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1. Modulation Classification of Single-Input Multiple-Output
Signals Using Asynchronous Sensors
ABSTRACT:
This paper discusses the automatic modulation classification of weak
communication signals using distributed low-cost sensors. The concept of a
secondary user in sensor networks is presented and very high-order statistics are
used as modulation features. Two feature-based methods, single variable and
multivariable modulation classifiers, are proposed for estimating unknown
modulation schemes through single-input multiple-output signal sensing channels.
The new approaches acquire multiple signal observations collected from
distributed sensors and leverage the channel diversity to enhance signal power and
reduce bias in estimation. The experiment demonstrates that the network
centric modulation classifier achieves significantly improved performance in terms
of probability of correct classification than the current state-of-the-art single sensor
modulation classifier, and the multivariable modulation classifier is more robust to
the channel parameter variations than the single-variable classifier.

2. EXISTING SYSTEM:
UNDER the effort of seeking more intensive and efficient use of spectrum and
power for modern wireless communications, the commercial market has redirected
the focus of automatic modulation classification (AMC) from military interest to
cognitive radio (CR) and wireless sensor network applications including fine
spectrum sensing, spectrum monitoring, user identification, adaptive modulation,
and network optimization [1]. AMC is a tool to identify the modulation scheme of
an unknown or partially known signal of interest (SOI) using a short sequence of
the signal (or snapshot). Early AMC devices acted as a universal demodulator to
recover AM and FM modulated voice automatically, but the algorithms became
more sophisticated when digital waveforms were introduced [2]–[4].
The most popular and effective statistical tools discussed in the AMC
literature for classifying digital modulation schemes are the maximum likelihood
test [4]–[9] and the higher-order statistics (HOS) method [10]–[21]. Lately,
antenna arrays were introduced to AMC for mitigating channel fading [22]–[24],
real-time approaches were studied for the CR applications [25], [26], and MIMO
and OFDM based AMCs were discussed for exploiting modern waveforms [27]–
[32]. Compared to a communication receiver, AMC is a more challenging device,
known as a non-cooperative receiver, since it usually operates in an unfavorable

3. location with bad channel quality and weak signal strength, and estimates unknown
parameters using limited signal length without any handshake from the transmitter.
PROPOSED SYSTEM:
The term SIMO or P2MP in this paper refers to a single SOI transmitter and
multiple SOI receivers and should not be confused with the use of SIMO antennas
in wireless communications or P2MP in wireless internet. However, techniques
developed in SIMO, P2MP, and antenna arrays can be leveraged or extended for
distributed AMC. Two major approaches, the distributed processing [35]–[40] and
the centralized processing [33], [41]–[44] methods have been investigated. The
former uses distributed single-variable AMC to make hard or soft decisions locally
at each sensor and pass the decisions to the fusion center for a global decision, and
the latter coherently combines multiple signal observations delivered to the fusion
center to form a single signal so that a single-variable AMC can be applied to make
the central decision.
In short, all prior art methods rely on the single variable AMC whether the
SOI is collected in a SISO or SIMO channels, and they inherit the same drawbacks
and physical limitations. To take the advantage of the multi-sensor framework and
break the tradition of investigating only single-variable AMC, this paper presents a

4. cognitive centralized processing scenario followed by a single-variable SIMO
AMC method using very high-order statistics (VHOS) [20]. Then, a novel multi-
variable SIMO AMC method is proposed to address the shortfall in prior art AMC
methods.

5. SOFTWARE IMPLEMENTATION:
 Modelsim 6.0
 Xilinx 14.2
HARDWARE IMPLEMENTATION:
 SPARTAN-III, SPARTAN-VI