  A fundamental problem arising in dense wireless networks is the high
co-channel interference. Interference alignment (IA) was recently proposed as
an effective way to combat interference in wireless networks. The concept of
IA, though, is originated by the capacity study of interference channels and as
such, its performance is mainly gauged under ideal assumptions, such as
instantaneous and perfect channel state information (CSI) at all nodes, and
homogeneous signal-to-noise ratio (SNR) users, i.e., each user has the same
average SNR. Consequently, the performance of IA under realistic conditions has
not been completely investigated yet. In this paper, we aim at filling this gap
by providing a performance assessment of spatial IA in practical systems.
Specifically, we derive a closed-form expression for the IA average sum-rate
when CSI is acquired through training and users have heterogeneous SNR. A main
insight from our analysis is that IA can indeed provide significant spectral
efficiency gains over traditional approaches in a wide range of dense network
scenarios. To demonstrate this, we consider the examples of linear, grid and
random network topologies.
