# US 2019/0174507 A1 · Method and apparatus for blind detection of interference parameters in lte system

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- Number: US 2019/0174507 A1
- Document ID: US20190174507A1
- Status: Published application
- Kind: A1
- Application number: 16/267,620
- Filed: 2019-02-05
- Published: 2019-06-06
- Earliest claimed: 2016-02-18
- Assignees: Samsung Electronics Co., Ltd.
- Applicants: Samsung Electronics Co., Ltd.
- Inventors: Mojtaba RAHMATI; Dongwoon Bai; Jungwon Lee
- Web page: https://patentgrep.com/patent/US20190174507A1

## CPC

- H04W 72/541: using the level of interference
- H04J 11/005: {of intercell interference}
- H04L 1/0054: {Maximum-likelihood or sequential decoding, e.g. Viterbi, Fano, ZJ algorithms}
- H04L 5/0026: {Division using four or more dimensions, e.g. beam steering or quasi-co-location [QCL]}
- H04L 5/005: {of common pilots, i.e. pilots destined for multiple users or terminals}
- H04W 72/563: of the wireless resources
- H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72)
- H04J: MULTIPLEX COMMUNICATION (transmission in general H04B; peculiar to transmission of digital information H04L5/00; systems for the simultaneous or sequential transmission of more than one television signal H04N7/08; in exchanges H04Q11/00; stereophonic systems H04S)
- H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M)

## Abstract

A method and an apparatus. The method includes receiving a signal including a serving signal and an interference signal; applying a Gaussian approximation (GA) on the serving signal and the interference signal; determining likelihood metrics for hypotheses on a rank of the interference signal, a precoding matrix index, and power on the GA-applied interference signal based on a covariance matrix for each of the hypotheses; and selecting one of the hypotheses that maximizes one of the likelihood metrics.

## Claims

### Claim 1

A method, comprising:
receiving a signal including a serving signal and an interference signal;
applying a Gaussian approximation (GA) on a serving signal and the interference signal;
determining likelihood metrics for hypotheses on a rank of the interference signal, a precoding matrix index, and power on the GA-applied interference signal based on a covariance matrix for each of the hypotheses; and
selecting one of the hypotheses that maximizes one of the likelihood metrics.

### Claim 2 (depends on 1)

The method of claim 1, further comprising:
determining, jointly, a maximum likelihood (ML) solution of rank, traffic to pilot ratio (TPR), and precoding matrix index on the GA-applied serving signal and the GA-applied interference signal; and
determining, sequentially, the rank of the interference signal based on the jointly determined ML solution.

### Claim 3 (depends on 2)

The method of claim 2, wherein the covariance matrix for each of the hypotheses is determined on the received signal after the serving signal is cancelled from the received signal.

### Claim 4 (depends on 2)

The method of claim 2, further comprising:
determining likelihood metrics for hypotheses on the interference signal rank, the precoding matrix index, and power on the GA-applied interference signal;
determining hypotheses that maximize the likelihood metrics and determine the TPR;
determining if the rank is 1;
if the rank is not 1 then determining if transmit diversity is present;
if the transmit diversity is not present then determining that the rank is 2;
determining the precoding matrix index if the rank is 1, the transmit diversity is present, or if the rank is 2; and
returning the determinations of the TPR, the rank, and the precoding matrix index.

### Claim 5 (depends on 3)

The method of claim 3, further comprising:
determining likelihood metrics for hypotheses on the interference signal rank, the precoding matrix index, and power on the GA-applied interference signal;
determining hypotheses that maximize the likelihood metrics and determine the TPR;
determining if the rank is 1;
if the rank is not 1 then determining if transmit diversity is present;
if the transmit diversity is not present then determining that the rank is 2;
determining the precoding matrix index if the rank is 1, the transmit diversity is present, or if the rank is 2; and
returning the determinations of the TPR, the rank, and the precoding matrix index.

### Claim 6 (depends on 4)

The method of claim 4, wherein the ML solution of rank, TPR, and precoding matrix index is determined for at least one cell-specific reference signal (CRS) antenna port.

### Claim 7 (depends on 5)

The method of claim 5, wherein the ML solution of rank, TPR, and precoding matrix index is determined for at least one cell-specific reference signal (CRS) antenna port.

### Claim 8 (depends on 6)

The method of claim 6, wherein the likelihood metric is one of:
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or
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1
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
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where MρI represents the metric in one CRS antenna port case and MρI,cmi represent the metric in two CRS antenna port case, yk=HkSxkS+ρIHkIxkI+nk, Hkm represents an effective channel matrix from a serving evolved node B (eNB) to a user equipment (UE) for m=S and from an interfering cell to the UE for m=I, where ρI represents a TPR of an interfering signal, xkm=[xkm,1, . . . ,xkm,lm]T is a lm×1 transmit signal vector, lm indicates a number of transmission layers, nk is an additive white Gaussian noise (AWGN) vector with covariance E{nknkH}=σ2I, K is a integer, |Rk(ρI)| denotes a determinant of matrix Rk(ρI)=RkS+ρI2RkI+σ2I,
R
k
S
=
[
r
k
,
00
S
r
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,
01
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r
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,
10
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r
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,
11
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]
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=
[
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,
00
I
r
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,
01
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,
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I
r
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11
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]
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=
[
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-
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k
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r
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,
and
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[
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I
-
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,
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-
r
k
,
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r
k
,
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,
and |Rk(ρI,cmi)| denotes a determinant of matrix Rk(ρI,cmi)=RkS+ρI2RkI(cmi)+σ2I, where
R
k
I
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(
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)
=
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k
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I
,
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,
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
(
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and
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(
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r
k
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,
then Rk−1(ρI,cmi) is as follows:
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,
cmi
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=
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I
2
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k
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(
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)
+
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I

R
k
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(
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I
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cmi
)

,
wherein cmi ∈ {0,1,2,3,4} with cmi=i (for i ∈ {0,1,2,3}) corresponding to rank 1 with a precoding index of p=i, and cmi=4 corresponding to rank 2 and SFBC, and wherein there are 5 possible hypotheses for cmi and 3 possible hypotheses for ρI.

### Claim 9 (depends on 7)

The method of claim 7, wherein the likelihood metric is one of:
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=
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(
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(
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;
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K
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y
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+
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log
(
∑
k
=
1
K
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
R
k
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(
ρ
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
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M
ρ
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,
cmi
=
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K
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trace
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(
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k
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y
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H
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+
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(
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(
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;
or
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ρ
I
,
cmi
≈
∑
K
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=
1

trace

(
y
k
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y
k
H
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(
D
k
S
+
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I
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trace

(
y
k
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y
k
H
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D
k
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(
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K
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R
k
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k
=
1
K


R
k

(
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I
,
cmi
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
K
)
,
where MρI represents the metric in one CRS antenna port case and MρI,cmi represent the metric in two CRS antenna port case, yk=HkSxkS+ρIHkIxkI+nk, Hkm represents an effective channel matrix from a serving evolved node B (eNB) to a user equipment (UE) for m=S and from an interfering cell to the UE for m=I, where ρI represents a TPR of an interfering signal, xkm=[xkm,1, . . . ,xkm,lm]T is a lm×1 transmit signal vector, lm indicates a number of transmission layers, nk is an additive white Gaussian noise (AWGN) vector with covariance E{nknkH}=σ2I, K is a integer, |Rk(ρI)| denotes a determinant of matrix Rk(ρI)=RkS+ρI2RkI+σ2I,
R
k
S
=
[
r
k
,
00
S
r
k
,
01
S
r
k
,
10
S
r
k
,
11
S
]
R
k
I
=
[
r
k
,
00
I
r
k
,
01
I
r
k
,
10
I
r
k
,
11
I
]
D
k
S
=
[
r
k
,
11
S
-
r
k
,
01
S
-
r
k
,
10
S
r
k
,
00
S
]
,
and
D
k
I
=
[
r
k
,
11
I
-
r
k
,
01
I
-
r
k
,
10
I
r
k
,
00
I
]
,
and |Rk(ρI,cmi)| denotes a determinant of matrix Rk(ρI,cmi)=RkS+ρI2RkI(cmi)+σ2I, where
R
k
I

(
cmi
)
=
G
k
I

P
I
,
l
I
,
p

(
P
I
,
l
I
,
p
)
H

(
G
k
I
)
H
=
[
r
k
,
00
I

(
cmi
)
r
k
,
01
I

(
cmi
)
r
k
,
10
I

(
cmi
)
r
k
,
11
I

(
cmi
)
]
,
and
D
k
I

(
cmi
)
=
[
r
k
,
11
I

(
cmi
)
-
r
k
,
01
I

(
cmi
)
-
r
k
,
10
I

(
cmi
)
r
k
,
00
I

(
cmi
)
]
,
then Rk−1(ρI,cmi) is as follows:
R
k
-
1

(
ρ
I
,
cmi
)
=
D
k
S
+
ρ
I
2

D
k
I

(
cmi
)
+
σ
2

I

R
k

(
ρ
I
,
cmi
)

,
wherein cmi ∈{0,1,2,3,4} with cmi=i (for i ∈ {0,1,2,3}) corresponding to rank 1 with a precoding index of p=i, and cmi=4 corresponding to rank 2 and SFBC, and wherein there are 5 possible hypotheses for cmi and 3 possible hypotheses for ρI.

### Claim 10 (depends on 8)

The method of claim 8, wherein the precoding matrix index for the received signal of rank 2 or a serving-signal-cancelled received signal of rank 2 is identified based on
M
I
=
1
K

∑
k
=
1
K


f

(
β
k
,
I
)
≈
E

{
f

(
β
k
,
I
)
}


and

M
Q
=
1
K

∑
k
=
1
K


f

(
β
k
,
Q
)
≈
E

{
f

(
β
k
,
Q
)
}
,
wherein a non-linear operation f(.) has a property that E{f(βk,I)}≠E{f(βk,Q)},
wherein the non-linear operation f(.) includes f(βk,I)=|βk,I| and f(βk,I)=|βk,I|2, where a precoding identification rule is
p
^
=
{
0
M
Q
>
M
I
1
M
Q
<
M
I
.

### Claim 11

An apparatus, comprising:
an antenna for receiving a signal including a serving signal and an interference signal; and
a processor configured to:
apply a Gaussian approximation (GA) on the serving signal and the interference signal;
determine likelihood metrics for hypotheses on a rank of the interference signal, a precoding matrix index, and power on the GA-applied interference signal based on a covariance matrix for each hypotheses; and
select one of the hypotheses that maximizes one of the likelihood metrics.

### Claim 12 (depends on 11)

The apparatus of claim 11, wherein the processor is further configured to:
determine, jointly, a maximum likelihood (ML) solution of rank, traffic to pilot ratio (TPR), and precoding matrix index on the GA-applied serving signal and the GA-applied interference signal; and
determine sequentially, the rank of the interference signal based on the jointly determined ML solution.

### Claim 13 (depends on 12)

The apparatus of claim 12, wherein the processor is further configured to determine the covariance matrix for each of the hypotheses on the received signal after the serving signal is cancelled from the received signal.

### Claim 14 (depends on 12)

The apparatus of claim 12, wherein the processor is further configured to:
determine likelihood metrics for hypotheses on the interference signal rank, the precoding matrix index, and power on the GA-applied interference signal;
determine hypotheses that maximize the likelihood metrics and determine the TPR;
determine if the rank is 1;
if the rank is not 1 then determine if transmit diversity is present;
if the transmit diversity is not present then determine that the rank is 2;
determine the precoding matrix index if the rank is 1, the transmit diversity is present, or if the rank is 2; and
return the determinations of the TPR, the rank, and the precoding matrix index.

### Claim 15 (depends on 13)

The apparatus of claim 13, wherein the processor is further configured to:
determine likelihood metrics for hypotheses on the interference signal rank, the precoding matrix index, and power on the GA-applied interference signal;
determine hypotheses that maximize the likelihood metrics and determine the TPR;
determine if the rank is 1;
if the rank is not 1 then determine if transmit diversity is present;
if the transmit diversity is not present then determine that the rank is 2;
determine the precoding matrix index if the rank is 1, the transmit diversity is present, or if the rank is 2; and return the determinations of the TPR, the rank, and the precoding matrix index.

### Claim 16 (depends on 14)

The apparatus of claim 14, wherein the processor is further configured to determine the ML solution of rank, TPR, and precoding matrix index for at least one cell-specific reference signal (CRS) antenna port.

### Claim 17 (depends on 15)

The apparatus of claim 15, wherein the processor is further configured to determine the ML solution of rank, TPR, and precoding matrix index for at least one cell-specific reference signal (CRS) antenna port.

### Claim 18 (depends on 16)

The apparatus of claim 16, wherein the processor is further configured to determine a likelihood metric selected from one of:
M
ρ
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=
∑
k
=
1
K


trace

(
y
k
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y
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H
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D
k
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(
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k
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1
K
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trace
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(
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y
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H
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(
∑
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k
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M
ρ
I
,
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=
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k
=
1
K


trace

(
y
k

y
k
H

(
D
k
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+
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2
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trace
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(
y
k

y
k
H

D
k
I

(
cmi
)
)

R
k

(
ρ
I
,
cmi
)

+
log

(

R
k

(
ρ
I
,
cmi
)

)
;
or
M
ρ
I
,
cmi
≈
∑
k
=
1
K

trace

(
y
k

y
k
H

(
D
k
S
+
σ
2

I
)
)
+
ρ
I
2

trace

(
y
k

y
k
H

D
k
I

(
cmi
)
)
∑
k
=
1
K


R
k

(
ρ
I
,
cmi
)

K
+
K


log
(
∑
k
=
1
K


R
k

(
ρ
I
,
cmi
)

K
)
,
where MρI represents the metric in one CRS antenna port case and MρI,cmi represent the metric in two CRS antenna port case, yk=HkSxkS+ρIHkIxkI+nk, Hkm represents an effective channel matrix from a serving evolved node B (eNB) to a user equipment (UE) for m=S and from an interfering cell to the UE for m=I, where ρI represents a TPR of an interfering signal, xkm=[xkm,1, . . . ,xkm,lm]T is a lm×1 transmit signal vector, lm indicates a number of transmission layers, nk is an additive white Gaussian noise (AWGN) vector with covariance E{nknkH}=σ2I, K is a integer, |Rk(ρI)| denotes a determinant of matrix Rk(ρI)=RkS+ρI2RkI+σ2I,
R
k
S
=
[
r
k
,
00
S
r
k
,
01
S
r
k
,
10
S
r
k
,
11
S
]
R
k
I
=
[
r
k
,
00
I
r
k
,
01
I
r
k
,
10
I
r
k
,
11
I
]
D
k
S
=
[
r
k
,
11
S
-
r
k
,
01
S
-
r
k
,
10
S
r
k
,
00
S
]
,
and
D
k
I
=
[
r
k
,
11
I
-
r
k
,
01
I
-
r
k
,
10
I
r
k
,
00
I
]
,
and |Rk(ρI,cmi)| denotes a determinant of matrix Rk(ρI,cmi)=RkS+ρI2RkI(cmi)+σ2I, where
R
k
I

(
cmi
)
=
G
k
I

P
I
,
l
I
,
p

(
P
I
,
l
I
,
p
)
H

(
G
k
I
)
H
=
[
r
k
,
00
I

(
cmi
)
r
k
,
01
I

(
cmi
)
r
k
,
10
I

(
cmi
)
r
k
,
11
I

(
cmi
)
]
,
and
D
k
I

(
cmi
)
=
[
r
k
,
11
I

(
cmi
)
-
r
k
,
01
I

(
cmi
)
-
r
k
,
10
I

(
cmi
)
r
k
,
00
I

(
cmi
)
]
,
then Rk−1(ρI,cmi) is as follows:
R
k
-
1

(
ρ
I
,
cmi
)
=
D
k
S
+
ρ
I
2

D
k
I

(
cmi
)
+
σ
2

I

R
k

(
ρ
I
,
cmi
)

,
wherein cmi ∈{0,1,2,3,4}, and wherein there are five possible hypotheses for cmi and three possible hypotheses for ρI.

### Claim 19 (depends on 17)

The apparatus of claim 17, wherein the processor is further configured to determine a likelihood metric selected from one of:
M
ρ
I
=
∑
k
=
1
K


trace

(
y
k

y
k
H

(
D
k
S
+
σ
2

I
)
)
+
ρ
I
2

trace

(
y
k

y
k
H

D
k
I
)

R
k

(
ρ
I
)

+
log

(

R
k

(
ρ
I
)

)
;
M
ρ
I
=
∑
k
=
1
K

trace

(
y
k

y
k
H

(
D
k
S
+
σ
2

I
)
)
+
ρ
I
2

trace

(
y
k

y
k
H

D
k
I
)
∑
k
=
1
K


R
k

(
ρ
I
)

K
+
K


log
(
∑
k
=
1
K


R
k

(
ρ
I
)

K
)
M
ρ
I
,
cmi
=
∑
k
=
1
K


trace

(
y
k

y
k
H

(
D
k
S
+
σ
2

I
)
)
+
ρ
I
2

trace

(
y
k

y
k
H

D
k
I

(
cmi
)
)

R
k

(
ρ
I
,
cmi
)

+
log

(

R
k

(
ρ
I
,
cmi
)

)
;
or
M
ρ
I
,
cmi
≈
∑
k
=
1
K

trace

(
y
k

y
k
H

(
D
k
S
+
σ
2

I
)
)
+
ρ
I
2

trace

(
y
k

y
k
H

D
k
I

(
cmi
)
)
∑
k
=
1
K


R
k

(
ρ
I
,
cmi
)

K
+
K


log
(
∑
k
=
1
K


R
k

(
ρ
I
,
cmi
)

K
)
,
where MρI represents the metric in one CRS antenna port case and MρI,cmi represent the metric in two CRS antenna port case, yk=HkSxkS+ρIHkIxkI+nk, Hkm represents an effective channel matrix from a serving evolved node B (eNB) to a user equipment (UE) for m=S and from an interfering cell to the UE for m=I, where ρI represents a TPR of an interfering signal, xkm=[xkm,1, . . . ,xkm,lm]T is a lm×1 transmit signal vector, lm indicates a number of transmission layers, nk is an additive white Gaussian noise (AWGN) vector with covariance E{nknkH}=σ2I, K is a integer, |Rk(ρI)| denotes a determinant of matrix Rk(ρI)=RkS+ρI2RkI+σ2I,
R
k
S
=
[
r
k
,
00
S
r
k
,
01
S
r
k
,
10
S
r
k
,
11
S
]
R
k
I
=
[
r
k
,
00
I
r
k
,
01
I
r
k
,
10
I
r
k
,
11
I
]
D
k
S
=
[
r
k
,
11
S
-
r
k
,
01
S
-
r
k
,
10
S
r
k
,
00
S
]
,
and
D
k
I
=
[
r
k
,
11
I
-
r
k
,
01
I
-
r
k
,
10
I
r
k
,
00
I
]
,
and |Rk(ρI,cmi)| denotes a determinant of matrix Rk(ρI,cmi)=RkS+ρI2RkI(cmi)+σ2I , where
R
k
I

(
cmi
)
=
G
k
I

P
I
,
l
I
,
p

(
P
I
,
l
I
,
p
)
H

(
G
k
I
)
H
=
[
r
k
,
00
I

(
cmi
)
r
k
,
01
I

(
cmi
)
r
k
,
10
I

(
cmi
)
r
k
,
11
I

(
cmi
)
]
,
and
D
k
I

(
cmi
)
=
[
r
k
,
11
I

(
cmi
)
-
r
k
,
01
I

(
cmi
)
-
r
k
,
10
I

(
cmi
)
r
k
,
00
I

(
cmi
)
]
,
then Rk−1(ρI,cmi) is as follows:
R
k
-
1

(
ρ
I
,
cmi
)
=
D
k
S
+
ρ
I
2

D
k
I

(
cmi
)
+
σ
2

I

R
k

(
ρ
I
,
cmi
)

,
wherein cmi ∈ {0,1,2,3,4}, and wherein there are five possible hypotheses for cmi and three possible hypotheses for ρI.

### Claim 20 (depends on 18)

The apparatus of claim 18, wherein the processor is further configured to identify a precoding matrix index for the received signal of rank 2 or a serving-signal-cancelled received signal of rank 2 based on
M
I
=
1
K

∑
k
=
1
K


f

(
β
k
,
I
)
≈
E

{
f

(
β
k
,
I
)
}


and

M
Q
=
1
K

∑
k
=
1
K


f

(
β
k
,
Q
)
≈
E

{
f

(
β
k
,
Q
)
}
,
wherein a non-linear operation f(.) has a property that E{f(βk,I)}≠E{f(βk,Q)},
wherein the non-linear operation f(.) includes f(βk,I)=|βk,I| and f(βk,I|2, where a precoding identification rule is
p
^
=
{
0
M
Q
>
M
I
1
M
Q
<
M
I
.