IP Library Granted Patent US 12,362,852
Granted Patent B2
US 12,362,852 · App. 17/950,085 · Granted Jul 15, 2025

Communication method and communication device thereof

Inventor: Chih-Ming Chen (New Taipei, TW)
Assignee: Wistron Corporation
H04L1/0039
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Quick Facts
Patent No.
US 12,362,852
App. No.
17/950,085
Granted
Jul 15, 2025
Kind
B2
Abstract

A communication method, for a receiver, including receiving a received signal, and obtaining information of an original signal according to the received signal. A transmitter obtains a transmitted signal according to the original signal. The transmitter sends the transmitted signal. The transmitted signal is changed to the received signal after passing through a channel. The transmitted signal and the received signal are correlated using a structural causal model. A number of a plurality of causal variables of a causal graph of the structural causal model and a causal structure of a causal graph of the structural causal model are determined together.

Claims (32)

1. A communication method, for a receiver, comprising:

receiving a received signal, wherein a transmitter obtains a transmitted signal according to an original signal, the original signal is substantially equal to or identical to the transmitted signal, the transmitter sends the transmitted signal, and the transmitted signal is changed into the received signal after passing through a channel; and

obtaining information of the original signal according to the received signal, wherein the transmitted signal and the received signal are correlated using a structural causal model, and a plurality of causal variables and a causal structure of a causal graph of the structural causal model between the transmitted signal and the received signal are determined together;

wherein the received signal is inferred into a plurality of combinations at least based on abductive reasoning, each of the plurality of combinations includes a candidate transmitted signal and a noise value, a preferred transmitted signal is selected from the plurality of candidate transmitted signals according to a signal-to-noise ratio, and the preferred transmitted signal is corresponding to the transmitted signal to convert the received signal into the original signal at least based on abductive reasoning.

2. The communication method of claim 1 , wherein the causal graph is generated based on maximum a posteriori and point estimation.

3. The communication method of claim 1 , wherein a plurality of data in a grounding data is mapped to the plurality of causal variables of the causal graph by using a plurality of observation functions, to generate the causal graph from the grounding data based on maximum a posteriori and point estimation.

4. The communication method of claim 2 , wherein the plurality of observation functions are obtained based on a causal semantic generative model.

5. The communication method of claim 1 , wherein the receiver is one of a user side equipment and a system comprising a radio unit and a distributed unit, and the transmitter is the other one of the user side equipment and the system.

6. The communication method of claim 1 , wherein the received signal includes the transmitted signal after distortion and noise, and at least one noise value corresponding to the structural causal model is set to a mean value of the noise.

7. The communication method of claim 1 , wherein the received signal is inferred into the plurality of combinations according to the causal graph at least based on abductive reasoning.

8. The communication method of claim 1 , wherein a plurality of posterior probabilities of assigning a plurality of data of a grounding data to a plurality of observation functions and the causal structure of the causal graph are maximized to generate the causal graph, wherein one of the plurality of posterior probabilities is proportional to Π t=0 T P(w i,t |s t-1 ,C,ƒ i ) (T-t) −γ , where w i,t denotes first data of the plurality of data at a time instant t, s t-1 denotes at least one state at a time instant t−1, C denotes the causal structure, ƒ i denotes a first observation function of the plurality of observation functions, T denotes a current time instant, and γ is a real number, wherein the first data and the first observation function correspond to a first causal variable of the plurality of causal variables.

9. A communication device, for a receiver, comprising:

a storage circuit, configured to store instructions of:

receiving a received signal, wherein a transmitter obtains a transmitted signal according to an original signal, the original signal is substantially equal to or identical to the transmitted signal, the transmitter sends the transmitted signal, and the transmitted signal is changed into the received signal after passing through a channel; and

obtaining information of the original signal according to the received signal, wherein the transmitted signal and the received signal are correlated using a structural causal model, and a plurality of causal variables of a causal graph of the structural causal model and a causal structure of the causal graph are determined together; and

a processing circuit, coupled to the storage device, configured to execute the instructions stored in the storage circuit;

wherein the received signal is inferred into a plurality of combinations at least based on abductive reasoning, each of the plurality of combinations includes a candidate transmitted signal and a noise value, a preferred transmitted signal is selected from the plurality of candidate transmitted signals according to a signal-to-noise ratio, and the preferred transmitted signal is corresponding to the transmitted signal to convert the received signal into the original signal at least based on abductive reasoning.

10. A communication method, for a transmitter, comprising:

obtaining a transmitted signal according to an original signal, wherein the original signal is substantially equal to or identical to the transmitted signal;

transmitting the transmitted signal, wherein the transmitted signal is changed into a received signal after passing through a channel, a receiver obtains information of the original signal according to the received signal, the transmitted signal and the received signal are correlated using a structural causal model, and a plurality of causal variables of a causal graph of the structural causal model and a causal structure of the causal graph are determined together;

wherein the received signal is inferred into a plurality of combinations at least based on abductive reasoning, each of the plurality of combinations includes a candidate transmitted signal and a noise value, a preferred transmitted signal is selected from the plurality of candidate transmitted signals according to a signal-to-noise ratio, and the preferred transmitted signal is corresponding to the transmitted signal to convert the received signal into the original signal at least based on abductive reasoning.

11. The communication method of claim 10 , wherein the causal graph is generated based on maximum a posteriori and point estimation.

12. The communication method of claim 10 , wherein a plurality of data in a grounding data is mapped to the plurality of causal variables of the causal graph by using a plurality of observation functions, to generate the causal graph from the grounding data based on maximum a posteriori and point estimation.

13. The communication method of claim 11 , wherein the plurality of observation functions are obtained based on a causal semantic generative model.

14. The communication method of claim 10 , wherein the receiver is one of a user side equipment and a radio unit, and the transmitter is the other one of the user side equipment and the radio unit.

15. The communication method of claim 10 , wherein the received signal includes the transmitted signal after distortion and noise, and at least one noise value corresponding to the structural causal model is set to a mean value of the noise.

16. A communication device, for a transmitter, comprising:

a storage circuit, configured to store instructions of:

obtaining a transmitted signal according to an original signal, wherein the original signal is substantially equal to or identical to the transmitted signal;

transmitting the transmitted signal, wherein the transmitted signal is changed into a received signal after passing through a channel, a receiver obtains information of the original signal according to the received signal, the transmitted signal and the received signal are correlated using a structural causal model, and a plurality of causal variables of a causal graph of the structural causal model and a causal structure of the causal graph are determined together; and

a processing circuit, coupled to the storage device, configured to execute the instructions stored in the storage circuit;

wherein the received signal is inferred into a plurality of combinations at least based on abductive reasoning, each of the plurality of combinations includes a candidate transmitted signal and a noise value, a preferred transmitted signal is selected from the plurality of candidate transmitted signals according to a signal-to-noise ratio, and the preferred transmitted signal is corresponding to the transmitted signal to convert the received signal into the original signal at least based on abductive reasoning.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 21, 2022
From: CHEN, CHIH-MING
To: WISTRON CORPORATION
Reel/Frame 061174/0237 →
Priority Claims (1)
TW 111126310 · Jul 13, 2022 · national
Continuity (1)
Related Publication 20240022349A1 · Jan 18, 2024
References Cited (20)
US 6278735B1 · Mohsenian · 2001 [cited by examiner]
US 11342946B1 · Bhatia · 2022 [cited by examiner]
US 11610132B2 · Hewage · 2023 [cited by examiner]
US 11715004B2 · Zhang · 2023 [cited by examiner]
US 11909482B2 · Namgoong · 2024 [cited by examiner]
US 20150043323A1 · Choi · 2015 [cited by examiner]
US 20200394512A1 · Zhang · 2020 [cited by examiner]
US 20240056983A1 · Chen · 2024 [cited by examiner]
US 20240169222A1 · Chen · 2024 [cited by examiner]
CN 112637094A · 2021 [cited by applicant]
JP 2006211131A · 2006 [cited by applicant]
JP 2021500813A · 2021 [cited by applicant]
KR 1020220009188A · 2022 [cited by applicant]
KR 20220009188A · 2024 [cited by examiner]
KR 102753303B1 · 2025 [cited by examiner]
Phillip Lippe et al., iCITRIS: Causal Representation Learning for Instantaneous Temporal Effects, Jun. 13, 2022, p. 1-48, XP091245967, Jun. 13, 2022. [cited by applicant]
Benben Jiang et al., Simultaneous Identification of Bidirectional Path Models Based on Process Data, IEEE Transactions on Automation Science and Engineering, vol. 12, No. 2, Apr. 2015, p. 666-679, XP011577516, Apr. 2015. [cited by applicant]
Johann Brehmer et al., Weakly supervised causal representation learning, May 30, 2022, p. 1-33, XP091228380, May 30, 2022. [cited by applicant]
Jithin Jagannath et al., Machine Learning for Wireless Communications in the Internet of Things: A Comprehensive Survey, Jul. 22, 2019, p. 1-97, XP081444168, Jul. 22, 2019. [cited by applicant]
Christian Toth et al., Active Bayesian Causal Inference, Jun. 4, 2022, p. 1-26, XP091240078, Jun. 4, 2022. [cited by applicant]