  Performing link adaptation in a multiantenna and multiuser system is
challenging because of the coupling between precoding, user selection, spatial
mode selection and use of limited feedback about the channel. The problem is
exacerbated by the difficulty of selecting the proper modulation and coding
scheme when using orthogonal frequency division multiplexing (OFDM). This paper
presents a data-driven approach to link adaptation for multiuser multiple input
mulitple output (MIMO) OFDM systems. A machine learning classifier is used to
select the modulation and coding scheme, taking as input the SNR values in the
different subcarriers and spatial streams. A new approximation is developed to
estimate the unknown interuser interference due to the use of limited feedback.
This approximation allows to obtain SNR information at the transmitter with a
minimum communication overhead. A greedy algorithm is used to perform spatial
mode and user selection with affordable complexity, without resorting to an
exhaustive search. The proposed adaptation is studied in the context of the
IEEE 802.11ac standard, and is shown to schedule users and adjust the
transmission parameters to the channel conditions as well as to the rate of the
feedback channel.
