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A machine learning assisted large scale MIMO downlink user scheduling method

A machine learning and user scheduling technology, applied in radio transmission systems, electrical components, transmission systems, etc., can solve problems with high computational complexity and high complexity

Active Publication Date: 2018-12-11
SOUTHEAST UNIV
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Problems solved by technology

However, user scheduling based on statistical CSI requires computation and rate expectations, and the complexity is high
Although some existing methods can be used as approximations for calculation and rate, the computational complexity is still high in practical applications

Method used

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  • A machine learning assisted large scale MIMO downlink user scheduling method
  • A machine learning assisted large scale MIMO downlink user scheduling method
  • A machine learning assisted large scale MIMO downlink user scheduling method

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Embodiment Construction

[0069] The technical solution of the present invention will be further introduced below in combination with specific embodiments.

[0070] This specific embodiment discloses a machine learning-assisted large-scale MIMO downlink user scheduling method, such as figure 1 shown, including the following steps:

[0071] S1: The base station obtains the eigenmode energy coupling matrix in the eigendirection through the uplink sounding signal sent by the user;

[0072] S2: The base station uses the eigenmode energy coupling matrix to assist in the calculation of the sum rate under various user and beam combinations through the method of machine learning;

[0073] S3: Use the greedy algorithm to implement user scheduling with the maximum rate criterion, and obtain the optimal user beam pairing combination.

[0074] The eigenmode energy coupling matrix in step S1 is calculated by formula (1):

[0075]

[0076] In formula (1), is the eigenmode energy coupling matrix of the kth us...

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Abstract

The invention discloses a large-scale MIMO downlink user scheduling method assisted by machine learning, which comprises the following steps: S1, a base station obtains a characteristic mode energy coupling matrix in a characteristic direction through an uplink detection signal sent by a user; S2, the base station calculates the sum rate under various user and beam combinations by using the eigenmode energy coupling matrix and the machine learning method; S3: the greedy algorithm is used to realize the user scheduling with the sum rate maximization criterion, and the optimal user beam pairingcombination is obtained. The invention obtains the statistical channel information through the uplink detection signal, and adopts the sum rate maximization criterion to carry out the user scheduling.Under the condition that the base station only has statistical channel information, the user scheduling complexity under large-scale antennas can be greatly reduced by the targeted feature extractionand the design of neural network, and the performance is close to the optimal, so the method has good applicability and robustness.

Description

technical field [0001] The invention relates to a massive MIMO downlink user scheduling method. Background technique [0002] With the extensive research of massive multiple-input multiple-output (MIMO) wireless communication systems, the spectral and radiated energy efficiency can be improved by deploying a large number of antennas. In a multi-user system, a base station can use the same time-frequency physical resource block to communicate with multiple mobile terminals by being equipped with a large number of antennas. [0003] The throughput of a massive MIMO system depends on the availability of channel state information (CSI) at the base station of the system. In the time division duplex system, the base station uses the reciprocity of the channel to obtain the channel information of the downlink through uplink pilot training. However, the pilot overhead is linearly related to the total number of antennas. When the number of users is large or the user end is configur...

Claims

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Application Information

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IPC IPC(8): H04W72/12H04B7/0413
CPCH04B7/0413H04W72/12
Inventor 高西奇王闻今是钧超熊佳媛洪姝
Owner SOUTHEAST UNIV
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