Information Theory and Reliable Communication
Information Theory and Reliable Communication
Proceedings of the 11th annual international conference on Mobile computing and networking
Proceedings of the 8th ACM international symposium on Mobile ad hoc networking and computing
Cross-layer latency minimization in wireless networks with SINR constraints
Proceedings of the 8th ACM international symposium on Mobile ad hoc networking and computing
Multiflows in multihop wireless networks
Proceedings of the tenth ACM international symposium on Mobile ad hoc networking and computing
Wireless Communication Is in APX
ICALP '09 Proceedings of the 36th International Colloquium on Automata, Languages and Programming: Part I
Distributed contention resolution in wireless networks
DISC'10 Proceedings of the 24th international conference on Distributed computing
Improved algorithms for latency minimization in wireless networks
Theoretical Computer Science
Nearly optimal bounds for distributed wireless scheduling in the SINR model
ICALP'11 Proceedings of the 38th international conference on Automata, languages and programming - Volume Part II
Proceedings of the twenty-second annual ACM-SIAM symposium on Discrete Algorithms
The capacity of wireless networks
IEEE Transactions on Information Theory
A tutorial on cross-layer optimization in wireless networks
IEEE Journal on Selected Areas in Communications
Real-Time video streaming in multi-hop wireless static ad hoc networks
ALGOSENSORS'11 Proceedings of the 7th international conference on Algorithms for Sensor Systems, Wireless Ad Hoc Networks and Autonomous Mobile Entities
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We present an algorithm for multi-hop routing and scheduling of requests in wireless networks in the sinr model. The goal of our algorithm is to maximize the throughput or maximize the minimum ratio between the flow and the demand. Our algorithm partitions the links into buckets. Every bucket consists of a set of links that have nearly equivalent reception powers. We denote the number of nonempty buckets by σ. Our algorithm obtains an approximation ratio of O(σ·logn), where n denotes the number of nodes. For the case of linear powers σ=1, hence the approximation ratio of the algorithm is O(logn). This is the first practical approximation algorithm for linear powers with an approximation ratio that depends only on n (and not on the max-to-min distance ratio). If the transmission power of each link is part of the input (and arbitrary), then σ≤log$#915;+log$#916;, where $#915; denotes the ratio of the max-to-min power, and $#916; denotes the ratio of the max-to-min distance. Hence, the approximation ratio is O(logn ·(log$#915;+log$#916;)). Finally, we consider the case that the algorithm needs to assign powers to each link in a range [P min ,P max ]. An extension of the algorithm to this case achieves an approximation ratio of O[(logn+loglog$#915;) ·(log$#915;+log$#916;)].