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Superimposed CSI (Channel State Information) feedback method based on deep learning large-scale MIMO (Multiple Input Multiple Output) system

A deep learning and large-scale technology, applied in baseband systems, baseband system components, transmission systems, etc., can solve problems such as large codebook dimensions, difficulty in application, and occupied spectrum resources, so as to improve recovery accuracy and reduce processing complexity degree of effect

Active Publication Date: 2019-04-26
XIHUA UNIV
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  • Summary
  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The traditional codebook-based CSI scheme has a large number of antennas and requires a huge codebook dimension, which makes it difficult to apply; and the compressed sensing (CS, compressed sensing) feedback technology that utilizes the signal sparsity can reduce the feedback of the system to a certain extent. Overhead, but occupy a certain spectrum resources in the feedback process

Method used

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  • Superimposed CSI (Channel State Information) feedback method based on deep learning large-scale MIMO (Multiple Input Multiple Output) system
  • Superimposed CSI (Channel State Information) feedback method based on deep learning large-scale MIMO (Multiple Input Multiple Output) system
  • Superimposed CSI (Channel State Information) feedback method based on deep learning large-scale MIMO (Multiple Input Multiple Output) system

Examples

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example 1

[0039] Example 1: The step 1) example is as follows:

[0040] Hypothesis: N H = 4, N D =12, K=3, Q=(1+1j, 2+2j, 3+3j),

[0041] Channel state information: H=(0.2+0.3j,0.4+0.5j,0.6+0.7j,0.8+0.9j),

[0042] Uplink data: D=(0,1,1,0,1,1,0,0,1,0,0,1,1,0,0,1,1,1,0,0,0,1, 1,0);

[0043] The channel state information H according to the formula Spreading sequence obtained after spreading:

[0044]

[0045] The uplink modulation sequence obtained after the uplink data D is digitally modulated:

[0046] D. modulate=(-1+1j,1-1j,1+1j,-1-1j,1-1j,-1+1j,1-1j,-1+1j,1+1j,-1-1j,-1+ 1j,1-1j);

[0047] 2) The spreading sequence H spread and uplink modulation sequence D modulate Perform weighted superposition to obtain a length of N D The superposition sequence S of the user terminal transmits the superposition sequence S, and the base station receives the superposition sequence S of length N D The received sequence R;

[0048] The elements of the superposition sequence S and the ...

example 2

[0052] Example 2: The example of step 2) is as follows:

[0053] Assumption: ρ=0.2, E K =100, modulation sequence: D modulate =(1-1j,-1+1j,1+1j),

[0054] Sequence after spreading: H spread =(0.2+0.3j,0.4+0.5j,0.6+0.7j),

[0055] According to the weighted superposition formula The superposition sequence can be computed:

[0056] S=(0.984-7.603j,-7.155+11.180j, 11.628+12.075j);

[0057] Construct model HDNet, described model HDNet comprises channel state information estimation model f H (R) and uplink data detection model f D (R);

[0058] Such as figure 2 Shown, in the embodiment of the present application, described step 3) comprises:

[0059] 3-1) Channel state information estimation model f H (R) contains 1 input layer, m H fully connected layers (m H ≥3), 1 output layer, the number of input layer nodes is 2N D , and the number of nodes in each fully connected layer is The number of nodes in the output layer is 2N H , the fully connected layer uses the Le...

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Abstract

The invention discloses a superimposed CSI (Channel State Information) feedback method based on a deep learning large-scale MIMO (Multiple Input Multiple Output) system. A user side reads a segment ofCSI and a segment of uplink data; spectrum spreading processing is carried out on the CSI to obtain a spread spectrum sequence, and digital modulation is carried out on the uplink data to obtain an uplink modulation sequence; weighted superimposition is carried out on the spread spectrum sequence and the uplink modulation sequence to obtain a superimposed sequence, the user side transmits the superimposed sequence, and a base station side receives to obtain a receiving sequence; models HDNet are constructed, and the models HDNet comprise a CSI estimation model and an uplink data detection model; model parameters are initialized, the models HDNet are subjected to offline training, and after error convergence, the models are stored; and a signal is received online to obtain an online receiving sequence, an estimated value of the CSI is recovered by the trained models HDNet according to the online receiving sequence, and a detection value of the uplink data is detected out. According tothe invention, feedback does not require additional spectrum cost, recovery accuracy of the feedback CSI can be improved, and processing complexity of the system is reduced.

Description

technical field [0001] The present invention relates to the technical field of superimposed channel state information feedback of a massive MIMO (multiple input multiple output) system, in particular to a method for superimposing channel state information feedback of a massive MIMO system based on deep learning. Background technique [0002] As a key technology to meet the high spectral efficiency and energy efficiency of the future 5G (the fifth generation) network, the massive MIMO system can serve more users without increasing the transmission power and system bandwidth through hundreds of antennas deployed at the base station. Provide wireless data service. At the same time, many performance-improving operations in massive MIMO systems (such as multi-user scheduling, rate allocation, and precoding at the transmitter, etc.) depend on the acquisition of accurate downlink channel state information (CSI, channel state information). In a frequency division duplex (FDD, frequ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04B7/06H04L25/02
CPCH04B7/0626H04L25/0204H04L25/0224
Inventor 卿朝进蔡斌阳庆瑶万东琴张岷涛
Owner XIHUA UNIV
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