Channel Prediction for Millimeter Wave MIMO-OFDM Communications in Rapidly Time-Varying Frequency-Selective Fading Channels

Abstract

The millimeter wave (mmWave) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems communicate at the extremely high-frequency band. In the extremely high band, the channel state information (CSI) from channel estimation will be outdated quickly, and herein, seriously degrading the system performance. In this paper, we focus on the channel prediction to obtain prior CSI in mmWave MIMO-OFDM systems. First, the mmWave MIMO-OFDM channel is categorized and represented in four domains: the array-frequency, array-time, angle-frequency, as well as angle-time. Then, for the above four domains, we investigate the effects of the channel representations on channel prediction, and analyze the mean-squared error performance as well as the computational complexity of the investigated prediction methods. We derive that the angle-time-domain prediction method achieves higher accuracy than the other three prediction techniques. In addition, we propose an enhanced angle-timedomain channel predictor by exploiting the spatial-time sparsity of the MIMO-OFDM channel to further improve the prediction accuracy. Finally, the simulation results confirm the statistical analysis and verify the superiority of the proposed predictors.INDEX TERMS Channel prediction, channel representations, millimeter wave, sparse channel, MIMO-OFDM systems.

CSI Calibration for Precoding in mmWave Massive MIMO Downlink Transmission Using Sparse Channel Prediction

“…Meanwhile, in each channel tap, different physical paths (sinusoids) contribute to different angle domain bins, and thus the channel coefficients in different angle domain bins are spatially uncorrelated. Therefore, the Ag-TD channel elements are uncorrelated, and the channel prediction in the Ag-TD achieves higher accuracy than the other three domain prediction techniques without exploiting channel correlations [11]. In this paper, an enhanced Ag-TD channel predictor which exploits the channel sparsity in both the angle and the time domains is employed to predict the channels.…”

“…where I is the identity matrix, R g a φ,l is the channel autocorrelation matrix, and r g a φ,l is the channel auto-correlation vector, as defined in [11].…”

“…This challenge will become more pronounced as the carrier frequency grows. In TDD systems, for downlink transmission, CSI can only be obtained by estimating uplink channel, thus resulting in unavoidable outdated problem [11], which will seriously degrade the performance of precoding and coherent signal detection.…”

“…For the MIMO-OFDM systems, the previously proposed prediction methods operate on each subcarrier in each antenna pair of MIMO-OFDM channel [6], [21]. [11] comprehensively investigates the channel prediction in different domains for mmWave Massive MIMO systems.…”

“…The proposed scheme utilizes the predicted channel coefficients instead of estimated channel coefficients to overcome the CSI delay problem. • The channel prediction and interpolation are performed in the Ag-TD, as the Ag-TD channel is composed of fewer physical paths and thus is more predictable than that in the other domains [11]. The channel sparsity in both the angle and time domains is also exploited for channel prediction to eliminate the noise perturbation and to reduce the computational burden.…”