  Interference Alignment (IA) is the process of designing signals in such a way
that they cast overlapping shadows at their unintended receivers, while
remaining distinguishable at the intended ones. Our goal in this paper is to
come up with an algorithm for IA that runs at the transmitters only (and is
transparent to the receivers), that doesn't require channel reciprocity, and
that alleviates the need to alternate between the forward and reverse network
as is the case in Distributed IA (Gomadam, Cadambe, Jafar 08'), thereby
inducing significant overhead in certain environments where the channel changes
frequently. Most importantly, our effort is focused on ensuring that this
one-sided approach does not degrade the performance of the system w.r.t.
Distributed IA (since it cannot improve it). As a first step, we model the
interference in each receiver's desired signal as a function of the
transmitters' beamforming vectors. We then propose a simple steepest descent
(SD) algorithm and use it to minimize the interference in each receiver's
desired signal space. We mathematically establish equivalences between our
approach and the Distributed IA algorithm (Gomadam, Cadambe, Jafar 08') and
show that our algorithm also converges to an alignment solution (when the
solution is feasible).
