Two-Step Approach to Time-Domain Channel Estimation for Wideband Millimeter Wave Systems With Hybrid Architecture

Kim, Hyoji; Gil, Gye-Tae; Lee, Yong H.

doi:10.1109/tcomm.2019.2906873

Cited by 18 publications

(12 citation statements)

References 29 publications

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“…Theorem 2 presents that the recovery performance of CS-based algorithm will be improved when the number of nonzero elements is decreased. According to channel model (17), L represents the number of nonzero elements in the virtual channel, so that better performance of channel estimation can be achieved in the scenario of smaller L.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…In order to evaluate the effectiveness of the estimated channels in constructing the precoding and combining matrix for downlink transmission, the achievable spectral efficiency (ASE) gain is also simulated, formulated as R = log 2 |I Nt + 1 Nt R n −1ŴH HFF H H HŴ |, whereF andŴ are the optimal precoder and combiner designed from the estimated channelĤ [17]. Denote the SVD ofĤ asĤ =ÛDV H , whereÛ andV are the right and left singular vector matrices, respectively.D is a diagonal matrix with the corresponding singular values on its diagonal.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…[16] proposes a sparse recovery based channel estimation technique in time domain for hybrid architectures frequency selective mmWave systems, requiring small training overhead. A twostep time domain channel estimator is introduced in [17], where the pulse shaping and desired channel are estimated separately by least square and orthogonal matching pursuit methods to reduce the complexity. The sparsity of mmWave MIMO channel in the angle and delay domains can also be exploited jointly to improve the performance of channel estimation [18].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Channel Estimation for Millimeter Wave Massive MIMO Systems Using Separable Compressive Sensing

et al. 2021

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Channel estimation is a fundamental problem for downlink transmission in millimeter wave (mmWave) multiple input multiple output (MIMO) systems. This paper proposes a channel estimation algorithm by exploiting the separable structured sparsity of mmWave massive MIMO channel. The mmWave downlink channel is firstly formulated as a two dimensional (2D) separable compressive sensing (CS) model according to the sparsity structure of the channel in angle of arrivals (AoAs) and angle of departures (AoDs) domains. Then a separable compressive sampling match pursuit (SCoSaMP) algorithm is proposed to solve the separable CS recovery problem for channel estimation. Based on the separable sparsity structure of the channel, we design the precoding and combining matrices under the metric of mutual information to further improve the performance of channel estimation. Simulations demonstrate the advantages of the proposed algorithm over the traditional CS-based channel estimation methods. INDEX TERMS channel estimation; separable compressive sensing; precoder design; millimeter wave; massive MIMO system

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Section: Numerical Resultsmentioning

confidence: 99%

Section: Numerical Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Channel Estimation for Millimeter Wave Massive MIMO Systems Using Separable Compressive Sensing

et al. 2021

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“…Since frequency selective fading is usually suffered in broadband wireless system, orthogonal frequency division multiplexing (OFDM) technology is often used in downlink transmission. [11] proposed a two-step channel estimation in time domain for broadband mmWave OFDM system. The algorithm firstly estimates the equivalent channels including beamforming, array response, and pulse shaping by least square method.…”

Section: Introductionmentioning

confidence: 99%

“…Then, the channels are recovered by OMP algorithm with low complexity. Different from [10,11], [12] proposed a hybrid algorithm to estimate channel for mmWave MIMO OFDM system by exploiting the sparse structure of the channel in angle domain, which can reduce power leaking in downlink transmission. [13] considers the downlink channel estimation and beamforming in mmWave OFDM system jointly.…”

Section: Introductionmentioning

confidence: 99%

Channel Estimation for Broadband Millimeter Wave MIMO Systems Based on High‐Order PARALIND Model

Jiang

Song

Zhu

et al. 2021

Wireless Communications and Mobile Computing

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Channel state information (CSI) is important to improve the performance of wireless transmission. However, the problems of high propagation path loss, multipath, and frequency selective fading make it difficult to obtain the CSI in broadband millimeter-wave (mmWave) system. Based on the inherent multidimensional structure of mmWave multipath channels and the correlation between channel dimensions, mmWave multiple input multiple output (MIMO) channels are modelled as high-order parallel profiles with linear dependence (PARALIND) model in this paper, and a new PARALIND-based channel estimation algorithm is proposed for broadband mmWave system. Due to the structural property of PARALIND model, the proposed algorithm firstly separates the multipath channels of different scatterers by PARALIND decomposition and then estimates the channel parameters from the factor matrices decomposed from the model based on their structures. Meanwhile, the performance of mmWave channel estimation is analysed theoretically. A necessary condition for channel parameter estimation is given based on the uniqueness principle of PARALIND model. Simulation results show that the proposed algorithm performs better than traditional compressive sensing-based channel estimation algorithms.

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Sensing matrix design for wideband channel estimation in millimeter‐wave hybrid multiple‐input multiple‐output system

Molazadeh

Ardebilipour

Maroufi

2022

Trans Emerging Tel Tech

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Hybrid analog/digital multiple‐input multiple‐output (MIMO) system is proposed to support low complexity multi‐stream communication in millimeter‐wave (mmWave) frequencies. The main idea of channel estimation methods for mmWave hybrid MIMO systems is based on leveraging the sparsity nature of the channel in the angle and delay domain. Sparse signal recovery algorithms have been used to reconstruct the channel coefficients. Due to the lack of information about the angle of arrival and angle of departure before channel estimation, random values are common choices for precoders and combiners. Moreover, the training pilot symbols are considered to be random variables. In this article, we propose sequential time domain sensing matrix design and sequential frequency domain sensing matrix design algorithms to design these variables in order to enhance sparse signal recovery performance and consequently the channel estimation accuracy. Our methods are based on reducing the correlation between the sensing matrix columns, which is defined in the sparse formulation of the channel estimation problem. We design the training radio frequency precoders/combiners phase angle values and pilot symbol vectors with the aid of a sequential procedure that has a reasonable complexity. Simulation results show that utilizing the designed values instead of random variables increased the performance of the channel estimation, especially when we deal with a higher number of variables.

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Two-Step Approach to Time-Domain Channel Estimation for Wideband Millimeter Wave Systems With Hybrid Architecture

Cited by 18 publications

References 29 publications

Channel Estimation for Millimeter Wave Massive MIMO Systems Using Separable Compressive Sensing

Channel Estimation for Millimeter Wave Massive MIMO Systems Using Separable Compressive Sensing

Channel Estimation for Broadband Millimeter Wave MIMO Systems Based on High‐Order PARALIND Model

Sensing matrix design for wideband channel estimation in millimeter‐wave hybrid multiple‐input multiple‐output system

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