Tensor Decomposition-Aided Time-Varying Channel Estimation for Millimeter Wave MIMO Systems

Cheng, Long; Yue, Guangrong; Xiong, Xinyu; Liang, Yueyue; Li, Shaoqian

doi:10.1109/lwc.2019.2912202

Cited by 21 publications

(11 citation statements)

References 11 publications

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“…For instance, the least squares estimation of the factor matrix

bold-italicD

at each iteration can be expressed as

{\hat{D}}^{()(, i + 1)} = Y_{()(, 1)} Z^{*} {({bold-italicZ}^{normalT} {bold-italicZ}^{*})}^{- 1}

where

bold-italicZ = {\hat{F}}^{()(, i)} ⊙ {\hat{E}}^{()(, i)} \in C^{J N \times K}

is a column full rank matrix since

J N > K

;

Y_{()(, 1)} \in C^{I \times J N}

. Then, the complexity of computing

{\hat{D}}^{()(, i + 1)}

is proportional to

scriptO ()(, I J N K + J N K^{2} + K^{3})

[23, 24]. Considering that K is usually small, the major computational complexity is proportional to

scriptO ()(, I J N)

, namely, the size of the tensor

scriptY

.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Note that the columns of

\overset{false^}{bold-italicD}

\overset{false^}{bold-italicE}

and

\overset{false^}{bold-italicF}

contain the estimated parameters

z_{0, k}

z_{1, k}

and

τ_{k}

, here we assume

z_{0, k} = \sin α_{k} \cos β_{k}

and

z_{1, k} = \sin α_{k} \sin β_{k}

z_{0, k}

and

z_{1, k}

can be obtained via the following correlation‐based method [23]:

{\hat{z}}_{0, k} = \arg \max_{z_{0, k}} \frac{(||, {\hat{d}}_{k}^{H} A_{x} ()(, z_{0, k}))}{{(∥∥, {\overset{false^}{bold-italicd}}_{k})}_{2} {(∥∥, A_{x} ()(, z_{0, k}))}_{2}}

{\hat{z}}_{1, k} = \arg \max_{z_{1, k}} \frac{(||, {\hat{e}}_{k}^{H} A_{y} ()(, z_{1, k}))}{{(∥∥, {\overset{false^}{bold-italice}}_{k})}_{2} (∥∥)}

…”

Section: Cp‐decomposition‐based Positioning Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Tensor decomposition‐based 3D positioning with a single‐antenna receiver in 5G millimetre wave systems

et al. 2020

View full text Add to dashboard Cite

“…For instance, the least squares estimation of the factor matrix

bold-italicD

at each iteration can be expressed as

{\hat{D}}^{()(, i + 1)} = Y_{()(, 1)} Z^{*} {({bold-italicZ}^{normalT} {bold-italicZ}^{*})}^{- 1}

where

bold-italicZ = {\hat{F}}^{()(, i)} ⊙ {\hat{E}}^{()(, i)} \in C^{J N \times K}

is a column full rank matrix since

J N > K

;

Y_{()(, 1)} \in C^{I \times J N}

. Then, the complexity of computing

{\hat{D}}^{()(, i + 1)}

is proportional to

scriptO ()(, I J N K + J N K^{2} + K^{3})

[23, 24]. Considering that K is usually small, the major computational complexity is proportional to

scriptO ()(, I J N)

, namely, the size of the tensor

scriptY

.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Note that the columns of

\overset{false^}{bold-italicD}

\overset{false^}{bold-italicE}

and

\overset{false^}{bold-italicF}

contain the estimated parameters

z_{0, k}

z_{1, k}

and

τ_{k}

, here we assume

z_{0, k} = \sin α_{k} \cos β_{k}

and

z_{1, k} = \sin α_{k} \sin β_{k}

z_{0, k}

and

z_{1, k}

can be obtained via the following correlation‐based method [23]:

{\hat{z}}_{0, k} = \arg \max_{z_{0, k}} \frac{(||, {\hat{d}}_{k}^{H} A_{x} ()(, z_{0, k}))}{{(∥∥, {\overset{false^}{bold-italicd}}_{k})}_{2} {(∥∥, A_{x} ()(, z_{0, k}))}_{2}}

{\hat{z}}_{1, k} = \arg \max_{z_{1, k}} \frac{(||, {\hat{e}}_{k}^{H} A_{y} ()(, z_{1, k}))}{{(∥∥, {\overset{false^}{bold-italice}}_{k})}_{2} (∥∥)}

…”

Section: Cp‐decomposition‐based Positioning Methodsmentioning

confidence: 99%

Tensor decomposition‐based 3D positioning with a single‐antenna receiver in 5G millimetre wave systems

et al. 2020

View full text Add to dashboard Cite

“…2) CRLBs of Doppler Shift and Path Delay Estimation: Similar to the CRLB derivations of angle estimation, according to (25) and (29), the CRLBs of virtual Doppler ν ψ l and virtual delay µ τ l can be obtained directly as ( 44) and ( 45), respectively, on the top of this page. In (44) and (45), the projection operators Φ Do and Φ De have the similar form to Φ BS .…”

Section: Performance Analysismentioning

confidence: 99%

“…By exploiting the sparsity of the virtual channel vector in angle domain, the virtual channel parameters based on first order auto regressive model were estimated and tracked using the expectation maximization-based sparse Bayesian learning framework in [22], [23]. Moreover, by acquiring the dominant channel parameters including the Angle of Arrivals/Departures (AoAs/AoDs), Doppler shifts, and channel gains, rather than the complete MIMO channel matrix, some multi-stage channel estimation solutions were proposed in [24], [25] enabling fast channel tracking for narrow-band mmWave MIMO systems. Note that these schemes above just consider the channel estimation and tracking for common mmWave systems.…”

Section: Introductionmentioning

confidence: 99%

Terahertz Ultra-Massive MIMO-Based Aeronautical Communications in Space-Air-Ground Integrated Networks

Liao

Gao

Wang

et al. 2021

IEEE J. Select. Areas Commun.

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The emerging space-air-ground integrated network has attracted intensive research and necessitates reliable and efficient aeronautical communications. This paper investigates terahertz Ultra-Massive (UM)-MIMO-based aeronautical communications and proposes an effective channel estimation and tracking scheme, which can solve the performance degradation problem caused by the unique triple delay-beam-Doppler squint effects of aeronautical terahertz UM-MIMO channels. Specifically, based on the rough angle estimates acquired from navigation information, an initial aeronautical link is established, where the delay-beam squint at transceiver can be significantly mitigated by employing a Grouping True-Time Delay Unit (GTTDU) module (e.g., the designed Rotman lens-based GTTDU module). According to the proposed prior-aided iterative angle estimation algorithm, azimuth/elevation angles can be estimated, and these angles are adopted to achieve precise beam-alignment and refine GTTDU module for further eliminating delay-beam squint. Doppler shifts can be subsequently estimated using the proposed prior-aided iterative Doppler shift estimation algorithm. On this basis, path delays and channel gains can be estimated accurately, where the Doppler squint can be effectively attenuated via compensation process. For data transmission, a data-aided decision-directed based channel tracking algorithm is developed to track the beam-aligned effective channels. When the dataaided channel tracking is invalid, angles will be re-estimated at the pilot-aided channel tracking stage with an equivalent sparse digital array, where angle ambiguity can be resolved based on the previously estimated angles. The simulation results and the derived Cramér-Rao lower bounds verify the effectiveness of our solution.Index Terms-Terahertz communications, aeronautical communications, ultra-massive MIMO, channel estimation and tracking, space-air-ground integrated network.The codes and some other associated materials of this work may be available at https://gaozhen16.github.io.

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“…The angle estimates were used to design pilot beamforming for estimating the path gains. For the same assumption of slower variations in angle than path gains, the authors in [93] proposed a two-stage tensor decomposition based method for a single receiver. Doppler shift estimation was achieved based on the estimated angles.…”

Section: Time-varying Channel Modelingmentioning

confidence: 99%

Tensor Decompositions in Wireless Communications and MIMO Radar

Chen

Ahmad

Vorobyov

et al. 2021

IEEE J. Sel. Top. Signal Process.

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The emergence of big data and the multidimensional nature of wireless communication signals present significant opportunities for exploiting the versatility of tensor decompositions in associated data analysis and signal processing. The uniqueness of tensor decompositions, unlike matrix-based methods, can be guaranteed under very mild and natural conditions. Harnessing the power of multilinear algebra through tensor analysis in wireless signal processing, channel modeling, and parametric channel estimation provides greater flexibility in the choice of constraints on data properties and permits extraction of more general latent data components than matrix-based methods. Tensor analysis has also found applications in Multiple-Input Multiple-Output (MIMO) radar because of its ability to exploit the inherent higher-dimensional signal structures therein. In this paper, we provide a broad overview of tensor analysis in wireless communications and MIMO radar. More specifically, we cover topics including basic tensor operations, common tensor decompositions via canonical polyadic and Tucker factorization models, wireless communications applications ranging from blind symbol recovery to channel parameter estimation, and transmit beamspace design and target parameter estimation in MIMO radar.

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Tensor Decomposition-Aided Time-Varying Channel Estimation for Millimeter Wave MIMO Systems

Cited by 21 publications

References 11 publications

Tensor decomposition‐based 3D positioning with a single‐antenna receiver in 5G millimetre wave systems

Tensor decomposition‐based 3D positioning with a single‐antenna receiver in 5G millimetre wave systems

Terahertz Ultra-Massive MIMO-Based Aeronautical Communications in Space-Air-Ground Integrated Networks

Tensor Decompositions in Wireless Communications and MIMO Radar

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