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.

翻译：现代IEEE 802.11（Wi-Fi）网络广泛依赖多输入多输出（MIMO）技术来显著提升吞吐量。为正确实现MIMO传输的波束赋形，接入点需要频繁从每个关联站点获取波束赋形矩阵（BM）。然而，该矩阵的规模随天线数和子载波数增长，导致站点端的空口开销和计算负载不断增加。传统方法要么计算负载过高，要么损失波束赋形精度。为此，我们提出SplitBeam——一种新型框架，通过训练拆分深度神经网络（DNN）直接以信道状态信息（CSI）矩阵为输入输出BM。我们构建并求解瓶颈优化问题（BOP），以确保计算量、空口开销和误码率（BER）均低于应用需求。我们使用商用Wi-Fi设备在两个不同环境中进行了广泛的实验性CSI采集，并将SplitBeam与标准IEEE 802.11 BM反馈算法及当前最先进的基于DNN的方法LB-SciFi进行了性能对比。实验结果表明，SplitBeam可将波束赋形反馈大小和计算复杂度分别降低高达81%和84%，同时维持BER与现有方法相差约10^-3以内。我们还在FPGA硬件上实现了SplitBeam DNN以评估端到端BM上报延迟，结果表明在最复杂场景下该延迟小于10毫秒——这恰好是现实多用户MIMO场景中的目标信道探测频率。