IP Library Granted Patent US 10,547,346
Granted Patent B2
US 10,547,346 · App. 16/450,092 · Granted Jan 28, 2020

Systems and methods for intelligently-tuned digital self-interference cancellation

Inventors: Rajendra Tushar Moorti (Sunnyvale, CA); Kumar Viswanatha (Sunnyvale, CA)
Assignee: Kumu Networks, Inc.
H04B1/56H04B1/12H04B1/123H04B1/525H04L25/0202H04L5/14H04L27/2628H04L27/2647
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Quick Facts
Patent No.
US 10,547,346
App. No.
16/450,092
Granted
Jan 28, 2020
Kind
B2
Abstract

A system for digital self-interference cancellation includes a filter that generates a reduced-noise digital residue signal; a channel estimator that generates a current self-interference channel estimate from a digital transmit signal, the reduced-noise digital residue signal, and past self-interference channel estimates; a controller that dynamically sets the digital transform configuration in response to changes in a controller-sampled digital residue signal; a predictor that modifies output of the channel estimator to compensate for a first time delay incurred in tuning the system for digital self-interference cancellation; and a channel memory that stores the past self-interference channel estimates.

Claims (23)

1. A system for digital self-interference cancellation comprising:

a filter coupled to a digital residue signal of a communication system that reduces noise, thereby generating a first reduced-noise digital residue signal;

a channel estimator coupled to the filter that generates a current self-interference channel estimate from a sampled digital transmit signal, the first reduced-noise digital residue signal, and past self-interference channel estimates; wherein the channel estimator generates the current self-interference channel estimate according to a digital transform configuration;

a controller that dynamically sets the digital transform configuration in response to changes in a controller-sampled digital residue signal;

a channel memory, coupled to the channel estimator, that stores the past self-interference channel estimates; and

a predictor that modifies output of the channel estimator to compensate for a first time delay incurred in tuning the system for digital self-interference cancellation; wherein the system generates a digital self-interference cancellation signal from the current self-interference channel estimate and the sampled digital transmit signal; wherein the digital self-interference cancellation signal is combined with a receive signal of the communication system to form the digital residue signal.

2. The system of claim 1 , wherein the predictor comprises an offset estimator, a first differentiator, and a slope estimator; wherein the offset estimator estimates an offset of the current self-interference channel estimate; wherein the first differentiator generates a first derivative of the current self-interference channel estimate; wherein the slope estimator estimates a slope of the current self-interference channel estimate using the first derivative.

3. The system of claim 2 , wherein the predictor multiplies the estimated slope by an estimate of the first time delay, generating a predictor product, and adds the estimated offset to the predictor product to generate a predicted channel estimate.

4. The system of claim 3 , wherein the predictor generates the estimate of the first time delay using instantaneous time deltas.

5. The system of claim 3 , wherein the predictor generates the estimate of the first time delay using filtered time deltas.

6. The system of claim 2 , further comprising a second differentiator; wherein the second differentiator generates a second derivative of the current self-interference channel estimate from the first derivative of the current self-interference channel estimate.

7. The system of claim 6 , wherein the slope estimator estimates a slope of the current self-interference channel estimate using both of the first derivative and the second derivative.

8. The system of claim 7 , further comprising a second derivative estimator, wherein the second derivative estimator estimates a concavity of the current self-interference channel estimate.

9. The system of claim 8 , wherein the predictor multiplies the concavity by a first time delta, generating a first predictor product, and adds the first predictor product to output of the slope estimator to estimate the slope of the current self-interference channel estimate.

10. The system of claim 9 , wherein the predictor multiplies the estimated slope by a second time delta, generating a second predictor product, and adds the estimated offset to the predictor product to generate a predicted channel estimate.

11. The system of claim 10 , wherein the first and second time deltas are equal.

12. The system of claim 2 , wherein the predictor transforms the output of the channel estimator from Cartesian to non-Cartesian coordinates, produces a predicted channel estimate using the non-Cartesian coordinates, and converts the predicted channel estimate back to Cartesian coordinates.

13. The system of claim 12 , wherein the non-Cartesian coordinates are polar coordinates.

14. The system of claim 1 , wherein the current self-interference channel estimate has a non-uniform sampling rate.

15. The system of claim 14 , wherein the predictor comprises an offset estimator, a first differentiator, and a slope estimator; wherein the offset estimator estimates an offset of the current self-interference channel estimate; wherein the first differentiator generates a first derivative of the current self-interference channel estimate.

16. The system of claim 15 , wherein the slope estimator estimates a slope of the current self-interference channel estimate using the first derivative multiplied by an inverse of a first time delta.

17. The system of claim 16 , wherein the predictor multiplies the estimated slope by a second time delta, generating a predictor product, and adds the estimated offset to the predictor product to generate a predicted channel estimate.

18. The system of claim 17 , wherein the first time delta and second time delta are not equal.

Assignments (3)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Mar 29, 2024
From: MOORTI, RAJENDRA TUSHAR
To: KUMU NETWORKS, INC.
Reel/Frame 066957/0063 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Dec 21, 2023
From: KUMU NETWORKS, INC.
To: QUALCOMM INCORPORATED
Reel/Frame 066090/0165 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jun 24, 2019
From: MOORTI, RAJENDRA TUSHAR; VISWANATHA, KUMAR
To: KUMU NETWORKS, INC.
Reel/Frame 049567/0538 →
Continuity (4)
Continuation 16125269 · Sep 7, 2018
Continuation 15937605 · Mar 27, 2018
Provisional Application 62477301 · Mar 27, 2017
Related Publication 20190312609A1 · Oct 10, 2019