Opportunistic routing in multi-hop wireless networks
ACM SIGCOMM Computer Communication Review
When Does Opportunistic Routing Make Sense?
PERCOMW '05 Proceedings of the Third IEEE International Conference on Pervasive Computing and Communications Workshops
Modeling and Analysis of Opportunistic Routing in Low Traffic Scenarios
WIOPT '05 Proceedings of the Third International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks
Symbol error probabilities for general Cooperative links
IEEE Transactions on Wireless Communications
Determination of optimal transmission power in wireless relay networks with generalized error model
IEEE Transactions on Wireless Communications
Optimal resource allocation in multi-hop OFDMA wireless networks with cooperative relay
IEEE Transactions on Wireless Communications - Part 2
On the performance of amplify-and-forward cooperative systems with fixed gain relays
IEEE Transactions on Wireless Communications - Part 2
Cooperative communications with relay-selection: when to cooperate and whom to cooperate with?
IEEE Transactions on Wireless Communications
IEEE Transactions on Wireless Communications
Capacity limits of cognitive radio with distributed and dynamic spectral activity
IEEE Journal on Selected Areas in Communications
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In this paper, we consider a cognitive multi-hop relay secondary user (SU) system sharing the spectrum with some primary users (PU). The transmit power as well as the hop selection of the cognitive relays can be dynamically adapted according to the local (and causal) knowledge of the instantaneous channel state information (CSI) in the multi-hop SU system. We shall determine a low complexity, decentralized algorithm to maximize the average end-to-end throughput of the SU system with dynamic spatial reuse. The problem is challenging due to the decentralized requirement as well as the causality constraint on the knowledge of CSI. Furthermore, the problem belongs to the class of stochastic Network Utility Maximization (NUM) problems which is quite challenging. We exploit the time-scale difference between the PU activity and the CSI fluctuations and decompose the problem into a master problem and subproblems. We derive an asymptotically optimal low complexity solution using divideand-conquer and illustrate that significant performance gain can be obtained through dynamic hop selection and power control. The worst case complexity and memory requirement of the proposed algorithm is O(M2) and O(M3) respectively, where M is the number of SUs.