  The emergence of interference alignment (IA) as a degrees-of-freedom optimal
strategy motivates the need to investigate whether IA can be leveraged to aid
conventional network optimization algorithms that are only capable of finding
locally optimal solutions. To test the usefulness of IA in this context, this
paper proposes a two-stage optimization framework for the downlink of a
$G$-cell multi-antenna network with $K$ users/cell. The first stage of the
proposed framework focuses on nulling interference from a set of dominant
interferers using IA, while the second stage optimizes transmit and receive
beamformers to maximize a network-wide utility using the IA solution as the
initial condition. Further, this paper establishes a set of new feasibility
results for partial IA that can be used to guide the number of dominant
interferers to be nulled in the first stage. Through simulations on specific
topologies of a cluster of base-stations, it is observed that the impact of IA
depends on the choice of the utility function and the presence of
out-of-cluster interference. In the absence of out-of-cluster interference, the
proposed framework outperforms straightforward optimization when maximizing the
minimum rate, while providing marginal gains when maximizing sum-rate. However,
the benefit of IA is greatly diminished in the presence of significant
out-of-cluster interference.
