On the Computational Power of Radio Channels

Authors Mark Braverman, Gillat Kol, Rotem Oshman, Avishay Tal

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Author Details

Mark Braverman
  • Princeton University, Princeton, NJ, USA
Gillat Kol
  • Princeton University, Princeton, NJ, USA
Rotem Oshman
  • Tel Aviv University, Israel
Avishay Tal
  • UC Berkeley, CA, USA

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Mark Braverman, Gillat Kol, Rotem Oshman, and Avishay Tal. On the Computational Power of Radio Channels. In 33rd International Symposium on Distributed Computing (DISC 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 146, pp. 8:1-8:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)


Radio networks can be a challenging platform for which to develop distributed algorithms, because the network nodes must contend for a shared channel. In some cases, though, the shared medium is an advantage rather than a disadvantage: for example, many radio network algorithms cleverly use the shared channel to approximate the degree of a node, or estimate the contention. In this paper we ask how far the inherent power of a shared radio channel goes, and whether it can efficiently compute "classicaly hard" functions such as Majority, Approximate Sum, and Parity. Using techniques from circuit complexity, we show that in many cases, the answer is "no". We show that simple radio channels, such as the beeping model or the channel with collision-detection, can be approximated by a low-degree polynomial, which makes them subject to known lower bounds on functions such as Parity and Majority; we obtain round lower bounds of the form Omega(n^{delta}) on these functions, for delta in (0,1). Next, we use the technique of random restrictions, used to prove AC^0 lower bounds, to prove a tight lower bound of Omega(1/epsilon^2) on computing a (1 +/- epsilon)-approximation to the sum of the nodes' inputs. Our techniques are general, and apply to many types of radio channels studied in the literature.

Subject Classification

ACM Subject Classification
  • Theory of computation → Distributed computing models
  • Theory of computation → Communication complexity
  • radio channel
  • lower bounds
  • approximate majority


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