Tensor-Based Near-Field Localization Using Massive Antenna Arrays
TL;DR: In this paper, the Tensor-Based Near Field Localization (TeNFiLoc) algorithm is proposed for channel estimation and user localization on the uplink of a multi-carrier wireless communication system with a massive antenna array.
Abstract: In this paper, the Tensor-Based Near-Field Localization (TeNFiLoc) algorithm is proposed for channel estimation and user localization on the uplink of a multi-carrier wireless communication system with a massive antenna array. OFDM is used as the modulation scheme and the user can be located in the near-field of the array. The exact spherical model of the impinging wavefronts allows TeNFiLoc to localize and identify the reflected paths and distinguish them from the Line-of-Sight (LoS) path. Some additional processing generally allows TeNFiLoc to localize user even in non-LoS scenarios, when the LoS path may be blocked or shadowed, provided that the number of reflected paths that reach the receiver is not less than three. It is also shown that perfect knowledge of the transmitted data is not necessarily required to perform channel estimation and user localization. Using a specific design of the transmitted signal, an additional low throughput, highly reliable communication link to the receiver can be established. Simulation results demonstrate the excellent localization accuracy of the TeNFiLoc algorithm and its applicability in practical scenarios.
Citations
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31 Oct 2022
TL;DR: In this article , a single-sample DoA estimation algorithm based on Hankel-matrix-representation and singular value decomposition (SVD) of the individual antenna-array snapshot is presented.
Abstract: Modern networked robotic platforms operating autonomously on the ground, in the air, or in space over highfrequency bands (e.g., mm-wave or future THz) require rapid and effective estimation of the direction of arrival (DoA) of signals of interest to maintain high data rate connectivity with each other and avoid interference from external in-band sources. High robotic platform mobility limits -or completely negates- our ability to wait and collect the necessary statistically stationary sequence of antenna-array-front measurements. As a result, conventional statistical DoA estimation optimization methods may not be applicable. In this paper, we present for the first time in the literature a single-sample DoA estimation algorithm based on Hankel-matrix-representation and singular-value decomposition (SVD) of the individual antenna-array snapshot. We compare the newly proposed estimator against the Maximum Likelihood (ML) single-sample estimator of the DoA of a signal observed in white Gaussian noise and -arguably surprisingly- demonstrate significant superiority in each metric of interest, such as meansquare estimation error, bias, and variance.
1 citations
TL;DR: In this article , a tensor-based signal model impinging on a monostatic frequency diverse array multiple-input multiple-output (FDA-MIMO) radar was formulated, and a corresponding tensor decomposition-based localization algorithm (TenDLA) was developed.
Abstract: Target localization is a fundamental problem in array signal processing. The problem of locating near-field targets with multiple-input multiple-output (MIMO) radar has been studied extensively; however, most of the conventional matrix-based approaches suffer from limitations in terms of the representation and exploitation of the multidimensional nature of MIMO radar signals. In this paper, we addressed the problem of localizing multiple targets in the near-field region, aiming at pursuing a solution applicable for multidimensional signal that is able to achieve sufficient accuracy. A tensor-based signal model impinging on a monostatic frequency diverse array multiple-input multiple-output (FDA-MIMO) radar was formulated, and a corresponding tensor decomposition-based localization algorithm (TenDLA) that showcases the connection between the tensor-based analysis and the localization problem was developed. Additionally, a correction procedure to mitigate the estimation deviations on the range and angle was presented, yielding significant improvements in estimation accuracy. Numerical examples demonstrated the validity and effectiveness of the proposed approach, and it was shown that this approach is superior to conventional methods due to its high-resolution estimation accuracy as well as its relatively low computational costs.
TL;DR: In this paper , a Bayesian tensor-based scheme is proposed for simultaneous channel estimation and localization (SCEAL) in wideband terahertz (THz) massive MIMO systems with hybrid analog-digital architectures.
Abstract: In this letter, a Bayesian tensor-based scheme is proposed for simultaneous channel estimation and localization (SCEAL) in wideband terahertz (THz) massive multiple-input multiple-output (MIMO) systems with hybrid analog-digital architectures. Considering the beam squint effect, we construct the received training signal into a sixth-order tensor model, including the angle-of-arrival (AOA)/angle-of-departure (AOD), time delay, and path gains. Then, a real-valued Bayesian inference algorithm is developed to fit the constructed tensor model for SCEAL. The proposed algorithm achieves SCEAL with and without line-of-sight (LOS) path, and offers higher accuracy compared to the state-of-the-art algorithms. Simulations verify the effectiveness of the proposed algorithm.
References
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01 Jan 1989
TL;DR: In this paper, the authors provide a thorough treatment of the fundamental theorems and properties of discrete-time linear systems, filtering, sampling, and discrete time Fourier analysis.
Abstract: For senior/graduate-level courses in Discrete-Time Signal Processing. THE definitive, authoritative text on DSP -- ideal for those with an introductory-level knowledge of signals and systems. Written by prominent, DSP pioneers, it provides thorough treatment of the fundamental theorems and properties of discrete-time linear systems, filtering, sampling, and discrete-time Fourier Analysis. By focusing on the general and universal concepts in discrete-time signal processing, it remains vital and relevant to the new challenges arising in the field --without limiting itself to specific technologies with relatively short life spans.
10,388 citations
TL;DR: The gains in multiuser systems are even more impressive, because such systems offer the possibility to transmit simultaneously to several users and the flexibility to select what users to schedule for reception at any given point in time.
Abstract: Multiple-input multiple-output (MIMO) technology is maturing and is being incorporated into emerging wireless broadband standards like long-term evolution (LTE) [1]. For example, the LTE standard allows for up to eight antenna ports at the base station. Basically, the more antennas the transmitter/receiver is equipped with, and the more degrees of freedom that the propagation channel can provide, the better the performance in terms of data rate or link reliability. More precisely, on a quasi static channel where a code word spans across only one time and frequency coherence interval, the reliability of a point-to-point MIMO link scales according to Prob(link outage) ` SNR-ntnr where nt and nr are the numbers of transmit and receive antennas, respectively, and signal-to-noise ratio is denoted by SNR. On a channel that varies rapidly as a function of time and frequency, and where circumstances permit coding across many channel coherence intervals, the achievable rate scales as min(nt, nr) log(1 + SNR). The gains in multiuser systems are even more impressive, because such systems offer the possibility to transmit simultaneously to several users and the flexibility to select what users to schedule for reception at any given point in time [2].
5,158 citations
01 Dec 1993
TL;DR: In this paper, a computationally efficient technique for blind estimation of directional vectors, based on joint diagonalization of fourth-order cumulant matrices, is presented for beamforming.
Abstract: The paper considers an application of blind identification to beamforming. The key point is to use estimates of directional vectors rather than resort to their hypothesised value. By using estimates of the directional vectors obtained via blind identification, i.e. without knowing the array manifold, beamforming is made robust with respect to array deformations, distortion of the wave front, pointing errors etc., so that neither array calibration nor physical modelling is necessary. Rather suprisingly, ‘blind beamformers’ may outperform ‘informed beamformers’ in a plausible range of parameters, even when the array is perfectly known to the informed beamformer. The key assumption on which blind identification relies is the statistical independence of the sources, which is exploited using fourth-order cumulants. A computationally efficient technique is presented for the blind estimation of directional vectors, based on joint diagonalisation of fourth-order cumulant matrices; its implementation is described, and its performance is investigated by numerical experiments.
2,851 citations
TL;DR: The Cramer-Rao bound (CRB) for the estimation problems is derived, and some useful properties of the CRB covariance matrix are established.
Abstract: The performance of the MUSIC and ML methods is studied, and their statistical efficiency is analyzed. The Cramer-Rao bound (CRB) for the estimation problems is derived, and some useful properties of the CRB covariance matrix are established. The relationship between the MUSIC and ML estimators is investigated as well. A numerical study is reported of the statistical efficiency of the MUSIC estimator for the problem of finding the directions of two plane waves using a uniform linear array. An exact description of the results is included. >
2,552 citations
05 Feb 2014
TL;DR: Measurements and capacity studies are surveyed to assess mmW technology with a focus on small cell deployments in urban environments and it is shown that mmW systems can offer more than an order of magnitude increase in capacity over current state-of-the-art 4G cellular networks at current cell densities.
Abstract: Millimeter-wave (mmW) frequencies between 30 and 300 GHz are a new frontier for cellular communication that offers the promise of orders of magnitude greater bandwidths combined with further gains via beamforming and spatial multiplexing from multielement antenna arrays. This paper surveys measurements and capacity studies to assess this technology with a focus on small cell deployments in urban environments. The conclusions are extremely encouraging; measurements in New York City at 28 and 73 GHz demonstrate that, even in an urban canyon environment, significant non-line-of-sight (NLOS) outdoor, street-level coverage is possible up to approximately 200 m from a potential low-power microcell or picocell base station. In addition, based on statistical channel models from these measurements, it is shown that mmW systems can offer more than an order of magnitude increase in capacity over current state-of-the-art 4G cellular networks at current cell densities. Cellular systems, however, will need to be significantly redesigned to fully achieve these gains. Specifically, the requirement of highly directional and adaptive transmissions, directional isolation between links, and significant possibilities of outage have strong implications on multiple access, channel structure, synchronization, and receiver design. To address these challenges, the paper discusses how various technologies including adaptive beamforming, multihop relaying, heterogeneous network architectures, and carrier aggregation can be leveraged in the mmW context.
2,452 citations