Distributed maximum likelihood classification of linear modulations over nonidentical flat block fading gaussian channels +91-9994232214,8144199666, ieeeprojectchennai@gmail.com, www.projectsieee.com, www.ieee-projects-chennai.com IEEE PROJECTS 2015-2016 ----------------------------------- Contact:+91-9994232214,+91-8144199666 Email:ieeeprojectchennai@gmail.com Support: ------------- Projects Code Documentation PPT Projects Video File Projects Explanation Teamviewer Support

1. Distributed Maximum Likelihood Classification of Linear Modulations
over Nonidentical Flat Block-Fading Gaussian Channels
Abstract:
In this paper, we consider distributed maximum likelihood (ML)
classification of digital amplitude-phase modulated signals using multiple
sensors that observe the same sequence of unknown symbol transmissions
over non identical flat block fading Gaussian noise channels. A variant of
the expectation maximization (EM) algorithm is employed to obtain the
ML estimates of the unknown channel parameters and compute the global
log-likelihood of the observations received by all the sensors in a
distributed manner by means of an average consensus filter. This
procedure is repeated for all candidate modulation formats in the reference
library, and a classification decision, which is available at any of the
sensors in the network, is declared in favor of the modulation with the
highest log-likelihood score. The proposed scheme improves the
classification accuracy by exploiting the signal-to-noise ratio (SNR)
diversity in the network while restricting the communication to a small
neighborhood of each sensor. Numerical examples show that the proposed
distributed EM-based classifier can achieve the same classification
performance as that of a centralized classifier, which has all the sensor
measurements, for a wide range of SNR values.

2. Existing System:
When the channel conditions are favorable, the average throughput can be
increased by selecting higher order modulation formats to transmit at
higher data rates. On the contrary, as the channel degrades, lower order
modulation formats can be employed to reduce the data rate to maintain an
acceptable average probability of error. If the modulation is estimated
automatically and blindly at the receiver from a sequence of noisy
observations, communication overhead between the transmitter and
receiver can be reduced considerably.
Proposed System:
The solution of the MC problem within the framework of detection theory
is obtained by computing the likelihood of the received waveform
conditioned on a particular signal constellation in the reference library,
under different levels of prior statistical information about the unknown
parameters, and deciding in favor of the constellation with the highest
likelihood.
Depending on whether the unknown signal and channel parameters and
the data symbols are treated as random variables (RVs) or deterministic
unknowns, there are three commonly used techniques: average likelihood,
generalized likelihood, and hybrid likelihood.
Hardware Requirements:
• System : Pentium IV 2.4 GHz.
• Hard Disk : 40 GB.
• Floppy Drive : 1.44 Mb.

3. • Monitor : 15 VGA Colour.
• Mouse : Logitech.
• RAM : 256 Mb.
Software Requirements:
• Operating system : - Centos.
• Tool : - VM Ware Workstation
• Script : - NS2