A new global positioning system (GPS)-based routing protocol for ad hoc networks, called zone-based hierarchical link state (ZHLS) routing protocol, is proposed. In this protocol, the network is divided into nonoverlapping zones. Each node only knows the node connectivity within its zone and the zone connectivity of the whole network. The link state routing is performed on two levels: local node and global zone levels. Unlike other hierarchical protocols, there is no cluster head in this protocol. The zone level topological information is distributed to all nodes. This "peer-to-peer" manner mitigates traffic bottleneck, avoids single point of failure, and simplifies mobility management. Since only zone ID and node ID of a destination are needed for routing, the route from a source to a destination is adaptable to changing topology. The zone ID of the destination is found by sending one location request to every zone. Simulation results show that our location search scheme generates less overhead than the schemes based on flooding. The results also confirm that the communication overhead for creating and maintaining the topology in the proposed protocol is smaller than that in the flat LSR protocol. This new routing protocol provides a flexible, efficient, and effective approach to accommodate the changing topology in a wireless network environment.
Multiple ray paths are resolved using highresolution digital signal processing algorithms. The Cramer-Rao (CR) bound is used as a benchmark where a combination of the singular value decomposition method and the eigen-matrix pencil method is proven to be most successful. The conventional complex channel model for wireless propagation is extended to include the frequency-dependent feature of rays which can be used to classify the ray arrivals and provide physical insight of the channel. A novel complex-time model is used to approximate the suggested model. This approach is important to various applications such as equalizer, RAKE receiver, etc., in wireless communication systems. Five key features (noise immunity, robustness, resolution, accuracy, and physical insight) of the proposed algorithm are studied using numerical examples.
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