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Deep learning beam domain channel estimation method based on approximate message passing algorithm

An approximate message and deep learning technology, applied in neural learning methods, transmission systems, baseband systems, etc., can solve problems such as insufficient scattering effects and limited number of effective propagation paths

Active Publication Date: 2019-10-22
ZHEJIANG UNIV
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Problems solved by technology

The number of effective propagation paths is limited due to insufficient scattering effects at mmWave frequencies

Method used

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  • Deep learning beam domain channel estimation method based on approximate message passing algorithm
  • Deep learning beam domain channel estimation method based on approximate message passing algorithm
  • Deep learning beam domain channel estimation method based on approximate message passing algorithm

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Embodiment Construction

[0055] In order to make the technical solution and advantages of the present invention clearer, the specific implementation of the technical solution will be described in more detail in conjunction with the accompanying drawings:

[0056] The application scenario of this embodiment is a millimeter-wave massive MIMO system based on a lens antenna. The system model is as follows figure 1 shown. In the millimeter-wave massive MIMO system based on the lens antenna, the base station is used as the receiving end, and a three-dimensional electromagnetic lens is installed, and a 32×32 antenna array is placed on the focal plane. The 1024 antennas are connected to 819 radio frequency chains through a selection network W with a scale of 819×1024, and the number of radio frequency chains is smaller than the number of antennas. The selection network W consists of M columns extracted from a randomly generated 1024×1024 Bernoulli matrix. The deep learning beam domain channel estimation met...

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Abstract

The invention provides a deep learning beam domain channel estimation method based on an approximate message passing algorithm. The deep learning beam domain channel estimation method is mainly applied to a millimeter wave large-scale MIMO system based on a lens antenna. The deep learning beam domain channel estimation method comprises the following steps: (1) constructing a deep network structurewhich is mainly composed of two parts, wherein one part is a model-driven deep network LAMP based on an approximate message passing algorithm, and the other part is a data-driven deep network ResNetbased on residual learning; (2) modeling a beam domain channel according to the geometric structure of the lens antenna, and generating training data according to a system model; (3) training data with different signal-to-noise ratios are used to carry out offline training on the network; and (4) fixing the optimized network parameters, and performing real-time beam domain channel estimation by using the trained network according to the received signal of the radio frequency link end. According to the invention, the precision of beam domain channel estimation can be effectively improved, and meanwhile, the calculation complexity similar to that of a traditional channel estimation algorithm is achieved.

Description

technical field [0001] The invention belongs to the field of wireless communication, and is a deep learning beam domain channel estimation method based on an approximate message passing algorithm. Background technique [0002] With the explosive growth of mobile data demand, the fifth generation (5G) communication network utilizes the abundant spectrum resources of the mmWave band to increase the communication capacity. However, mmWave communication has the disadvantage of large in-band penetration loss, which will lead to severe channel fading. Millimeter-wave massive MIMO systems can utilize large antenna arrays to provide high data rates to compensate for in-band penetration losses. [0003] However, when each antenna is equipped with an RF chain, the implementation of mmWave massive MIMO antenna system will be accompanied by unaffordable hardware complexity and power consumption. An effective way to reduce the implementation complexity is to use advanced lens antenna a...

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Application Information

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IPC IPC(8): H04L25/02H04B7/0413G06N3/04G06N3/08
CPCH04L25/0242H04B7/0413G06N3/08G06N3/045
Inventor 韦逸赵明敏赵民建雷鸣
Owner ZHEJIANG UNIV
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