IP Library › Granted Patent US 12,301,266
Granted Patent B2
US 12,301,266 · App. 18/311,685 · Granted May 13, 2025

Interference mitigation in wireless communication using artificial interference signal

Inventor: Muhammad Tawhidur Rahman (Bellevue, WA)
Assignee: T-Mobile USA, Inc.
H04B1/0475H04B17/345H04B2001/0425
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,301,266
App. No.
18/311,685
Granted
May 13, 2025
Kind
B2
Abstract

Methods, devices, and system related to wireless communications are disclosed. In one example aspect, a device for wireless communication includes a processor that is configured to determine an estimation of an interference signal for a connection between the device and a receiving device in a wireless communication system, construct an interference elimination signal based on the estimation of the interference signal, and perform a data transmission to the receiving device with the interference elimination signal to enable the receiving device to eliminate the interference signal in the data transmission. The estimation of the interference signal is determined by building a probabilistic model of the interference signal using at least an interference template associate with a characteristic of the device or one or more measurements of a channel condition collected within a predefined observation window.

Claims (33)

1. A method for wireless communication, comprising:

transmitting, by a communication device to a network node, a measurement result associated with a channel between the network node and the communication device to enable an estimation of an interference signal based on a probabilistic model that is constructed using at least the measurement result;

determining, by the communication device, an interference elimination signal that has a substantially same amplitude as the estimated interference signal and a substantially opposite phase as compared to the estimated interference signal,

wherein the determining of the interference elimination signal comprises:

receiving, by the communication device, an indication of the interference elimination signal from the network node,

wherein the interference elimination signal is determined by the network node based on the probabilistic model; and

applying, by the communication device, the interference elimination signal to a data transmission to the network node.

2. The method of claim 1 , wherein the indication is carried in a control signal in a control channel that corresponds to a data channel for the data transmission.

3. The method of claim 1 , wherein the determining of the interference elimination signal comprises:

deriving, by the communication device, the interference elimination signal based on a geographical location of the communication device.

4. The method of claim 3 , wherein the interference elimination signal is derived based on an interference template associated with a characteristic of the geographical location of the communication device, the characteristic comprising a landscape near the geographical location or a topology associated with the geographical location.

5. The method of claim 4 , further comprising:

refining, by the communication device, the interference template based on at least the measurement result collected in a predefined time window.

6. The method of claim 1 , wherein the measurement result comprises at least one of: a Channel Quality Indicator (CQI), a Precoding Matrix Index (PMI), or Rank Indicator (RI).

7. The method of claim 1 , wherein the measurement result is transmitted within a time window that is represented in a number of hours, days, weeks, months, or quarters.

8. The method of claim 7 , wherein the time window is adjustable based on processing load of the network node.

9. The method of claim 1 , wherein the probabilistic model comprises at least one of a closest pattern matching (CPM) model or a correlation distortion model.

10. A device for wireless communication, comprising one or more processors that are configured to perform operations comprising:

transmitting, to a network node, a measurement result associated with a channel between the network node and the device to enable an estimation of an interference signal based on a probabilistic model that is constructed using at least the measurement result;

receiving an indication of an interference elimination signal from the network node,

wherein the interference elimination signal is determined by the network node based on the probabilistic model;

determining the interference elimination signal that has a substantially same amplitude as the estimated interference signal and a substantially opposite phase as compared to the estimated interference signal; and

applying the interference elimination signal to a transmission to the network node.

11. The device of claim 10 , wherein the indication is carried in control information that corresponds to the transmission.

12. The device of claim 10 , wherein the operations comprise:

deriving the interference elimination signal based on a geographical location of the device.

13. The device of claim 12 , wherein the interference elimination signal is derived based on an interference template associated with a characteristic of the geographical location of the device, the characteristic comprising a landscape near the geographical location or a topology associated with the geographical location.

14. The device of claim 13 , wherein the operations further comprise:

refining the interference template based on at least the measurement result collected in a predefined time window.

15. The device of claim 10 , wherein the measurement result comprising at least one of a Channel Quality Indicator (CQI), a Precoding Matrix Index (PMI), or Rank Indicator (RI).

16. The device of claim 10 , wherein the measurement result is transmitted within a time window that is represented in a number of hours, days, weeks, months, or quarters.

17. The device of claim 16 , wherein the time window is adjustable based on processing load of the network node.

18. The device of claim 10 , wherein the probabilistic model comprises at least one of a closest pattern matching (CPM) model or a correlation distortion model.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 3, 2023
From: RAHMAN, MUHAMMAD TAWHIDUR
To: T-MOBILE USA, INC.
Reel/Frame 063525/0242 →
Continuity (2)
Continuation 17384619 · Jul 23, 2021
Related Publication 20230275606A1 · Aug 31, 2023
References Cited (50)
US 6185256B1 · Saito et al. · 2001 [cited by applicant]
US 6661835B1 · Sugimoto et al. · 2003 [cited by applicant]
US 7027533B2 · Abe et al. · 2006 [cited by applicant]
US 7043242B2 · Kuiri et al. · 2006 [cited by applicant]
US 7079607B2 · Brunel · 2006 [cited by applicant]
US 7088978B2 · Hui et al. · 2006 [cited by applicant]
US 7136638B2 · Wacker et al. · 2006 [cited by applicant]
US 7245679B2 · Aoki et al. · 2007 [cited by applicant]
US 7545827B2 · Tao et al. · 2009 [cited by applicant]
US 7546105B2 · Piirainen · 2009 [cited by examiner]
US 7697645B2 · Jong · 2010 [cited by examiner]
US 7733813B2 · Shin et al. · 2010 [cited by applicant]
US 7782987B2 · Jonsson · 2010 [cited by applicant]
US 7796716B2 · Bhukania et al. · 2010 [cited by applicant]
US 8077627B2 · Chang et al. · 2011 [cited by applicant]
US 8155046B2 · Jung et al. · 2012 [cited by applicant]
US 8200484B2 · Choi et al. · 2012 [cited by applicant]
US 8849210B2 · Mese et al. · 2014 [cited by applicant]
US 9559874B2 · Han et al. · 2017 [cited by applicant]
US 9602230B2 · Roman et al. · 2017 [cited by applicant]
US 9748990B2 · Wu et al. · 2017 [cited by applicant]
US 9912374B2 · Zhang · 2018 [cited by applicant]
US 10284313B2 · Abdelmonem · 2019 [cited by examiner]
US 10396925B2 · Wu et al. · 2019 [cited by applicant]
US 10560244B2 · Jana et al. · 2020 [cited by applicant]
US 11166288B2 · Abdelmonem et al. · 2021 [cited by applicant]
US 20070135051A1 · Zheng et al. · 2007 [cited by applicant]
US 20070280332A1 · Srikanteswara et al. · 2007 [cited by applicant]
US 20100080323A1 · Mueck et al. · 2010 [cited by applicant]
US 20100290552A1 · Sasaki · 2010 [cited by applicant]
US 20110158211A1 · Gaal et al. · 2011 [cited by applicant]
US 20130102256A1 · Cendrillon et al. · 2013 [cited by applicant]
US 20130114468A1 · Hui et al. · 2013 [cited by applicant]
US 20130188760A1 · Subramanian et al. · 2013 [cited by applicant]
US 20210153052A1 · Taherzadeh Boroujeni et al. · 2021 [cited by applicant]
US 20210160713A1 · Yang et al. · 2021 [cited by applicant]
CN 1311646C · 2007 [cited by applicant]
CN 1951058B · 2010 [cited by applicant]
CN 106797223B · 2019 [cited by applicant]
CN 109274399B · 2020 [cited by applicant]
JP 2006229503A · 2006 [cited by applicant]
JP 2006238423A · 2006 [cited by applicant]
JP 2006287551A · 2006 [cited by applicant]
KR 20070074708A · 2007 [cited by applicant]
KR 20080086726A · 2008 [cited by applicant]
WO 03001742A1 · 2003 [cited by applicant]
WO 2005114874A1 · 2005 [cited by applicant]
WO 2007046503A1 · 2007 [cited by applicant]
WO 2008040088A1 · 2008 [cited by applicant]
WO 2010151849A2 · 2010 [cited by applicant]