Spatial-Wideband Effect in Massive MIMO with Application in mmWave Systems

Wang, Bolei; Gao, Feifei; Jin, Shi; Lin, Hai; Li, Geoffrey Ye; Sun, Shu; Rappaport, Theodore S.

doi:10.1109/mcom.2018.1701051

Cited by 136 publications

(92 citation statements)

References 14 publications

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“…In terms of the asymptotical angle-delay orthogonality [23], [24], two non-identical paths 1 are asymptotically orthogonal. For finite values of M and P in practice, we can use the following distance to indicate the orthogonality level between two uplink channels as…”

Section: A Uplink User Groupingmentioning

confidence: 99%

“…However, in a system with large-scale antenna arrays, different antennas may receive different time-domain symbols from the same physical path at the same sampling time due to the large propagation delay of electromagnetic waves travelling across the whole antenna array, which is known as the spatial-wideband effect [22]- [24]. In this case, the massive MIMO channel model, which only considers phase difference and ignores delay difference among the received signals at different antennas, are not applicable any more.…”

Section: Introductionmentioning

confidence: 99%

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Beam Squint and Channel Estimation for Wideband mmWave Massive MIMO-OFDM Systems

Wang

Jian

Gao

et al. 2019

IEEE Trans. Signal Process.

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140

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With the increasing scale of antenna arrays in wideband millimeter-wave (mmWave) communications, the physical propagation delays of electromagnetic waves traveling across the whole array will become large and comparable to the time-domain sample period, which is known as the spatial-wideband effect. In this case, different subcarriers in an orthogonal frequency division multiplexing (OFDM) system will "see" distinct angles of arrival (AoAs) for the same path. This effect is known as beam squint, resulting from the spatial-wideband effect, and makes the approaches based on the conventional multipleinput multiple-output (MIMO) model, such as channel estimation and precoding, inapplicable. After discussing the relationship between beam squint and the spatial-wideband effect, we propose a channel estimation scheme for frequency-division duplex (FDD) mmWave massive MIMO-OFDM systems with hybrid analog/digital precoding, which takes the beam squint effect into consideration. A super-resolution compressed sensing approach is developed to extract the frequency-insensitive parameters of each uplink channel path, i.e., the AoA and the time delay, and the frequency-sensitive parameter, i.e., the complex channel gain. With the help of the reciprocity of these frequency-insensitive parameters in FDD systems, the downlink channel estimation can be greatly simplified, where only limited pilots are needed to obtain downlink complex gains and reconstruct downlink channels. Furthermore, the uplink and downlink 2 channel covariance matrices can be constructed from these frequency-insensitive channel parameters rather than through a long-term average, which enables the minimum mean-squared error (MMSE) channel estimation to further enhance performance. Numerical results demonstrate the superiority of the proposed scheme over the conventional methods under general system configurations in mmWave communications.

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Section: A Uplink User Groupingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Beam Squint and Channel Estimation for Wideband mmWave Massive MIMO-OFDM Systems

Wang

Jian

Gao

et al. 2019

IEEE Trans. Signal Process.

Self Cite

140

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show abstract

“…To generate the training dataset, the uplink frequency is randomly selected in [1,3] GHz. The frequency difference between the uplink and the downlink is 120 MHz.…”

Section: A Simulation Setupmentioning

confidence: 99%

“…The acquisition of downlink channel state information (CSI) is a very challenging task for frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems due to the prohibitively high overheads associated with downlink training and uplink feedback [1]- [3]. By exploiting the angular and delay reciprocities between the uplink and the downlink [4]- [6], conventional methods proposed to reduce the downlink training overhead by extracting frequencyindependent information from the uplink CSI, or to reduce the uplink feedback overhead by using compressive sensing based algorithms [7]- [10].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Downlink Channel Prediction for FDD Massive MIMO System

et al. 2019

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Artificial intelligence (AI) based downlink channel state information (CSI) prediction for frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems has attracted growing attention recently. However, existing works focus on the downlink CSI prediction for the users under a given environment and is hard to adapt to users in new environment especially when labeled data is limited. To address this issue, we formulate the downlink channel prediction as a deep transfer learning (DTL) problem, where each learning task aims to predict the downlink CSI from the uplink CSI for one single environment. Specifically, we develop the directtransfer algorithm based on the fully-connected neural network architecture, where the network is trained on the data from all previous environments in the manner of classical deep learning and is then fine-tuned for new environments. To further improve the transfer efficiency, we propose the meta-learning algorithm that trains the network by alternating inner-task and acrosstask updates and then adapts to a new environment with a small number of labeled data. Simulation results show that the direct-transfer algorithm achieves better performance than the deep learning algorithm, which implies that the transfer learning benefits the downlink channel prediction in new environments. Moreover, the meta-learning algorithm significantly outperforms the direct-transfer algorithm in terms of both prediction accuracy and stability, which validates its effectiveness and superiority.

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“…Therefore, estimation of mmWave channels is usually time consuming. Moreover, the mmWave channels are frequency-selective in most of application environments [6]- [10], which brings more challenges for channel estimation.…”

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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Spatial-Wideband Effect in Massive MIMO with Application in mmWave Systems

Cited by 136 publications

References 14 publications

Beam Squint and Channel Estimation for Wideband mmWave Massive MIMO-OFDM Systems

Beam Squint and Channel Estimation for Wideband mmWave Massive MIMO-OFDM Systems

Deep Learning-Based Downlink Channel Prediction for FDD Massive MIMO System

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

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