Tensor-Based compressed estimation of frequency-selective mmWave MIMO channels

Araújo, Daniel C.; Almeida, André L. F. de

doi:10.1109/camsap.2017.8313186

Cited by 8 publications

(8 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A brief summary of the CS algorithms is given in Table 2, and more details can be found in [60,61]. The opportunities and challenges of applying the CS techniques to 5G are investigated in [49,62,63,64,65,66,67].…”

Section: From Point To Distributed Source Modelsmentioning

confidence: 99%

A survey on 5G massive MIMO localization

Wen

Wymeersch

Peng

et al. 2019

Digital Signal Processing

111

View full text Add to dashboard Cite

show abstract

Section: From Point To Distributed Source Modelsmentioning

confidence: 99%

A survey on 5G massive MIMO localization

Wen

Wymeersch

Peng

et al. 2019

Digital Signal Processing

111

View full text Add to dashboard Cite

show abstract

“…Similarly, but disregarding the angular spreads in the spatial domains, the method in [16] uses the 2D unitary ESPRIT algorithm for spatial parameters estimation, while the path gains are estimated by means of the LS criterion. e researchers [17][18][19][20][21] have proposed CS-based and tensor-based channel estimators for mmWave MIMO systems, respectively. ey assume the conventional channel training framework, where the DL and UL channel estimation problems are treated separately (as two decoupled procedures at the MS and BS, respectively).…”

Section: Introductionmentioning

confidence: 99%

“…However, the adaptive algorithm proposed in [17] can be applied to estimate the DL or UL channel separately. In [19], a layered pilot transmission scheme is proposed to UL channel estimation, while [20,21] exploit the DL communication of wideband mmWave channels. e main idea in [21] is to divide the overall channel estimation problem into three smaller CS subproblems via tensor-based modeling to estimate the channel parameters (AoDs, AoAs, and delays) with less computational complexity.…”

Section: Introductionmentioning

confidence: 99%

“…In [19], a layered pilot transmission scheme is proposed to UL channel estimation, while [20,21] exploit the DL communication of wideband mmWave channels. e main idea in [21] is to divide the overall channel estimation problem into three smaller CS subproblems via tensor-based modeling to estimate the channel parameters (AoDs, AoAs, and delays) with less computational complexity. e system model is formulated as a parallel factors (PARAFAC) decomposition [24], and tensor-based algorithms combined with CS tools are proposed to solve the channel estimation problem.…”

Section: Introductionmentioning

confidence: 99%

“…e system model is formulated as a parallel factors (PARAFAC) decomposition [24], and tensor-based algorithms combined with CS tools are proposed to solve the channel estimation problem. However, Zhou et al [19] only considered the UL channel estimation, while Zhou et al and Araújo and de Almeida [20,21] focused on the DL case. On the other hand, Shen et al [25] proposed a closed-loop based training framework, where the MSs directly feed the received pilots back to the BS without channel estimation.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Joint DL and UL Channel Estimation for Millimeter Wave MIMO Systems Using Tensor Modeling

Gomes

Almeida

Costa

et al. 2019

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

In this paper, we address the problem of joint downlink (DL) and uplink (UL) channel estimation for millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems. Assuming a closed-loop and multifrequency-based channel training framework in which pilot signals received by multiple antenna mobile stations (MSs) are coded and spread in the frequency domain via multiple adjacent subcarriers, we propose two tensor-based semiblind receivers by capitalizing on the multilinear structure and sparse feature of the received signal at the BS equipped with a hybrid analog-digital beamforming (HB) architecture. As a first processing stage, the joint estimation of the compressed DL and UL channel matrices can be obtained in an iterative way by means of an alternating least squares (ALS) algorithm that capitalizes on a parallel factors model for the received signals. Alternatively, for more restricted scenarios, a closed-form solution is also proposed. From the estimated effective channel matrices, the users’ channel parameters such as angles of departure (AoD), angles of arrival (AoA), and path gains are then estimated in a second processing stage by solving independent compressed sensing (CS) problems (one for each MS). In contrast to the classical approach in the literature, in which the DL and UL channel estimation problems are usually considered as two separate problems, our idea is to jointly estimate both the DL and UL channels as a single problem by concentrating most of the processing burden for channel estimation at the BS side. Simulation results demonstrate that the proposed receivers achieve a performance close to the classical approach that is applied on DL and UL communication links separately, with the advantage of avoiding complex computations for channel estimation at the MS side as well as dedicated feedback channels for each MS, which are attractive features for massive MIMO systems.

show abstract

A tensor-based approach for frequency-selective MIMO channel equalization

Forghany,

Ahadi Akhlaghi

2023

Multidim Syst Sign Process

View full text Add to dashboard Cite

Tensor-Based compressed estimation of frequency-selective mmWave MIMO channels

Cited by 8 publications

References 18 publications

A survey on 5G massive MIMO localization

A survey on 5G massive MIMO localization

Joint DL and UL Channel Estimation for Millimeter Wave MIMO Systems Using Tensor Modeling

A tensor-based approach for frequency-selective MIMO channel equalization

Contact Info

Product

Resources

About