Millimeter wave communication works in the 30–300 GHz frequency range, and can obtain a very high bandwidth, which greatly improves the transmission rate of the communication system and becomes one of the key technologies of fifth-generation (5G). The smaller wavelength of the millimeter wave makes it possible to assemble a large number of antennas in a small aperture. The resulting array gain can compensate for the path loss of the millimeter wave. Utilizing this feature, the millimeter wave massive multiple-input multiple-output (MIMO) system uses a large antenna array at the base station. It enables the transmission of multiple data streams, making the system have a higher data transmission rate. In the millimeter wave massive MIMO system, the precoding technology uses the state information of the channel to adjust the transmission strategy at the transmitting end, and the receiving end performs equalization, so that users can better obtain the antenna multiplexing gain and improve the system capacity. This paper proposes an efficient algorithm based on machine learning (ML) for effective system performance in mmwave massive MIMO systems. The main idea is to optimize the adaptive connection structure to maximize the received signal power of each user and correlate the RF chain and base station antenna. Simulation results show that, the proposed algorithm effectively improved the system performance in terms of spectral efficiency and complexity as compared with existing algorithms.

In the past ten years, the rapid development of various business systems such as the Internet of Things (IoT) and the Internet of Vehicles (IoV), as well as the advancement of wireless equipment manufacturing processes, have promoted the development and deployment of 5G mobile communication systems with high-speed, large connections and low latency. In general, the improvement of spectrum efficiency is achieved through network densification of micro-cell millimeter wave and massive MIMO technology [

In order to reduce the number and precision of the hardware used, further research on hybrid precoding of partial connection structures has been carried out [

As shown in

The signal received by

The analog precoding of the traditional low-precision phase shifter partial connection structure often fails to achieve the array gain of the millimeter wave large-dimensional antenna. Therefore, this article simulates the precoder adaptive connection and deploy adaptive connection network instead of fixed sub-connection switch and reverse vectorizer (equivalent to a phase shifter with 1-bit quantization). The same as the fixed sub-connection structure, the adaptive connection only requires

Due to the special structure of the adaptive connection and the normal mode constraint of the elements in the analog precoder, the corresponding analog precoding

Indicates the connection relationship between all radio frequency chains and all antennas of the base station. Assuming that

where

It can be seen that

There are only

The constraint

Performing

In the ACN-MLACE algorithm, since the phase shifter is quantized by 1 bit, the non-convex optimization problem of

By initializing the probability distribution parameter

After that, the achievable sum rate

Here, the

There is also

Substituting

Setting

In order to ensure that the adaptive cross entropy optimization converges to the optimal solution to avoid local convergence, a constant smoothing parameter Ӫ can be further added between the current probability distribution and the next probability distribution.

This section provides the simulation results and analysis. The proposed machine learning based precoding algorithm is compared with fully digital precoding, hybrid precoding of adaptive connection structure, and the conventional OMP precoding of structure. The combined precoding has the same lower hardware complexity and eliminates the

Parameter | Value |
---|---|

Distance between ULA |
0.5 |

Number of beam paths |
3 |

Number of transmitter antennas |
1024 |

Number of receiver antennas |
64 |

SNR | 25 dB |

Number of RF chains | 16 |

Number of data streams |
8 |

Number of phase shifter |
40 |

This paper proposes an adaptive connection network hybrid precoding with 1-bit quantization, and applies the adaptive algorithm based on machine learning to the adaptive connection structure hybrid precoding, which improves the 1-bit quantization phase shift of the adaptive connection structure. Under the same low hardware complexity, the proposed solution has a higher computational complexity than the switch and inverter hybrid precoding based on the fixed sub-connection of machine learning and the hybrid precoding based on the adaptive connection structure and achievable rate performance. Recently, highly efficient deep learning methods have been applied to hybrid precoding, and precoding with lower computational complexity and better spectral efficiency is worthy of further research.

Taif University Researchers Supporting Project Number (TURSP-2020/260), Taif University, Taif, Saudi Arabia.