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A beam training method for millimeter wave communication based on deep reinforcement learning

A beam training and reinforcement learning technology, applied in the field of millimeter wave wireless communication, can solve the problems of hardware complexity, high training overhead, and high power consumption, and achieve the effect of reducing hardware complexity and overhead.

Active Publication Date: 2022-07-22
SOUTHEAST UNIV
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Problems solved by technology

[0006] In view of this, the object of the present invention is to provide a millimeter wave communication beam training method based on deep reinforcement learning, which introduces a framework of reinforcement learning into the beam training, so that the trained beam can be adjusted in time as the channel changes. The state of the channel is tracked, which effectively reduces the cost of beam training and ensures the performance of beam training. It solves the technical problems of high training cost, hardware complexity and power consumption of the existing beam training method. At the same time, it supports both Communication scenarios with multiple antennas

Method used

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  • A beam training method for millimeter wave communication based on deep reinforcement learning
  • A beam training method for millimeter wave communication based on deep reinforcement learning
  • A beam training method for millimeter wave communication based on deep reinforcement learning

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Embodiment 1

[0071] see Figure 1-Figure 5 , this embodiment provides a millimeter wave communication beam training method based on deep reinforcement learning, which specifically includes:

[0072] Consider a mmWave massive MIMO system for a single user with N at the user r root antenna, with N at the base station t The root antenna and the arrangement of the antennas are placed in the form of a uniform linear array (Uniform Linear Array, ULA). According to the widely used Saleh-Valenzuela model, the mmWave channel for the downlink can be modeled as:

[0073]

[0074] Among them, L, α l , θ l represent the number of paths, the channel gain of the lth path, the arrival angle of the channel, and the departure angle of the channel, respectively. Usually, the path with l=1 is the LOS path, and the other paths are the NLOS path. definition Θ l and Ψ l is the arrival angle and departure angle of the space domain, and both obey the uniform distribution in [0, π]. d t and d r ...

Embodiment 2

[0148] On the basis of Embodiment 1, this embodiment provides a millimeter-wave communication beam training device based on deep reinforcement learning. The device includes:

[0149] Beam selection module, according to the action A performed at time t t The set of beam combinations used for testing at the next moment is obtained as where I represents the total number of beam combinations used for training, Indicates the i-th transmit and receive beam combination.

[0150] The channel sample generation module generates several time-varying channel matrices H according to the random change of the channel steering angle t , use the beam scanning method to determine each channel matrix H t Corresponding best transmit and receive beam combination

[0151] Receive signal matrix module, with set B t+1 The beams in are tested in turn to obtain each beam combination Corresponding received signal strength z t+1 , the received signals corresponding to other untested beam comb...

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Abstract

The invention discloses a beam training method for millimeter wave communication based on deep reinforcement learning. The method defines the specific representation of elements such as states, goals, rewards and other elements in the reinforcement learning model in the actual problem of beam training. Track; define the state as the form of an image, use a convolutional neural network to approximate the value function in reinforcement learning, and define the action as a ternary based on the moving direction, distance and beam coverage of the channel optimal beam combination at the previous moment Group form; when designing the reward function, the effective data reachable rate in a time slice is used as the target value; in the training process of the neural network, the Q-learning method is used to update the network parameters; the trained deep Q network is used for For prediction, select the action with the largest Q value, which corresponds to the beam combination that needs to be tested at the next moment.

Description

technical field [0001] The present invention relates to the technical field of millimeter-wave wireless communication, in particular to a method for training a millimeter-wave communication beam based on deep reinforcement learning. Background technique [0002] With the continuous development of wireless communication technology, almost all of the spectrum resources with lower frequency bands have been occupied. In order to meet the requirements of communication performance and obtain more spectrum resources, people's attention has shifted to the frequency band with a higher frequency band, that is, the millimeter wave frequency band. This frequency band refers to the frequency band in the range of 30 to 300 GHz. The frequency band is rich in spectrum resources and has a high transmission rate, which can meet the needs of some applications with high bandwidth requirements. However, due to the propagation characteristics of millimeter-wave signals, the path loss of millimet...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04B7/06H04B7/08G06N3/04
CPCH04B7/0617H04B7/086G06N3/045
Inventor 戚晨皓姜国力王宇杰
Owner SOUTHEAST UNIV
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