  Motivated by mobile edge computing and wireless data centers, we study a
wireless distributed computing framework where the distributed nodes exchange
information over a wireless interference network. Our framework follows the
structure of MapReduce. This framework consists of Map, Shuffle, and Reduce
phases, where Map and Reduce are computation phases and Shuffle is a data
transmission phase. In our setting, we assume that the transmission is operated
over a wireless interference network. We demonstrate that, by duplicating the
computation work at a cluster of distributed nodes in the Map phase, one can
reduce the amount of transmission load required for the Shuffle phase. In this
work, we characterize the fundamental tradeoff between computation load and
communication load, under the assumption of one-shot linear schemes. The
proposed scheme is based on side information cancellation and zero-forcing, and
we prove that it is optimal in terms of computation-communication tradeoff. The
proposed scheme outperforms the naive TDMA scheme with single node transmission
at a time, as well as the coded TDMA scheme that allows coding across data, in
terms of the computation-communication tradeoff.
