  The next-generation wireless networks are envisioned to support large-scale
sensing and distributed machine learning, thereby enabling new intelligent
mobile applications. One common network operation will be the aggregation of
distributed data (such as sensor observations or AI-model updates) for
functional computation (e.g., averaging) so as to support large-scale sensing
and distributed machine learning. An efficient solution for data aggregation,
called "over-the-air computation" (AirComp), embeds functional computation into
simultaneous access by many edge devices. Such schemes exploit the waveform
superposition of a multi-access channel to allow an access point to receive a
desired function of simultaneous signals. In this work, we aim at realizing
AirComp in a two-cell multi-antenna system. To this end, a novel scheme of
simultaneous signal-and-interference alignment (SIA) is proposed that builds on
classic IA to manage interference for multi-cell AirComp. The principle of SIA
is to divide the spatial channel space into two subspaces with equal
dimensions: one for signal alignment required by AirComp and the other for
inter-cell IA. As a result, the number of interference-free spatially
multiplexed functional streams received by each AP is maximized (equal to half
of the available spatial degrees-of-freedom). Furthermore, the number is
independent of the population of devices in each cell. In addition, the
extension to SIA for more than two cells is discussed.
