Millimeter wave communications channel estimation via Bayesian group sparse recovery

Suryaprakash, Raj Tejas; Pajovic, Milutin; Kim, Kyeong Jin; Orlik, Phil

doi:10.1109/icassp.2016.7472309

Cited by 13 publications

(15 citation statements)

References 6 publications

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“…The rank of the reconstructed tensor is the estimate for the number of paths and the second term ensures that a minimal number of paths are used for channel parameter estimation. Section IV provides a method that uses the MSVD to estimate the number of significant paths and recover each of the channel parameters as well as the path gain in (13).…”

Section: Problem Formulation For Mmwave Channel Parameter Estimamentioning

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: Problem Formulation For Mmwave Channel Parameter Estimamentioning

confidence: 99%

“…It is known that few significant paths exist in the model from (13), which besides small noise interactions, makes Y a low rank tensor. On the other hand, the MSVD from (16) has a column rank of L rx (number of data streams), a row rank of N T (number of training symbols), and a fiber rank of N s (number of subcarriers).…”

Section: B Rank Reductionmentioning

confidence: 99%

Multilinear Singular Value Decomposition for Millimeter Wave Channel Parameter Estimation

Ruble

Güvenç

2020

IEEE Access

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“…where p(y|h, σ 2 ) is given in (9). When the prior p(h) is chosen such that p(h) ∝ M m |h m | −2 [6], and we compute the logarithm of the posteriori p(h|y, α, σ 2 ), we obtain…”

Section: Non-bayesian Channel Estimationmentioning

confidence: 99%

“…The LASSObased estimator is popular due to its convexity. The Bayesian estimators for estimating the channel in a mmWave-hybrid system have also been recently proposed in [9], [10]. For the Bayesian estimators, a prior probability density function (pdf) of the unknown parameter is specified and the aim is to find a sparse maximum a posteriori (MAP) estimate.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Learning Based Millimeter-Wave Sparse Channel Estimation with Hybrid Antenna Array

Aminu

Codreanu

Juntti

2018

2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

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We consider the problem of millimeter-wave (mmWave) channel estimation with a hybrid digital-analog twostage beamforming structure. A radio frequency (RF) chain excites a dedicated set of antenna subarrays. To compensate for the severe path loss, known training signals are beamformed and swept to scan the angular space. Since the mmWave channels typically exhibit sparsity, the channel response can usually be expressed as a linear combination of a small number of scattering clusters. Thereby the number of angles of arrival (AoAs) and angles of departure (AoDs) with significant signal components is limited, and compressive sensing techniques can be leveraged for estimating the channel. In this paper, we investigate two sparse recovery algorithms: a Bayesian and non-Bayesian one. In the Bayesian approach, we invoke the sparse Bayesian learning (SBL) framework, which relies on a 2-layer hierarchical prior model for channel. A highly efficient and fast iterative Bayesian inference method is then applied to the proposed model. The non-Bayesian approach is a LASSO-based approach, where we devise a low complexity solution by adopting alternating directions method of multipliers (ADMM) technique to solve the problem. The efficacy of the proposed algorithms is demonstrated using numerical examples. The Bayesian approach shows improved estimation performance in relation to the non-Bayesian approach.Index Terms-Millimeter-wave communications, sparse channel estimation, sparse Bayesian learning, compressive sensing.

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“…In this paper, we develop an 802.11ad-based channel estimation procedure that operates at up to one-seventh of the Nyquist rate without significant loss of performance. Although a comprehensive statistical characterization of the 802.11ad channel model is still ongoing, it has been found that it is similar to the well-documented IEEE 802.15.3c channel model [17,18], and can be assumed to be sparse. Our recovery algorithm uses the Xampling framework where Fourier coefficients of the received signal are acquired from their low-rate samples [16].…”

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confidence: 99%

Sub-Nyquist channel estimation over IEEE 802.11ad link

Mishra

Eldar

2017

2017 International Conference on Sampling Theory and Applications (SampTA)

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Nowadays, millimeter-wave communication centered at the 60 GHz radio frequency band is increasingly the preferred technology for near-field communication since it provides transmission bandwidth that is several GHz wide. The IEEE 802.11ad standard has been developed for commercial wireless local area networks in the 60 GHz transmission environment. Receivers designed to process IEEE 802.11ad waveforms employ very high rate analog-to-digital converters, and therefore, reducing the receiver sampling rate can be useful. In this work, we study the problem of low-rate channel estimation over the IEEE 802.11ad 60 GHz communication link by harnessing sparsity in the channel impulse response. In particular, we focus on single carrier modulation and exploit the special structure of the 802.11ad waveform embedded in the channel estimation field of its single carrier physical layer frame. We examine various sub-Nyquist sampling methods for this problem and recover the channel using compressed sensing techniques. Our numerical experiments show feasibility of our procedures up to one-seventh of the Nyquist rates with minimal performance deterioration.

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Millimeter wave communications channel estimation via Bayesian group sparse recovery

Cited by 13 publications

References 6 publications

Multilinear Singular Value Decomposition for Millimeter Wave Channel Parameter Estimation

Multilinear Singular Value Decomposition for Millimeter Wave Channel Parameter Estimation

Bayesian Learning Based Millimeter-Wave Sparse Channel Estimation with Hybrid Antenna Array

Sub-Nyquist channel estimation over IEEE 802.11ad link

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