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This paper introduces CogNet architecture which is developed based on cross-layer optimized network architecture and specially designed for cognitive radio networks. It enables cognitive radios to share the network information between the lower three layers through a common database while efficiently processing the shared information using the cognitive engine which is attached onto the common database...
This paper introduces a novel cognitive multipath multi-channel routing protocol (CMMRP) for mobile ad-hoc networks. It is designed for a multi-channel environment where nodes can simultaneously use multiple interfaces to transmit packets over different frequencies. It employs cognitive functions to make nodes intelligently select multiple node-disjoint, edge-disjoint and frequency- disjoint paths...
This paper introduces a novel scalable cognitive routing protocol (SCRP) for mobile ad-hoc networks. It is an on-demand cognitive routing protocol. It employs an intelligent flooding protocol, a novel approach for scalable flooding. A neural network machine learning method is adopted to make nodes aware of history. The intelligent flooding protocol saves routing overhead because nodes selectively...
This paper provides a novel spectrum-aware routing protocol (SARP) for cognitive ad-hoc networks. It consists of two parts. One is the intelligent multi-interface selection function, which is used to assign an appropriate interface to a route to efficiently allocate the channels. The other is the intelligent multi-path selection function, which is used to select an appropriate path to route packets...
This paper introduces a novel mobility-aware routing protocol (MARP) for mobile, cognitive ad-hoc networks. Traditional routing protocols trigger the routing updates after the nodes detect the route failure. Even if the link condition is getting worse which means the link is likely to break in the future, the nodes will still transmit the packets along the current path. MARP uses cognitive techniques...
This paper provides a method for multi-path selection based on parameters prediction. In wireless networks, links with different bandwidths induce different end-to-end delay and the packet loss rate characteristics. It means that we should be able to gain some knowledge of the type of links given the end-to-end delay and the packet loss rate. In this work, we use a neural network machine learning...
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