The weighted majority algorithm
Information and Computation
Mobility increases the capacity of ad hoc wireless networks
IEEE/ACM Transactions on Networking (TON)
Throughput analysis of fading sensor networks with regular and random topologies
EURASIP Journal on Wireless Communications and Networking
Oblivious interference scheduling
Proceedings of the 28th ACM symposium on Principles of distributed computing
Distributed algorithms for approximating wireless network capacity
INFOCOM'10 Proceedings of the 29th conference on Information communications
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
Wireless capacity with oblivious power in general metrics
Proceedings of the twenty-second annual ACM-SIAM symposium on Discrete Algorithms
Proceedings of the twenty-second annual ACM-SIAM symposium on Discrete Algorithms
Capacity regions for wireless ad hoc networks
IEEE Transactions on Wireless Communications
On routing in random Rayleigh fading networks
IEEE Transactions on Wireless Communications
The capacity of wireless networks
IEEE Transactions on Information Theory
A network information theory for wireless communication: scaling laws and optimal operation
IEEE Transactions on Information Theory
Dynamic packet scheduling in wireless networks
PODC '12 Proceedings of the 2012 ACM symposium on Principles of distributed computing
Approximation algorithms for wireless link scheduling with flexible data rates
ESA'12 Proceedings of the 20th Annual European conference on Algorithms
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We study algorithms for wireless spectrum access of $n$ communication requests when interference conditions are given by the Rayleigh-fading model. This model extends the recently popular deterministic interference model based on the signal-to-interference-plus-noise ratio (SINR) using stochastic propagation to address fading effects observed in reality. We consider worst-case approximation guarantees for the two standard problems of capacity maximization (maximize the expected number of successful transmissions in a single slot) and latency minimization (minimize the expected number of slots until all transmissions were successful). Our main result is a generic reduction of Rayleigh fading to the deterministic SINR model. It allows to apply existing algorithms for the non-fading model in the Rayleigh-fading scenario while losing only a factor of O(logast n) in the approximation guarantee. This way, we obtain the first approximation guarantees for Rayleigh fading and, more fundamentally, show that non-trivial stochastic fading effects can be successfully handled using existing and future techniques for the non-fading model. Using a more detailed argument, a similar result applies even for distributed and game-theoretic capacity maximization approaches. For example, it allows to show that regret learning yields an O(log* n)-approximation with uniform power assignments. Our analytical treatment is supported by simulations illustrating the performance of regret learning and, more generally, the relationship between both models.