Abstract
In a frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) system, the acquisition of downlink channel state information (CSI) at base station (BS) is a very challenging task due to the overwhelming overheads required for downlink training and uplink feedback. In this paper, we reveal a deterministic uplink-to-downlink mapping function when the position-to-channel mapping is bijective. Motivated by the universal approximation theorem, we then propose a sparse complex-valued neural network (SCNet) to approximate the uplink-to-downlink mapping function. Different from general deep networks that operate in the real domain, the SCNet is constructed in the complex domain and is able to learn the complex-valued mapping function by off-line training. After training, the SCNet is used to directly predict the downlink CSI based on the estimated uplink CSI without the need of either downlink training or uplink feedback. Numerical results show that the SCNet achieves better performance than general deep networks in terms of prediction accuracy and exhibits remarkable robustness over complicated wireless channels, demonstrating its great potential for practical deployments.
Highlights
M ASSIVE multiple-input multiple-output (MIMO) has been widely recognized as a promising technique in future wireless communication systems for its high spectrum and energy efficiency, high spatial resolution, and largeManuscript received July 12, 2019; revised August 8, 2019; accepted August 9, 2019
2) We propose a sparse complex-valued neural network (SCNet) for downlink channel state information (CSI) prediction in frequency division duplexing (FDD) massive MIMO systems, which is applicable to complex-valued function approximation with complexvalued representations
We revealed the existence of a deterministic uplink-to-downlink mapping function for a given communication environment
Summary
M ASSIVE multiple-input multiple-output (MIMO) has been widely recognized as a promising technique in future wireless communication systems for its high spectrum and energy efficiency, high spatial resolution, and large. The acquisition of downlink CSI is a very challenging task for frequency division duplexing (FDD) massive MIMO systems due to the prohibitively high overheads associated with downlink training and uplink feedback. Several CS-based channel feedback schemes for massive MIMO have been proposed to reduce the feedback overhead but are sensitive to the model errors and suffer from high complexity. In [12], a fully-connected neural network (FNN) is trained for uplink/downlink channel calibration for massive MIMO systems. We propose a sparse complex-valued neural network (SCNet) for the downlink CSI prediction in FDD massive MIMO systems. 2) We propose a SCNet for downlink CSI prediction in FDD massive MIMO systems, which is applicable to complex-valued function approximation with complexvalued representations. 3) Experiment results demonstrate that SCNet outperforms the FNN of [12] in terms of prediction accuracy and exhibits remarkable robustness over the number of paths
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