  Modern IEEE 802.11 (Wi-Fi) networks extensively rely on multiple-input
multiple-output (MIMO) to significantly improve throughput. To correctly
beamform MIMO transmissions, the access point needs to frequently acquire a
beamforming matrix (BM) from each connected station. However, the size of the
matrix grows with the number of antennas and subcarriers, resulting in an
increasing amount of airtime overhead and computational load at the station.
Conventional approaches come with either excessive computational load or loss
of beamforming precision. For this reason, we propose SplitBeam, a new
framework where we train a split deep neural network (DNN) to directly output
the BM given the channel state information (CSI) matrix as input. We formulate
and solve a bottleneck optimization problem (BOP) to keep computation, airtime
overhead, and bit error rate (BER) below application requirements. We perform
extensive experimental CSI collection with off-the-shelf Wi-Fi devices in two
distinct environments and compare the performance of SplitBeam with the
standard IEEE 802.11 algorithm for BM feedback and the state-of-the-art
DNN-based approach LB-SciFi. Our experimental results show that SplitBeam
reduces the beamforming feedback size and computational complexity by
respectively up to 81% and 84% while maintaining BER within about 10^-3 of
existing approaches. We also implement the SplitBeam DNNs on FPGA hardware to
estimate the end-to-end BM reporting delay, and show that the latter is less
than 10 milliseconds in the most complex scenario, which is the target channel
sounding frequency in realistic multi-user MIMO scenarios.
