Jithin Jagannath

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We propose a novel optimal time slot allocation scheme for clustered underwater acoustic sensor networks that leverages physical (PHY) layer information to minimize the energy consumption due to unnecessary retransmissions thereby improving network lifetime and throughput. To reduce the overhead and the computational complexity, we employ a two-phase(More)
This paper introduces innovative technology development that will improve performance of next-generation cognitive wireless networking among space, air, and ground assets. The paper describes methods to develop a system where cognitive users transmit wideband spread-spectrum signals that are designed to adaptively avoid the interference dynamics of the(More)
In this paper, we propose a novel distributed deadline-based routing and spectrum allocation algorithm for tactical ad-hoc networks. The proposed algorithm will enable nodes to adapt to various deadline requirements unique to each traffic classes. A tactical ad-hoc network needs to handle a variety of data flowing through the network including voice,(More)
In this paper, we discuss the implementation of a CrOss-layer Based testbed with Analysis Tool (COmBAT). COmBAT is developed to enable the design and development process of next-generation cross-layer based wireless communication technologies for tactical ad-hoc networks. The COmBAT architecture comprises of two major components; (i) Adaptive cross-layer(More)
The objective of this work is to design and implement a novel framework for automatic signal classification techniques (FACT) for software defined radios (SDR) capable of classifying multiple signals simultaneously. The focus of this work is to create a modular classification framework to facilitate the testing and implementation of new classification(More)
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)
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