Lakshmi Narasimhan Theagarajan

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Automatic modulation classification (AMC) is a key component of intelligent communication systems used in various military and cognitive radio applications. In AMC, it is desired to increase the number of different modulation formats that can be classified, reduce the computational complexity of classification, and improve the robustness and accuracy of the(More)
Automatic modulation classification (AMC) with multiple sensors is a challenging problem when the channel conditions are unknown at the receiver. In this paper, using the Markov chain Monte Carlo (MCMC) approach, we develop a novel algorithm for AMC when the amplitude and phase of the channel gains are unknown. Using sampling techniques, we marginalize over(More)
We consider the scenario where multiple users send information to a single receiver over a common channel. Each user is aware of only his channel fading state and has statistical knowledge of the fading process of the other users. The fading process of each user is modeled as ergodic finite state Markov chains. We consider two problems. The first problem is(More)
In this paper, we generalize the well-known index coding problem to exploit the structure in the source-data to improve system throughput. In many applications (e.g., multimedia), the data to be transmitted may lie (or can be well approximated) in a low-dimensional subspace. We exploit this low-dimensional structure of the data using an algebraic framework(More)
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