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.

我们提出了 SplitBeam，这是一个新的框架，通过训练一个分裂的深度神经网络（DNN）来直接输出给定信道状态信息（CSI）矩阵作为输入的波束成形矩阵（BM），以解决现有方法中存在的过多计算负载或波束成形精度损失的问题。我们对两个不同环境中的现有 Wi-Fi 设备进行了大量实验 CSI 采集，并将 SplitBeam 与标准 IEEE 802.11 算法和最先进的基于 DNN 的方法 LB-SciFi 的性能进行了比较。实验结果显示，SplitBeam 将波束成形反馈大小和计算复杂度分别降低了 81% 和 84%，同时将误码率（BER）维持在现有方法的 10^-3 左右。我们还在 FPGA 硬件上实现了 SplitBeam DNN，估计了端到端的 BM 报告延迟，结果显示在最复杂的情景中，即逼真的多用户 MIMO 场景中的目标信道探测频率，延迟小于 10 毫秒。