EXTREME HIGH THROUGHPUT LONG TRAINING FIELD

• US 20200136875A1
• Filed: 12/26/2019
• Published: 04/30/2020
• Est. Priority Date: 05/29/2019
• Status: Abandoned Application

• Current Assignee: Intel Corporation
• Original Assignee: Intel Corporation
• Inventors: Chen, Xiaogang, Jiang, Feng, Kenney, Thomas J., Li, Qinghua
• Application Number:
  US16/727,626
• Publication Number:
  US 20200136875A1
• Time in Patent Office: Days
• CPC Class Codes: H04B 7/0413 MIMO systems
• CPC Class Codes: H04L 27/2613 Structure of the reference signals
• CPC Class Codes: H04L 5/0007 the frequencies being orthogonal, e.g. OFDM(A), DMT
• CPC Class Codes: H04L 5/0023 Time-frequency-space
• CPC Class Codes: H04L 5/0048 Allocation of pilot signals, i.e. of signals known to the receiver

Abstract

This disclosure describes systems, methods, and devices related to extreme high throughput (EHT) long training field (LTF). A device may determine one or more symbols associated with a high efficiency (HE) LTF, wherein the one or more symbols are used for training one or more spatial streams. The device may determine a first group of spatial streams of the one or more spatial streams. The device may determine a second group of spatial streams of the one or more spatial streams, wherein the first group of spatial streams is different from the second group of spatial streams. The device may cause to train the first group of spatial streams using a first set of frequency tones. The device may cause to train the second group of spatial streams using a second set of frequency tones, wherein the first set of frequency tones is different from the second set of frequency tones.

Claims

1. A device, the device comprising processing circuitry coupled to storage, the processing circuitry configured to:

determine one or more symbols associated with a high efficiency (HE) long training field (LTF), wherein the one or more symbols are used for training one or more spatial streams;

determine a first group of spatial streams of the one or more spatial streams;

determine a second group of spatial streams of the one or more spatial streams, wherein the first group of spatial streams is different from the second group of spatial streams;

cause to train the first group of spatial streams using a first set of frequency tones; and

cause to train the second group of spatial streams using a second set of frequency tones, wherein the first set of frequency tones is different from the second set of frequency tones.

2. The device of claim 1, wherein a p-matrix having a maximum size of 8×

8 is used for the first group of spatial streams, and wherein the p-matrix is used for the second group of spatial streams.

3. The device of claim 2, wherein the first group of spatial streams comprises up to eight spatial streams.

4. The device of claim 1, wherein the second group of spatial streams comprises up to eight spatial streams.

5. The device of claim 1, wherein the first group and the second group of spatial streams are trained using up to eight HE-LTF symbols.

6. The device of claim 1, wherein the processing circuitry is further configured to expand a 8×

8 p-matrix to up to 16×

16 p-matrix using a subset of p-matrices.

7. The device of claim 1, wherein the processing circuitry is further configured to determine a 10×

10 p-matrix, wherein the 10×

10 p-matrix a first element comprised of 10 “

1,” and

at least a second element based on a weight vector, wherein the weight vector is made up of 10 elements.

8. A non-transitory computer-readable medium storing computer-executable instructions which when executed by one or more processors result in performing operations comprising:

determining one or more symbols associated with a high efficiency (HE) long training field (LTF), wherein the one or more symbols are used for training one or more spatial streams;

determining a first group of spatial streams of the one or more spatial streams;

determining a second group of spatial streams of the one or more spatial streams, wherein the first group of spatial streams is different from the second group of spatial streams;

causing to train the first group of spatial streams using a first set of frequency tones; and

causing to train the second group of spatial streams using a second set of frequency tones, wherein the first set of frequency tones is different from the second set of frequency tones.

9. The non-transitory computer-readable medium of claim 8, wherein a p-matrix having a maximum size of 8×

8 is used for the first group of spatial streams, and wherein the p-matrix is used for the second group of spatial streams.

10. The non-transitory computer-readable medium of claim 9, wherein the first group of spatial streams comprises up to eight spatial streams.

11. The non-transitory computer-readable medium of claim 8, wherein the second group of spatial streams comprises up to eight spatial streams.

12. The non-transitory computer-readable medium of claim 8, wherein the first group and the second group of spatial streams are trained using up to eight HE-LTF symbols.

13. The non-transitory computer-readable medium of claim 8, wherein the operations further comprise expanding a 8×

8 p-matrix to up to 16×

16 p-matrix using a subset of p-matrices.

14. The non-transitory computer-readable medium of claim 8, wherein the operations further comprise determining a 10×

10 p-matrix, wherein the 10×

10 p-matrix a first element comprised of 10 “

1,” and

at least a second element based on a weight vector, wherein the weight vector is made up of 10 elements.

15. A method comprising:

determining, by one or more processors, one or more symbols associated with a high efficiency (HE) long training field (LTF), wherein the one or more symbols are used for training one or more spatial streams;

determining, by one or more processors, a first group of spatial streams of the one or more spatial streams;

determining a second group of spatial streams of the one or more spatial streams, wherein the first group of spatial streams is different from the second group of spatial streams;

causing to train the first group of spatial streams using a first set of frequency tones; and

causing to train the second group of spatial streams using a second set of frequency tones, wherein the first set of frequency tones is different from the second set of frequency tones.

16. The method of claim 15, wherein a p-matrix having a maximum size of 8×

8 is used for the first group of spatial streams, and wherein the p-matrix is used for the second group of spatial streams.

17. The method of claim 16, wherein the first group of spatial streams comprises up to eight spatial streams.

18. The method of claim 15, wherein the second group of spatial streams comprises up to eight spatial streams.

19. The method of claim 15, wherein the first group and the second group of spatial streams are trained using up to eight HE-LTF symbols.

20. The method of claim 15, further comprising expanding a 8×

8 p-matrix to up to 16×

16 p-matrix using a subset of p-matrices.