Channel Prediction in High-Mobility Massive MIMO: From Spatio-Temporal Autoregression to Deep Learning

Wu, Chi; Yi, Xinping; Zhu, Yiming; Wang, Wenjin; You, Li; Gao, Xiqi

doi:10.1109/jsac.2021.3078503

Cited by 59 publications

(49 citation statements)

References 35 publications

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“…The condition L = 1, N L = 2 gives the lower bound of matrix pencil parameter configuration, which means that only one pole needs to be estimated, i.e., M = 1. Then, the UL channel (20) becomes…”

Section: Appendix C Proof Of Theoremmentioning

confidence: 99%

“…In [15], the authors proposed a channel prediction method to solve the the mobility problem utilizing Prony-based angle-delay domain channel prediction. The authors of [20] addressed the mobility problem in massive MIMO from a deep learning view. Nevertheless these papers mainly focused on TDD mode.…”

Section: Introductionmentioning

confidence: 99%

“…Worse still, the training and feedback overhead are much heavier than TDD, and thus has to be reduced. The state-of-the-art algorithms like CS [10], deep learning [20] and JSDM [8] method mainly focus on reducing the pilot training and feedback overhead. Some research works utilized maximum-likelihood method [17], deep learning [20] and machine learning method [21] to address the high mobility problem in TDD.…”

Section: Introductionmentioning

confidence: 99%

“…The state-of-the-art algorithms like CS [10], deep learning [20] and JSDM [8] method mainly focus on reducing the pilot training and feedback overhead. Some research works utilized maximum-likelihood method [17], deep learning [20] and machine learning method [21] to address the high mobility problem in TDD. However, these methods did not consider the mobility problem in FDD and even the given solutions were NP-hard.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Partial Reciprocity-based Channel Prediction Framework for FDD Massive MIMO with High Mobility

Qin,

Yin,

Cao

et al. 2022

Preprint

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Massive multiple-input multiple-output (MIMO) is believed to deliver unrepresented spectral efficiency gains for 5G and beyond. However, a practical challenge arises during its commercial deployment, which is known as the "curse of mobility". The performance of massive MIMO drops alarmingly when the velocity level of user increases. In this paper, we tackle the problem in frequency division duplex (FDD) massive MIMO with a novel Channel State Information (CSI) acquisition framework. A joint angle-delay-Doppler (JADD) wideband beamformer is proposed for channel training. Our idea consists in the exploitation of the partial channel reciprocity of FDD and the angle-delay-Doppler channel structure. More precisely, the base station (BS) estimates the angle-delay-Doppler information of the UL channel based on UL pilots using Matrix Pencil method. It then computes the wideband JADD beamformers according to the extracted parameters. Afterwards, the user estimates and feeds back some scalar coefficients for the BS to reconstruct the predicted DL channel. Asymptotic analysis shows that the CSI prediction error converges to zero when the number of BS antennas and the bandwidth increases. Numerical results with industrial channel model demonstrate that our framework can well adapt to high speed (350 km/h), large CSI delay (10 ms) and channel sample noise.

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Section: Appendix C Proof Of Theoremmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Partial Reciprocity-based Channel Prediction Framework for FDD Massive MIMO with High Mobility

Qin,

Yin,

Cao

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…As a prevailing approach to AI, deep learning (DL) is an efficient method to analyze data by identifying patterns and learning underlying structures, denoting an effective approach to problems faced in various scientific fields. DL algorithms have been integrated into the physical layer of wireless communications systems [18]- [20], including channel estimation [21]- [26]. In turn, this is attributable to the great success in enhancing the overall system performance, particularly when used in addition to conventional estimators, where coarse channel estimation is derived from conventional estimators, following which DL is employed to achieve a fine estimation.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Deep Learning Based Channel Estimation in Doubly Dispersive Environments

Gizzini

Chafii

2022

IEEE Access

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Wireless communications systems are impacted by multi-path fading and Doppler shift in dynamic environments, where the channel becomes doubly-dispersive and its estimation becomes an arduous task. Only a few pilots are used for channel estimation in conventional approaches to preserve high data rate transmission. Consequently, such estimators experience a significant performance degradation in high mobility scenarios. Recently, deep learning has been employed for doubly-dispersive channel estimation due to its low-complexity, robustness, and good generalization ability. Against this backdrop, the current paper presents a comprehensive survey on channel estimation techniques based on deep learning by deeply investigating different methods. The study also provides extensive experimental simulations followed by a computational complexity analysis. After considering different parameters such as modulation order, mobility, frame length, and deep learning architecture, the performance of the studied estimators is evaluated in several mobility scenarios. In addition, the source codes are made available online in order to make the results reproducible.

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Analysis of Path Prediction Error for a High-Mobility Massive MIMO-OTFS System

Ying,

2024

Lecture Notes in Networks and Systems

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Channel Prediction in High-Mobility Massive MIMO: From Spatio-Temporal Autoregression to Deep Learning

Cited by 59 publications

References 35 publications

A Partial Reciprocity-based Channel Prediction Framework for FDD Massive MIMO with High Mobility

A Partial Reciprocity-based Channel Prediction Framework for FDD Massive MIMO with High Mobility

A Survey on Deep Learning Based Channel Estimation in Doubly Dispersive Environments

Analysis of Path Prediction Error for a High-Mobility Massive MIMO-OTFS System

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