Time-domain channel estimation for wideband millimeter wave systems with hybrid architecture

Venugopal, Kiran; Alkhateeb, Ahmed; Heath, Robert W.; González–Prelcic, Nuria

doi:10.1109/icassp.2017.7953407

Cited by 61 publications

(90 citation statements)

References 17 publications

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“…Note that the CENN is trained to predict the beamspace channel amplitude, where the training process of the CENN is independent of J. The proposed DLCS channel estimation scheme is compared with the existing OMP [5] and DGMP [4] channel estimation schemes, while the proposed DLQP hybrid precoder design method is compared with the existing QALS [14] hybrid precoder design method. We also compare the DLQP method with the Exhaustion hybrid precoder design method, i.e., we generate the analog precoding matrix F R for 30, 000 times, where each entry of F R is randomly drawn from the set {e j2πn/Q , n = 1, 2, .…”

Section: Simulation Resultsmentioning

confidence: 99%

Sparse Channel Estimation and Hybrid Precoding Using Deep Learning for Millimeter Wave Massive MIMO

Zhang

et al. 2020

IEEE Trans. Commun.

179

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Channel estimation and hybrid precoding are considered for multi-user millimeter wave massive multi-input multioutput system. A deep learning compressed sensing (DLCS) channel estimation scheme is proposed. The channel estimation neural network for the DLCS scheme is trained offline using simulated environments to predict the beamspace channel amplitude. Then the channel is reconstructed based on the obtained indices of dominant beamspace channel entries. A deep learning quantized phase (DLQP) hybrid precoder design method is developed after channel estimation. The training hybrid precoding neural network for the DLQP method is obtained offline considering the approximate phase quantization. Then the deployment hybrid precoding neural network (DHPNN) is obtained by replacing the approximate phase quantization with ideal phase quantization and the output of the DHPNN is the analog precoding vector. Finally, the analog precoding matrix is obtained by stacking the analog precoding vectors and the digital precoding matrix is calculated by zero-forcing. Simulation results demonstrate that the DLCS channel estimation scheme outperforms the existing schemes in terms of the normalized mean-squared error and the spectral efficiency, while the DLQP hybrid precoder design method has better spectral efficiency performance than other methods with low phase shifter resolution.

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Section: Simulation Resultsmentioning

confidence: 99%

Sparse Channel Estimation and Hybrid Precoding Using Deep Learning for Millimeter Wave Massive MIMO

Zhang

et al. 2020

IEEE Trans. Commun.

179

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“…7 (a). As a comparison, we also simulate a recent time-domain BA algorithm proposed in [42], which focuses on estimating the instantaneous channel coefficients with an orthogonal matching pursuit (OMP) technique. As we can see, the proposed BA scheme requires much less training overhead than that in [42].…”

Section: B Fully-connected (Fc) or One-stream-per-subarray (Osps)?mentioning

confidence: 99%

Fully-/Partially-Connected Hybrid Beamforming Architectures for mmWave MU-MIMO

Song

Kühne

Caire

2020

IEEE Trans. Wireless Commun.

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Hybrid digital analog (HDA) beamforming has attracted considerable attention in practical implementation of millimeter wave (mmWave) multiuser multiple-input multiple-output (MU-MIMO) systems due to the low power consumption with respect to its fully digital baseband counterpart. The implementation cost, performance, and power efficiency of HDA beamforming depends on the level of connectivity and reconfigurability of the analog beamforming network. In this paper, we investigate the performance of two typical architectures that can be regarded as extreme cases, namely, the fully-connected (FC) and the one-stream-per-subarray (OSPS) architectures. In the FC architecture each RF antenna port is connected to all antenna elements of the array, while in the OSPS architecture the RF antenna ports are connected to disjoint subarrays. We jointly consider the initial beam acquisition and data communication phases, such that the latter takes place by using the beam direction information obtained by the former. We use the state-of-the-art beam alignment (BA) scheme previously proposed by the authors and consider a family of MU-MIMO precoding schemes well adapted to the beam information extracted from the BA phase. We also evaluate the power efficiency of the two HDA architectures taking into account the power dissipation at different hardware components as well as the power backoff under typical power amplifier constraints. Numerical results show that the two architectures achieve similar sum spectral efficiency, while the OSPS architecture is advantageous with respect to the FC case in terms of hardware complexity and power efficiency, at the sole cost of a slightly longer BA time-to-acquisition due to its reduced beam angle resolution. Index TermsX.

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“…We use all K subcarriers to transmit pilot sequences for T time slots. Therefore (16) can be rewritten as…”

Section: A Aod Estimation For the Ems-based Schemementioning

confidence: 99%

“…Recent work demonstrates that the mmWave massive MIMO channels are actually frequency-selective. In [16], a channel estimation scheme based on orthogonal matching pursuit (OMP) is proposed by exploring the sparsity of beamspace channels. A channel estimation scheme based on simultaneous weighted orthogonal matching pursuit (SWOMP) in [6] whitens the spatial noise components to improve the estimation accuracy using the OMP method.…”

Section: Introductionmentioning

confidence: 99%

High-Resolution Channel Estimation for Frequency-Selective mmWave Massive MIMO Systems

2020

IEEE Trans. Wireless Commun.

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In this paper, we develop two high-resolution channel estimation schemes based on the estimating signal parameters via the rotational invariance techniques (ESPRIT) method for frequency-selective millimeter wave (mmWave) massive MIMO systems. The first scheme is based on two-dimensional ESPRIT (TDE), which includes three stages of pilot transmission. This scheme first estimates the angles of arrival (AoA) and angles of departure (AoD) and then pairs the AoA and AoD. The other scheme reduces the pilot transmission from three stages to two stages and therefore reduces the pilot overhead. It is based on one-dimensional ESPRIT and minimum searching (EMS). It first estimates the AoD of each channel path and then searches the minimum from the identified mainlobe. To guarantee the robust channel estimation performance, we also develop a hybrid precoding and combining matrices design method so that the received signal power keeps almost the same for any AoA and AoD. Finally, we demonstrate that the proposed two schemes outperform the existing channel estimation schemes in terms of computational complexity and performance.Index Terms-Millimeter wave communications, channel estimation, hybrid precoding, massive MIMO.

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Time-domain channel estimation for wideband millimeter wave systems with hybrid architecture

Cited by 61 publications

References 17 publications

Sparse Channel Estimation and Hybrid Precoding Using Deep Learning for Millimeter Wave Massive MIMO

Sparse Channel Estimation and Hybrid Precoding Using Deep Learning for Millimeter Wave Massive MIMO

Fully-/Partially-Connected Hybrid Beamforming Architectures for mmWave MU-MIMO

High-Resolution Channel Estimation for Frequency-Selective mmWave Massive MIMO Systems

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