# US 10,873,491 B2 · Apparatus and method for coarse timing and frequency synchronization

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- Number: US 10,873,491 B2
- Document ID: US10873491B2
- Status: Granted
- Kind: B2
- Application number: 16/269,223
- Filed: 2019-02-06
- Published: 2019-06-06
- Granted: 2020-12-22
- Earliest claimed: 2016-12-13
- Assignees: Samsung Electronics Co., Ltd.
- Applicants: Samsung Electronics Co., Ltd.
- Inventors: Xiaoqiang Ma; Hongbing Cheng; Linbo Li; Jungwon Lee
- Web page: https://patentgrep.com/patent/US10873491B2

## CPC

- H04L 27/2663: {Coarse synchronisation, e.g. by correlation}
- H04L 27/2601: {Multicarrier modulation systems}
- H04L 27/2671: {Time domain}
- H04L 27/2675: {Pilot or known symbols}
- H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M)

## Abstract

An apparatus including a plurality of multipliers each configured to multiply correlations by a weight, wherein the correlations include a plurality of subsets of correlations, wherein the weight of each of the plurality of multipliers is uniquely different from the weights of at least two of the plurality of multipliers and the correlations; a summation processor configured to sum the weight multiplied correlations of the plurality of multipliers for time n, where n is an integer; and a coarse timing and coarse frequency processor configured to generate a coarse timing and a coarse frequency based on the sum.

## Claims

### Claim 1

An apparatus, comprising:
a plurality of multipliers, wherein each multiplier of the plurality of multipliers is configured to multiply a unique subset of correlations, from a plurality of correlations, by a unique weight, from a plurality of weights;
a summation processor configured to sum the weight multiplied correlations of the plurality of multipliers for time n, where n is an integer; and
a coarse timing and coarse frequency processor configured to generate a coarse timing and a coarse frequency based on the sum.

### Claim 2 (depends on 1)

The apparatus of claim 1, wherein each unique subset of correlations is based on one group of short training field (STF) samples from N groups of STF samples, wherein the total number of STF samples is j, and where Cd is a number of samples per group, and wherein N, j, and Cd are integers.

### Claim 3 (depends on 2)

The apparatus of claim 2, wherein a first unique subset of correlations is based on a first group of STF samples that includes STF samples 0 to Cd−1, a second unique subset of correlations is based on a second group of STF samples that includes STF samples Cd to 2Cd−1, and a last unique subset of correlations is based on a last group of STF samples that includes STF samples (N−1)Cd to NCd−1, where Cd is a number of samples per group, and wherein N, j, and Cd are integers.

### Claim 4 (depends on 1)

The apparatus of claim 1, wherein the weights of a first k number of the plurality of multipliers are less than the weights of others of the plurality of multipliers.

### Claim 5 (depends on 4)

The apparatus of claim 4, wherein the weights of the first k number of the plurality of multipliers are set to 0 and the weights of others of the plurality of multipliers are set to 1.

### Claim 6 (depends on 1)

The apparatus of claim 1, wherein the summation processor is configured to sum the weight multiplied correlations of the plurality of multipliers in accordance with the following expression:
A
STF
⁡
(
n
)
=
∑
p
=
0
144
M
W
-
1
⁢
⁢
w
⁡
(
p
)
⁢
∑
q
=
0
M
W
-
1
⁢
⁢
x
⁡
(
n
-
M
W
⁢
p
-
q
)
⁢
x
*
⁡
(
n
-
M
W
⁢
p
-
q
-
16
)
=
∑
p
=
0
144
M
W
-
1
⁢
⁢
w
⁡
(
p
)
⁢
∑
q
=
0
M
W
-
1
⁢
⁢
c
⁡
(
n
,
M
W
⁢
p
+
q
)
=
∑
p
=
0
144
M
W
-
1
⁢
⁢
w
⁡
(
p
)
⁢
A
STF
(
p
)
⁡
(
n
)
=
∑
p
=
0
144
M
W
-
1
⁢
⁢
w
⁡
(
p
)
⁢
(
A
STF
(
p
)
⁡
(
n
-
1
)
+
c
⁡
(
n
,
M
W
⁢
p
+
q
)
-
c
⁡
(
n
,
M
W
⁢
p
+
q
+
16
)
)
,
where MW is an integer, l is an integer representing a number of short training field (STF) auto-correlations, x(n−MWp−q) is an input data sample at time instant (n−MWp−q), x*(n−MWp−q−16) is a conjugate of a delayed input data sample at time instant (n−MWp−q−16), w(p) is a weight of a pth STF auto-correlation c(n, MWp+q)=x(n−MWp−q)x*(n−MWp−q−16), and
ASTF(p)(n)=Σq=0MW-1c(n, MWp+q) is a summed correlation with weights w(p).

### Claim 7 (depends on 6)

The apparatus of claim 6, wherein ASTF(p)(n) is calculated recursively as follows: ASTF(p)(n−1)+c(n, MWp+q)−c(n, MWp+q+16).

### Claim 8 (depends on 6)

The apparatus of claim 6, wherein the coarse timing and coarse frequency processor uses the summation ASTF(n) to determine the coarse timing position with an STF auto-correlation peak search method as follows:
npeak=arg max(|ASTF(n)|),
and wherein the coarse frequency is determined as follows:
Δ
⁢
⁢
f
STF
=
2
π
⁢
arg
⁡
(
A
STF
⁡
(
n
peak
)
)
.

### Claim 9 (depends on 6)

The apparatus of claim 6, wherein the coarse timing and coarse frequency processor uses the summation ASTF(n) to determine a coarse timing position with an STF auto-correlation peak search method as follows:
npeak=arg max(|ASTF(n)|),
wherein a timing estimation bias is applied in addition to npeak to obtain a final estimated timing position as follows:
nfinal=npeak+nbias,
and wherein the coarse frequency is determined as follows:
Δ
⁢
⁢
f
STF
=
2
π
⁢
arg
⁡
(
A
STF
⁡
(
n
final
)
)
.

### Claim 10 (depends on 1)

The apparatus of claim 1, further comprising a plurality of correlation processors configured to generate the correlations.

### Claim 11

A method, comprising:
generating a unique subset of correlations, from a plurality of correlations, for each group of samples, from N groups of samples;
multiplying, by each of a plurality of multipliers, a corresponding unique subset of correlations by a corresponding unique weight, from a plurality of weights;
summing, by a summation processor, the weight multiplied correlations of the plurality of multipliers for time n, where n is an integer; and
determining, by a coarse timing and coarse frequency processor, a coarse timing and a coarse frequency based on the summing of the weight multiplied correlations of the plurality of multipliers for time n.

### Claim 12 (depends on 11)

The method of claim 11, wherein the N groups of samples comprise short training field (STF) samples, wherein the total number of STF samples is j, and where Cd is a number of samples per group, and wherein N, j, and Cd are integers.

### Claim 13 (depends on 12)

The method of claim 12, wherein the N groups of samples includes a first group of STF samples that includes STF samples 0 to Cd−1, a second group of STF samples that includes STF samples Cd to 2Cd−1, and a last group of STF samples that includes STF samples (N−1)Cd to NCd−1.

### Claim 14 (depends on 11)

The method of claim 11, wherein the weights of a first k number of the plurality of multipliers are less than the weights of others of the plurality of multipliers.

### Claim 15 (depends on 14)

The method of claim 14, wherein the weights of the first k number of the plurality of multipliers are set to 0 and the weights of the others of the plurality of multipliers are set to 1.

### Claim 16 (depends on 11)

The method of claim 11, wherein the summation processor sums the weight multiplied correlations of the plurality of multipliers for time n, where n is an integer, in accordance with the following expression:
A
STF
⁡
(
n
)
=
∑
p
=
0
144
M
W
-
1
⁢
⁢
w
⁡
(
p
)
⁢
∑
q
=
0
M
W
-
1
⁢
⁢
x
⁡
(
n
-
M
W
⁢
p
-
q
)
⁢
x
*
⁡
(
n
-
M
W
⁢
p
-
q
-
16
)
=
∑
p
=
0
144
M
W
-
1
⁢
⁢
w
⁡
(
p
)
⁢
∑
q
=
0
M
W
-
1
⁢
⁢
c
⁡
(
n
,
M
W
⁢
p
+
q
)
=
∑
p
=
0
144
M
W
-
1
⁢
⁢
w
⁡
(
p
)
⁢
A
STF
(
p
)
⁡
(
n
)
=
∑
p
=
0
144
M
W
-
1
⁢
⁢
w
⁡
(
p
)
⁢
(
A
STF
(
p
)
⁡
(
n
-
1
)
+
c
⁡
(
n
,
M
W
⁢
p
+
q
)
-
c
⁡
(
n
,
M
W
⁢
p
+
q
+
16
)
)
,
where MW is an integer, l is an integer representing a number of short training field (STF) auto-correlations, x(n−MWp−q) is an input data sample at time instant (n−MWp−q), x*(n−MWp−q−16) is a conjugate of a delayed input data sample at time instant (n−MWp−q−16), w(p) is a weight of a pth STF auto-correlation c(n, MWp+q)=x(n−MWp−q)x*(n−MWp−q−16), and
ASTF(p)=Σq=0MW-1c(n, MWp+q) is a summed correlation with weights w(p).

### Claim 17 (depends on 16)

The method of claim 16, wherein ASTF(p)(n) is calculated recursively as follows: ASTF(p)(n−1)+c(n, MWp+q)−c(n, MWp+q+16).

### Claim 18 (depends on 16)

The method of claim 16, wherein determining, by the coarse timing and coarse frequency processor, the coarse timing and the coarse frequency based on the summation comprises using the summation ASTF(n) to determine the coarse timing with an STF auto-correlation peak search method as follows:
npeak=arg max(|ASTF(n)|),
and wherein the coarse frequency is determined as follows:
Δ
⁢
⁢
f
STF
=
2
π
⁢
arg
⁡
(
A
STF
⁡
(
n
peak
)
)
.

### Claim 19 (depends on 16)

The method of claim 16, wherein determining, by the coarse timing and coarse frequency processor, the coarse timing and the coarse frequency based on the summation comprises using the summation ASTF(n) to determine the coarse timing with an STF auto-correlation peak search method as follows:
npeak=arg max(|ASTF(n)|),
wherein a timing estimation bias is applied in addition to npeak to obtain a final estimated timing position as follows:
nfinal=npeak+nbias,
and wherein the coarse frequency is determined as follows:
Δ
⁢
⁢
f
STF
=
2
π
⁢
arg
⁡
(
A
STF
⁡
(
n
final
)
)
.

### Claim 20 (depends on 11)

The method of claim 11, wherein the correlations are generated by a plurality of correlation processors.