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Please use this identifier to cite or link to this item: http://hdl.handle.net/1807/17730

Title: Node Selection in Cooperative Wireless Networks
Authors: Beres, Elzbieta
Advisor: Adve, Raviraj
Department: Electrical and Computer Engineering
Keywords: Cooperative Diversity
Spatial Diversity
Resource Allocation
Issue Date: 23-Sep-2009
Abstract: In this thesis, we argue for node selection in cooperative decode-and-forward networks. In a single-hop network with multiple relays, we show that selecting a single node to aid in the transmission between a source and a destination outperforms both traditional orthogonal transmissions and distributed space-time codes. In networks where sources transmit information over multiple hops and relays can communicate with each other, we study the relationship between cooperation and channel-adaptive routing. We show that cooperation is only beneficial if designed jointly with a routing scheme. This motivates a search for optimal algorithms in generalized relay networks. In networks without restrictions on the relays in terms of whom they can communicate with, we study the problem of optimal resource allocation in terms of transmission time. The resource allocation selects the relays to participate in the transmission and optimally allocates time resource between the selected relays. To implement this resource allocation algorithm, we propose a recursive solution which reduces the computational complexity of the algorithm. For large networks, the resulting computational complexity of implementing the algorithm is exponential in the size of the network and is likely to preclude its implementation. We thus propose that the resource allocation be implemented sub-optimally through node selection: a subset of the nodes in the network should be selected and used as input to the optimal resource allocation algorithm. We provide guidelines for selecting the nodes and propose four heuristics which offer various complexity-performance trade-offs. Compared to the optimal resource algorithm, all four heuristics significantly decrease the required computation complexity of the optimal algorithm.
URI: http://hdl.handle.net/1807/17730
Appears in Collections:Doctoral
The Edward S. Rogers Sr. Department of Electrical & Computer Engineering - Doctoral theses

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