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• W973156638 abstract "In the first part, we study cooperative data communication, specifically, network-coded receiver cooperation for a single-source multiple-receiver broadcasting model. Cooperative communication has been studied mostly with the senders or relays doing the cooperative relaying. Less study has been done when the receiver side cooperate to each other. We develop network coding scheme for the receiver cooperation. Network codes based on GF(2q) random-mixing are considered complex and prone to errors. Sparse binary random-mixing is considerably simpler, but for it to be space-preserving requires the involvement of a huge number of source packets (vectors). In our case, we propose a novel strategy of an offset sparse binary random mixing at the receiver side, in which the source vectors are firstly circularly shifted, each by a different random offset, before being XORed. This simple strategy cleverly compensates for the low degree of the binary field (because of fewer number of practical receivers who in the same cooperating set) by the large dimension of the vector space. This ensures the near linear-independence of the random binary superposition while enjoying solid code structure from the well-known class of quasi-cyclic low-density parity-check codes. In the second part, we investigate efficient algorithm in cooperated spectrum sensing in wireless network, which is generally harsh environment caused by shadowing and/or fading. Spectrum sensing in cognitive radio considers two entities, the owner of the spectrum, both transmitters/receivers called primary users and the other set of users called secondary users, who are waiting to use the bandwidth whenever it is vacant. Each cycle consists of two stages, sense and occupy. The first step is a very important as it would help to ensure that the interference by the primary users will be minimized as well as utilizing most of the available time slot. Cooperative sensing will improve the accuracy of the measuring step. Here, inspired by a well-known class of probabilistic inference problems, believe propagation and message passing algorithm, we model the network of the secondary users as Markov random fields and pull a group of secondary users to cooperate through distributed probabilistic inference. This results in an effective sensing and fusion strategy. The proposed framework subsumes belief propagation, as well as conventional weighted hard/soft combining (such as maximal ratio combining and equal gain combing). It can also account for the distance dependent correlation among individual sensing results by setting appropriate compatibility function. Theoretic upper and lower bounds are derived, demonstrating the significant gains made possible by effective cooperation. Extensive simulations confirm the analytical results. We further study more into the theoretical ground of our distributed sensing framework: the belief propagation (BP) as well as a similar class of in-network computation algorithm called distributed consensus (DC). We detail the exact operations of these algorithms, unify them in the general formulation of linear dynamical systems, evaluate their convergences, and compare their behaviors. Our analysis provides useful insight into how the two algorithms arising from drastically different theoretical basis can serve a common purpose. Specifically, it is shown that belief propagation is an algorithm of ''less is more'' and hence favors sparse graphs, whereas distributed consensus is an algorithm of ''the more the merrier'' and hence favors dense graphs. In the third part, we study the problem of cooperative localization of a large network of nodes in integer-coordinated unit disk graphs, a simplified but useful version of general random graph. Exploiting the property that the radius r sets clear cut on the connectivity of two nodes, we propose an essential philosophy that ''no connectivity is a useful information'' in unit disk graphs. Exercising this philosophy, we show that the conventional network localization problem can be re-formulated to significantly reduce the search space, and that global rigidity, a necessary and sufficient condition for the existence of unique solution in general graphs, is no longer necessary. (Abstract shortened by UMI.)" @default.
• W973156638 created "2016-06-24" @default.
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• W973156638 date "2012-01-01" @default.
• W973156638 modified "2023-09-24" @default.
• W973156638 title "Cooperative computing techniques in networked systems" @default.
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