Improved modulation format identification based on
Stokes parameters using combination of fuzzy c-means
and hierarchical clustering in coherent optical
communication system
Longxue Cheng (程龙雪)
1
, Lixia Xi (席丽霞)
1,
*, Donghe Zhao (赵东鹤)
1
,
Xianfeng Tang (唐先锋)
1
, Wenbo Zhang (张文博)
2
, and Xiaoguang Zhang (张晓光)
1
1
State Key Laboratory of Information Photonics and Optical Communications, Beijing University of Posts
and Telecommunications, Beijing 100876, China
2
School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
*Corresponding author: xilixia@bupt.edu.cn
Received June 9, 2015; accepted August 19, 2015; posted online October 5, 2015
In this Letter, we develop the Stokes space-based method for modulation format identification by combing power
spectral density and a cluster analysis to identify quadrature amplitude modulation (QAM) and phase-shift
keying (PSK) signals. Fuzzy c-means and hierarchical clustering algorithms are used for the cluster analysis.
Simulations are conducted for binary PSK, quadrature PSK, 8PSK, 16-QAM, and 32-QAM signals. The results
demonstrate that the proposed technique can effectively classify all these modulation formats, and that the
method is superior in lowering the threshold of the optical signal-to-noise ratio. Meanwhile, the proposed method
is insensitive to phase offset and laser phase noise.
OCIS codes: 060.1660, 060.2330, 060.4510.
doi: 10.3788/COL201513.100604.
The continued demand for increased optical network
capacities provides challenges for current and future
network designs. To overcome these challenges, elastic
optical networks equipped with flexible transceivers are
required
[1]
. In order to demodulate signals optimally at the
receiver side
[2]
, modulation format identification (MFI) is
needed in future elastic optical networks.
Exploration for MFI techniques in optical communica-
tion has just begun. Four different methods have been
employed for optical MFI: (a) identification from constel-
lation diagrams using k-means, which is simple but
requires a carrier and phase recovery before MFI
[3]
;
(b) artificial neural network-based identification, which
can recognize all the formats but needs prior training
[4]
;
(c) principal component analysis-ba sed pattern recogni-
tion on asynchronous delay-tap plots, which can realize
channel estimation in the meantime but needs specific
amounts of sampling points
[5]
; (d) the Stokes space and
machine learning technique
[6]
.
Here, we theoretically analyze the distribution charac-
teristics of Stokes space clusters for different formats.
Based on this, Stokes parameters are extracted in the
coherent receiver and utilized t o distinguish between
quadrature amplitude modulation (QAM) and phase-shift
keying (PSK) signals. Furth ermore, a decision criterion
combining fuzzy c-means (FCM) and a hierarchical clus-
tering algorithm is used to provide enhanced discrimina-
tion among the modulation formats. This method was
proved applicable to wireless communications
[7]
. The
identification algorithm is implemented after chromatic
dispersion (CD) compensation.
For polarization-multiplexed (PM) system, the received
signal R can be expressed with the Jones vector in the
following form:
R ¼
E
x
E
y
¼
A
x
e
jφ
x
A
y
e
jφ
y
: (1)
The Jones vector is transformed into the Stokes vector,
S, as follows
[8]
:
S ¼
2
6
6
4
s
0
s
1
s
2
s
3
3
7
7
5
¼
2
6
6
6
4
A
2
x
þ A
2
y
A
2
x
− A
2
y
2A
x
A
y
cos δ
2A
x
A
y
sin δ
3
7
7
7
5
; (2)
where δ ¼ φ
x
− φ
y
is the phase difference between the X
and Y polarization components of the Jones vector R.If
the frequency offset, laser linewidth, and initial phase are
considered in the received signal, they just change the
phase of E
x
and E
y
, and hence the Stokes parameters
are not affected, as shown in Eq. (
2). Thus, the proposed
method is insensitive to these impairments. When normal-
ized by maxðs
0
Þ, the vector ½s
1
; s
2
; s
3
T
indicates different
points inside the Poincaré sphere. Different modulation
formats exhibit different signatures in accordance with
the number of clusters inside the Poincaré sphere; there-
fore, we can identify modulation formats by recording the
number of clusters. For binary PSK (BPSK), quadrature
PSK (QPSK), 8PSK, 16-QAM, and 32-QAM signals, the
COL 13(10), 100604(2015) CHINESE OPTICS LETTERS October 10, 2015
1671-7694/2015/100604(5) 100604-1 © 2015 Chinese Optics Letters
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