Downlink Training Techniques for FDD Massive MIMO Systems: Open-Loop and Closed-Loop Training With Memory

By Junil Choi, D. J. Love, Patrick Bidigare. IEEE Journal on Selected Topics in Signal Processing, 2013

434 citations recorded by the source corpus.

The concept of deploying a large number of antennas at the base station, often called massive multiple-input multiple-output (MIMO), has drawn considerable interest because of its potential ability to revolutionize current wireless communication systems. Most literature on massive MIMO systems assumes time division duplexing (TDD), although frequency division duplexing (FDD) dominates current cellular systems. Due to the large number of transmit antennas at the base station, currently standardized approaches would require a large percentage of the precious downlink and uplink resources in FDD massive MIMO be used for training signal transmissions and channel state information (CSI) feedback. To reduce the overhead of the downlink training phase, we propose practical open-loop and closed-loop training frameworks in this paper. We assume the base station and the user share a common set of training signals in advance. In open-loop training, the base station transmits training signals in a round-robin manner, and the user successively estimates the current channel using long-term channel statistics such as temporal and spatial correlations and previous channel estimates. In closed-loop training, the user feeds back the best training signal to be sent in the future based on channel prediction and the previously received training signals. With a small amount of feedback from the user to the base station, closed-loop training offers better performance in the data communication phase, especially when the signal-to-noise ratio is low, the number of transmit antennas is large, or prior channel estimates are not accurate at the beginning of the communication setup, all of which would be mostly beneficial for massive MIMO systems.

Subfields: Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/f119637cf7c45f3a1c4454d0cb228b5cb065da92

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Citations in the corpus

Toward Massive MIMO 2.0: Understanding Spatial Correlation, Interference Suppression, and Pilot Contamination (2019, methodology): “We refer the reader to [31], [32] for alternative MIMO concepts tailored to frequency-division duplex (FDD) mode1 and to [33], [34] for overviews of MIMO communications with analog or hybrid analog-digital transceivers.”
Deep CNN-Based Channel Estimation for mmWave Massive MIMO Systems (2019, methodology): “According to [26], [27], the temporal correlation between channels in successive coherence intervals can be modeled as Gauss-Markov distribution”
Beam Division Multiple Access Transmission for Massive MIMO Communications (2015, background): “However, in practice, there are system imperfections that limit the performance gains of TDD massive MIMO systems, such as the calibration errors in uplink/downlink RF chains [12].”
Survey on UAV Cellular Communications: Practical Aspects, Standardization Advancements, Regulation, and Security Challenges (2018, background): “This generally entails the need of performing complex signal processing tasks at the analog repeater during the channel estimation procedure [131]–[134].”
Deep Learning for Massive MIMO CSI Feedback (2017, methodology): “We assume that perfect CSI has been acquired through pilot-based training [15] and focus on the feedback scheme.”
Massive MIMO: An Introduction (2015, neutral): “Considerable research is underway to mitigate these limitations [23–30].”
Spatial- and Frequency-Wideband Effects in Millimeter-Wave Massive MIMO Systems (2017, background): “Downlink channel estimation for frequency division duplex (FDD) networks has been investigated in [13]– [15].”
A Unified Transmission Strategy for TDD/FDD Massive MIMO Systems With Spatial Basis Expansion Model (2017, methodology): “In [2], [14], and [15], the closed-loop training schemes were applied to sequentially design the optimal pilot beam patterns.”