Abstract
Aiming at the problem of high complexity and low feedback accuracy of existing channel state information (CSI) feedback algorithms for frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems, this paper proposes a CSI compression feedback algorithm based on deep learning (DL), which is suitable for single-user and multi-user scenarios in massive MIMO systems. This algorithm considers the spatial correlation of massive MIMO channel and uses bidirectional long short-term memory (Bi-LSTM) and bidirectional convolutional long short-term memory (Bi-ConvLSTM) network to decompress and recover the CSI for single-user and multi-user, respectively. The proposed DL-based CSI feedback network is trained offline by massive MIMO channel data and could learn the structural characteristics of the massive MIMO channel by fully exploiting the channel information in the training samples. The simulation results show that compared with several classical CSI compression feedback algorithms, the proposed CSI compression feedback algorithm has lower computational complexity, higher feedback accuracy, and better system performance in massive MIMO systems.
Highlights
As a key technology of the fifth generation (5G) communication system, massive multiple-input multiple-output (MIMO) technology has many advantages, such as high spectrum efficiency [1], large system capacity, strong system robustness [2]–[4]
Aiming at the problems of high computational complexity, low feedback accuracy in conventional algorithms and a lack of consideration of spatial correlation between antennas in CsiNet network, this paper proposes a deep learning (DL)-based channel state information (CSI) compression feedback algorithm with low feedback overhead and high feedback accuracy for frequency-division duplexing (FDD) massive MIMO systems, which considers the spatial correlation of massive MIMO channel data
In this paper, the CSI compression feedback algorithm based on spatial correlation for FDD massive MIMO systems is studied
Summary
As a key technology of the fifth generation (5G) communication system, massive multiple-input multiple-output (MIMO) technology has many advantages, such as high spectrum efficiency [1], large system capacity, strong system robustness [2]–[4]. In massive MIMO systems, the channel has a strong spatial correlation because of the use of the massive antenna arrays and the dense deployment of antennas Considering this channel characteristics, compression sensing (CS) or dimensionality reduction technology have been proposed to solve the CSI compressed feedback problems recently. In [18], a DL-based channel estimation and direction-of-arrival (DOA) estimation algorithm for massive MIMO systems is proposed This algorithm uses DNN to effectively learn the statistical characteristics of wireless channels and the spatial structure in the angle domain. Aiming at the problems of high computational complexity, low feedback accuracy in conventional algorithms and a lack of consideration of spatial correlation between antennas in CsiNet network, this paper proposes a DL-based CSI compression feedback algorithm with low feedback overhead and high feedback accuracy for FDD massive MIMO systems, which considers the spatial correlation of massive MIMO channel data.
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