he noisy broadcast model was first studied by [Gallager, 1988] where an ncharacter input is distributed among n processors, so that each processor receives one input bit. Computation proceeds in rounds, where in each round each processor broadcasts a single character, and each reception is corrupted independently at random with some probability p. [Gallager, 1988] gave an algorithm for all processors to learn the input in O(log log n) rounds with high probability. Later, a matching lower bound of Omega(log log n) was given by [Goyal et al., 2008]. We study a relaxed version of this model where each reception is erased and replaced with a `?' independently with probability p, so the processors have knowledge of whether a bit has been corrupted. In this relaxed model, we break past the lower bound of [Goyal et al., 2008] and obtain an O(log^* n)round algorithm for all processors to learn the input with high probability. We also show an O(1)round algorithm for the same problem when the alphabet size is Omega(poly(n)).
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Broadcasting in Noisy Radio Networks
The widelystudied radio network model [Chlamtac and Kutten, 1985] is a graphbased description that captures the inherent impact of collisions in wireless communication. In this model, the strong assumption is made that node v receives a message from a neighbor if and only if exactly one of its neighbors broadcasts. We relax this assumption by introducing a new noisy radio network model in which random faults occur at senders or receivers. Specifically, for a constant noise parameter p ∈ [0,1), either every sender has probability p of transmitting noise or every receiver of a single transmission in its neighborhood has probability p of receiving noise.
We first study singlemessage broadcast algorithms in noisy radio networks and show that the Decay algorithm [BarYehuda et al., 1992] remains robust in the noisy model while the diameterlinear algorithm of Gasieniec et al., 2007 does not. We give a modified version of the algorithm of Gasieniec et al., 2007 that is robust to sender and receiver faults, and extend both this modified algorithm and the Decay algorithm to robust multimessage broadcast algorithms, broadcasting Ω(1/log n log log n) and Ω(1/log n) messages per round, respectively.
We next investigate the extent to which (network) coding improves throughput in noisy radio networks. In particular, we study the coding cap  the ratio of the throughput of coding to that of routing  in noisy radio networks. We address the previously perplexing result of Alon et al. 2014 that worst case coding throughput is no better than worst case routing throughput up to constants: we show that the worst case throughput performance of coding is, in fact, superior to that of routing  by a Θ(log(n)) gap  provided receiver faults are introduced. However, we show that sender faults have little effect on throughput. In particular, we show that any coding or routing scheme for the noiseless setting can be transformed to be robust to sender faults with only a constant throughput overhead. These transformations imply that the results of Alon et al., 2014 carry over to noisy radio networks with sender faults as well. As a result, if sender faults are introduced then there exist topologies for which there is a Θ(log log n) gap, but the worst case throughput across all topologies is Θ(1/log n) for both coding and routing.
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 NSFPAR ID:
 10121508
 Date Published:
 Journal Name:
 ACM SIGACTSIGOPS Symposium on Principles of Distributed Computing
 Page Range / eLocation ID:
 33 to 42
 Format(s):
 Medium: X
 Sponsoring Org:
 National Science Foundation
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