Wireless localization for mmWave networks in urban environments

Ruble, Macey; Güvenç, İsmail

doi:10.1186/s13634-018-0556-6

Cited by 26 publications

(36 citation statements)

References 36 publications

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“…Additionally, mmWave non-line of sight (NLOS) paths are not treated as interference, but rather as additional paths that carry useful information [4], [19]. This enables the reflection locations to be estimated from the channel parameters; making simultaneous localization and mapping (SLAM) possible, where the receiver is localized while the environment is mapped in parallel [7], [18].…”

Section: A Literature Reviewmentioning

confidence: 99%

“…3 visualizes T as a series of vector tensor products. The Tucker tensor form in (7) can also be written in a shorthand notation as follows:…”

Section: B Tucker Tensor Formmentioning

confidence: 99%

“…The most obvious capability is channel estimation, since H[k] can be reconstructed from the channel parameters for each subcarrier [6]. Additionally, the channel parameters can be exploited to estimate the receiver position and NLOS path reflection locations [7]. Thus, receiver localization and environmental mapping can be achieved with the knowledge of these parameters, where environmental mapping is obtained when the NLOS path reflection locations are estimated over several measurements.…”

Section: The Channel In Tucker Tensor Formmentioning

confidence: 99%

“…, N s by substituting θ rx,n , θ tx,n , τ n = d n /c and h n for the N p paths into (5). The channel parameters from one LOS path or three NLOS paths are also sufficient for estimating receiver location and orientation as well as mapping the environment [3], [7].…”

Section: Applications Of Channel Parameter Estimationmentioning

confidence: 99%

“…Channel estimation for mmWave is accomplished by transmitting a known training sequence so that the environmental impacts on the channel can be determined and accounted for when unknown data sequences are transmitted [3], [5], [6]. The channel parameters from multiple paths can also be simultaneously utilized to estimate a receiver's position and orientation while also mapping the scatterers in the environment [3], [7]. 5G networks will use orthogonal frequency division multiplexing (OFDM) or OFDM-like waveforms, which enable broadband communication by distributing the transmitted data over many subcarrier frequencies [3], [5], [8], [9].…”

Section: Introductionmentioning

confidence: 99%

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Multilinear Singular Value Decomposition for Millimeter Wave Channel Parameter Estimation

Ruble

Güvenç

2020

IEEE Access

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Fifth generation (5G) cellular standards are set to utilize millimeter wave (mmWave) frequencies, which enable data speeds greater than 10 Gbps and sub-centimeter localization accuracy. These capabilities rely on accurate estimates of the channel parameters, which we define as the angle of arrival, angle of departure, and path distance for each path between the transmitter and receiver. Estimating the channel parameters in a computationally efficient manner poses a challenge because it requires estimation of parameters from a high-dimensional measurement -particularly for multi-carrier systems since each subcarrier must be estimated separately. Additionally, channel parameter estimation must be able to handle hybrid beamforming, which uses a combination of digital and analog beamforming to reduce the number of required analog to digital converters. This paper introduces a channel parameter estimation technique based on the multilinear singular value decomposition (MSVD), a tensor analog of the singular value decomposition, for massive multiple input multiple output (MIMO) multi-carrier systems with hybrid beamforming. The MSVD tensor estimation approach provides a computationally efficient method and is shown to closely match the Cramer-Rao bound (CRB) of parameter estimates through simulations. Limitations of channel parameter estimation and communication waveform effects are also studied.

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Section: A Literature Reviewmentioning

confidence: 99%

“…3 visualizes T as a series of vector tensor products. The Tucker tensor form in (7) can also be written in a shorthand notation as follows:…”

Section: B Tucker Tensor Formmentioning

confidence: 99%

Section: The Channel In Tucker Tensor Formmentioning

confidence: 99%

Section: Applications Of Channel Parameter Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multilinear Singular Value Decomposition for Millimeter Wave Channel Parameter Estimation

Ruble

Güvenç

2020

IEEE Access

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Development Challenges of Millimeter‐Wave 5G Beamformers

Abbasi

2020

Wiley 5G Ref

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Beamforming is the central concept that can make millimetre-wave (mmWave) a viable solution by solving challenges of link-budget, cost, power, form factor, complexity, regulatory constraint and so on. Before deployment of mmWave beamforming solution for 5G, and possibly beyond, several fundamental questions need to be answered. Some of them include hardware constraints, performance matrices, realistic propagation channel models including impairments due to obstacles such as body, blockages due to casings, phase noise, a model for Spatial-temporal variations in channels, and advanced MIMO techniques for multi-carrier and multi-user communications. The site constraints and massive non-line-of-sight transmission within the urban environment are severely questioning the conventional terrestrial low power node deployments that used to work well at low operational frequencies (sub-6 GHz). In addition to this, in beamforming technology using massive antenna arrays, the coordination of the users and beams at the transmitter and receiver end within a large network are very challenging. In this study, we comprehensively investigate some of the most prominent and best practice solutions that attempted to answer these challenging questions.

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Beamformer development challenges for 5G and beyond

Abbasi

Fusco

2020

Antennas and Propagation for 5G and Beyond

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Antenna's beamforming technology [1] is vital in the utilization of the microwave and millimeter-wave (mmWave) frequency bands for the fifth-generation (5G) communication technology, and beyond, applications that include the sixth generation (6G), Internet of Things and Industry 4.0 [2,3]. Antenna beamforming can be defined as the ability of an antenna array to steer the maximum radiation toward a prescribed direction, or, conversely, the ability of the antenna array to estimate the direction of arrival (DOA) of an impinging signal. Beamforming is generally achieved by using multiple antennas, spatially colocated to either function as an independent radiator or as a unit cell in larger array arrangements. Recently, the term multiple-input-multiple-output (MIMO) has been used in conjugation with the beamforming technology since it also involves multiple antenna systems, thus linking it to the beamforming technology [4]. When the number of antennas used in the MIMO operation becomes very large, it is generally termed massive MIMO technology [5]. Different beamforming concepts are used for the two frequency spectrum classifications in cellular technology, i.e., sub-6 GHz and mmWave. The majority of beamformer challenges at sub-6 GHz are already resolved, and prototypes are now available [6]; however, there are still major fundamental challenges that engineers face while developing beamformers for use in the mmWave bands, i.e., >28 GHz. Challenges include, but are not limited to, high path loss, power generation, adaption to account for realistic channel estimation, hardware impairments, energy leakage in circuits, high development cost, spatial-temporal channel variations, signal blockages due to the presence of obstacles such as beamformer casing, user body and hand. Also, multiuser MIMO techniques are not easily implementable in mmWave frequencies. In addition to this, beam coordination at transmitter and receiver antenna systems especially in mmWave spectrum is very challenging. In this chapter, we will first classify the beamformers based on the system architecture, operational frequency

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Wireless localization for mmWave networks in urban environments

Cited by 26 publications

References 36 publications

Multilinear Singular Value Decomposition for Millimeter Wave Channel Parameter Estimation

Multilinear Singular Value Decomposition for Millimeter Wave Channel Parameter Estimation

Development Challenges of Millimeter‐Wave 5G Beamformers

Beamformer development challenges for 5G and beyond

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