for many use-cases across various domains such as industrial internet of things (IIoT), healthcare and entertainment. Radio frequency (RF) measurements are widely utilized for user localization. However, challenging radio conditions such as non-line-of-sight (NLOS) and multipath propagation can deteriorate the positioning accuracy. Machine learning (ML)-based estimators have been proposed to overcome these challenges. RF measurements can be utilized for positioning in multiple ways resulting in time-based, angle-based and fingerprinting-based methods. Different methods, however, impose different implementation requirements to the system, and may perform differently in terms of accuracy for a given setting. In this paper, we use artificial neural networks (ANNs) to realize time-of-arrival (ToA)-based and channel impulse response (CIR) fingerprinting-based positioning. We compare their performance for different indoor environments based on real-world ultra-wideband (UWB) measurements. We first show that using ML techniques helps to improve the estimation accuracy compared to conventional techniques for time-based positioning. When comparing time-based and fingerprinting schemes using ANNs, we show that the favorable method in terms of positioning accuracy is different for different environments, where the accuracy is affected not only by the radio propagation conditions but also the density and distribution of reference user locations used for fingerprinting.Comment: Accepted for presentation at International Conference on Indoor Positioning and Indoor Navigation (IPIN) 202

Experimental analysis of dense multipath components in an industrial environment

This work presents an analysis of dense multipath components (DMC) in an industrial workshop. Radio channel sounding was performed with a vector network analyzer and virtual antenna arrays. The specular and dense multipath components were estimated with the RiMAX algorithm. The DMC covariance structure of the RiMAX data model was validated. Two DMC parameters were studied: the distribution of radio channel power between specular and dense multipath, and the DMC reverberation time. The DMC power accounted for 23% to 70% of the total channel power. A significant difference between DMC powers in line-of-sight and nonline-of-sight was observed, which can be largely attributed to the power of the line-of-sight multipath component. In agreement with room electromagnetics theory, the DMC reverberation time was found to be nearly constant. Overall, DMC in the industrial workshop is more important than in office environments: it occupies a fraction of the total channel power that is 4% to 13% larger. The industrial environment absorbs on average 29% of the electromagnetic energy compared to 45%-51% for office environments in literature: this results in a larger reverberation time in the former environment. These findings are explained by the highly cluttered and metallic nature of the workshop