Abstract
The precoding scheme based on codebooks is used to save the same set of codebook in advance at the transmitter and the receiver, then, the receiver selects the most appropriate precoding matrix from codebooks according to different channel state information (CSI). Therefore, the design of codebook plays an important role in the performance of the whole scheme. The symmetry-based hybrid precoder and combiner is a highly energy efficient structure in the millimeter-wave massive multiple-input multiple-output (MIMO) system, but at the same time, it also has the problems of high bit error rate and low spectral efficiency. In order to improve the spectral efficiency, we formulate the codebook design as a joint optimization problem and propose an iteration algorithm to obtain the enhanced codebook by combining the compressive sampling matching pursuit (CoSaMP) algorithm with the dictionary learning algorithm. In order to prove the validity of the proposed algorithm, we simulate and analyze the change of the spectral efficiency of the algorithm with the signal-to-noise ratio (SNR) and the number of radio frequency (RF) chains of different precoding schemes. The simulation results demonstrate that the spectral efficiency of the algorithm is obviously outstanding compared with that of the OMP-based joint codebook algorithm and the hybrid precoding algorithm with quantization algorithm under low SNR and different numbers of RF chains. Particularly, when SNR is lower than 0 dB, the proposed algorithm performs very close to the optimal unconstrained precoding algorithm.
Highlights
Millimeter-wave massive multiple-input multiple-output (MIMO), which is very suitable for 5G wireless communication transmission, has been extensively studied in the past ten years [1]
Digital precoding can achieve excellent performance in the system, the digital precoding consumes a lot of energy and the hardware cost is quite expensive, owing to the requirement that each antenna element needs to be connected to one dedicated radio frequency (RF) chain, which is impractical in the mmWave massive MIMO systems
We focus on the design of the hybrid precoding codebook in frequency division duplexing (FDD)-based millimeter-wave massive MIMO systems
Summary
Millimeter-wave massive MIMO, which is very suitable for 5G wireless communication transmission, has been extensively studied in the past ten years [1]. In [5,6], a hybrid precoding scheme based on the partial connection structure is proposed, which reduces the number of phase shifters by connecting each RF chain with partial antennas, effectively reducing the hardware complexity. Compared with partial connection and hybrid connection, fully-connected hybrid precoding has better spectral efficiency performance because all RF chains are connected to each antenna at the transmitter, which can provide higher degree of freedom for the transmitted signal. In recent years, some researchers have explored the use of machine learning and deep learning methods to realize the hybrid precoding algorithm based on full connection structure, in order to reduce the hardware cost. The training cost is high for each update, that is to say, the computational complexity is high
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