Device and method for channel estimation using short/long-term memory network in millimeter-wave communication system

Patent metadata

Field	Value
Publication number	US-2024195662-A1
Application number	US-202418584561-A
Country	US
Kind code	A1
Filing date	Feb 22, 2024
Priority date	Aug 26, 2021
Publication date	Jun 13, 2024
Grant date	—

Abstract

A device and a method for channel estimation using a short/long-term memory network in a millimeter-wave (mmWave) communication system are provided. The channel estimation method includes the operations of inputting a received pilot signal of a time slot to a long short-term memory network, extracting a time-varying channel feature embedding vector by estimating a change state of a channel by using the received pilot signal of the time slot as an input in the long short-term memory network, estimating a parameter of a channel model by using the time-varying channel feature embedding vector as an input in a fully connected network, and estimating a channel for the received pilot signal of the time slot, using the parameter of the channel model.

First claim

What is claimed is:

1 . A channel estimation method, the channel estimation method comprising:
inputting a received pilot signal of a time slot to a long short-term memory network; extracting a time-varying channel feature embedding vector by estimating a change state of a channel by using the received pilot signal of the time slot as an input in the long short-term memory network; estimating a parameter of a channel model by using the time-varying channel feature embedding vector as an input in a fully connected network; and estimating a channel for the received pilot signal of the time slot, using the parameter of the channel model.

2 . The channel estimation method of claim 1 , wherein the parameter of the channel model comprises a departure angle, an arrival angle, a path delay, and a path gain.

3 . The channel estimation method of claim 1 , wherein the received pilot signal of the time slot is converted into a real number and input to the long short-term memory network.

4 . The channel estimation method of claim 1 , wherein the extracting of the time-varying channel feature embedding vector by estimating the change state of the channel by using the received pilot signal of the time slot as the input in the long short-term memory network comprises extracting the time-varying channel feature embedding vector using the received pilot signal of the time slot, a final state information cell of the long short-term memory network of a previous time slot, and an output of the long short-term memory network of the previous time slot in the long short-term memory network.

5 . The channel estimation method of claim 1 , wherein the extracting of the time-varying channel feature embedding vector by estimating the change state of the channel by using the received pilot signal of the time slot as the input in the long short-term memory network comprises:
calculating an output of an input gate that determines a degree to which a candidate state information cell is reflected in a final state information cell based on the received pilot signal of the time slot and an output of the long short-term memory network of a previous time slot; calculating an output of a forget gate that determines a degree to which a final state information cell of the previous time slot is reflected in the final state information cell based on the received pilot signal of the time slot and the output of the long short-term memory network of the previous time slot; calculating an output of an output gate that determines a degree to which the final state information cell is reflected in an output of the long short-term memory network based on the received pilot signal of the time slot and the output of the long short-term memory network of the previous time slot; calculating the candidate state information cell based on the received pilot signal of the time slot and the output of the long short-term memory network of the previous time slot; calculating the final state information cell by adding a value obtained by multiplying the output of the forget gate by the final state information cell of the previous time slot and a value obtained by multiplying the output of the input gate by the candidate state information cell; and calculating and outputting the time-varying channel feature embedding vector by multiplying the output of the output gate by a value obtained by applying a hyperbolic tangent to the final state information cell.

6 . The channel estimation method of claim 1 , wherein the estimating of the parameter of the channel model by using the time-varying channel feature embedding vector as the input in the fully connected network comprises estimating the parameter of the channel model by matching the time-varying channel feature embedding vector and the parameter of the channel model, using an input layer, at least one hidden layer, and an output layer in the fully connected network.

7 . The channel estimation method of claim 6 , wherein the input layer outputs a value calculated by the following equation:

x
0

=

f

activation

(

W
0

⁢

z
l

+

b
0

)

in which x 0 denotes an output of the input layer, W 0 denotes a weight matrix of the input layer, b 0 denotes a deviation of the input layer, z l denotes the time-varying channel feature embedding vector that is an output of the long short-term memory network, and f activation denotes an activation function.

8 . The channel estimation method of claim 6 , wherein the hidden layer outputs a value calculated by the following equation:

x
i

=

f

activation

(

W
i

⁢

x

i
-
1

+

b
i

)

in which x i denotes an output of an i-th hidden layer, W i denotes a weight of the i-th hidden layer, b i denotes a deviation of the i-th hidden layer, and f activation denotes an activation function used in each hidden layer.

9 . The channel estimation method of claim 6 , wherein the output layer outputs a value calculated by the following equation:

Ψ
l

=

tanh
⁡
(

W

out

⁢

x

N

hidden

+

b

out

)

in which Ψ l denotes a final output that yields estimates of a departure angle, an arrival angle, a path delay, and a path gain for each channel path in an l-th time s

Assignees

• Samsung Electronics Co Ltd
• Seoul Nat Univ R&Db Foundation

Inventors

• Kim Kyeongyeon
• Kim Won Jun
• Kim Jin Hong
• Shim Byong Hyo
• An Yong Jun

Classifications

• H04L25/0224

  using sounding signals · CPC title

• H04L25/0254 Primary

  using neural network algorithms · CPC title

• H04L25/02

  Details {; arrangements for supplying electrical power along data transmission lines (systems for transmitting signals via power distribution lines H04B3/54)} · CPC title

• H04B17/373 Primary

  Predicting channel quality {or other radio frequency [RF]} parameters · CPC title