Placing resources on a growing line
Journal of Algorithms
A constant-factor approximation algorithm for the k-median problem (extended abstract)
STOC '99 Proceedings of the thirty-first annual ACM symposium on Theory of computing
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Grassmannian beamforming for multiple-input multiple-output wireless systems
IEEE Transactions on Information Theory
Cooperative diversity in wireless networks: Efficient protocols and outage behavior
IEEE Transactions on Information Theory
Monge strikes again: optimal placement of web proxies in the internet
Operations Research Letters
An O(pn2) algorithm for the p -median and related problems on tree graphs
Operations Research Letters
Grouping and partner selection in cooperative wireless networks
IEEE Journal on Selected Areas in Communications
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We take an algorithmic approach to a well-known communication channel problem and develop several algorithms for solving it. Specifically, we develop power control algorithms for sensor networks with collaborative relaying under bandwidth constraints, via quantization of finite rate (bandwidth limited) feedback channels. We first consider the power allocation problem under collaborative relaying where the tradeoff between minimizing ones own energy expenditure and the energy for relaying is considered under the constraints of packet outage probability and bandwidth constrained (finite rate) feedback. Then we develop bandwidth constrained quantization algorithms (due to the finite rate feedback) that seek the optimal way of quantizing channel quality and power values in order to minimize the total average transmission power and satisfy the given probability of outage. We develop two kinds of quantization protocols and associated quantization algorithms. For separate source-relay quantization, we reduce the problem to the well-known k-median problem [1] on line graphs and show a a simple O((KJ)2N) polynomial time algorithm, where log2 KJ is the quantization bandwidth and N is the size of the discretized parameter space. For joint quantization, we first develop a simple 2-factor approximation of complexity O(KJN + N logN). Then, for Ɛ 0, we develop a fully polynomial approximation scheme (FPAS) that approximates the optimal quantization cost to within an 1+Ɛ-factor. The running time of the FPAS is polynomial in 1/Ɛ, size of the input N and also ln F, where F is the maximum available transmit power.