SplitBeam: Effective and Efficient Beamforming in Wi-Fi Networks Through Split Computing
SplitBeam: Effective and Efficient Beamforming in Wi-Fi Networks Through Split Computing
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DOI：
10.1109/icdcs57875.2023.00081
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发表时间：
2023-07
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影响因子：
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通讯作者：
Niloofar Bahadori;Yoshitomo Matsubara;Marco Levorato;Francesco Restuccia
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作者：
Niloofar Bahadori;Yoshitomo Matsubara;Marco Levorato;Francesco Restuccia
现代IEEE 802.11（Wi-Fi）网络广泛依赖于多输入多输出（MIMO）来显著提高吞吐量。为了正确地对MIMO传输进行波束成形，接入点需要频繁地从每个连接的站获取波束成形矩阵（BM）。然而，矩阵的大小随着天线和子载波的数量而增长，导致在站处的空中时间开销和计算负载的量增加。传统的方法要么带来过多的计算负载，要么带来波束成形精度的损失。出于这个原因，我们提出了SplitBeam，这是一个新的框架，在这个框架中，我们训练一个分裂的深度神经网络（DNN），以直接输出BM，给定信道状态信息（CSI）矩阵作为输入。DNN被设计为具有附加的“瓶颈”层，以将原始DNN“分割”成分别由站和接入点执行的头部模型和尾部模型。头部模型生成BM的压缩表示，然后AP使用该压缩表示来使用尾部模型产生BM。我们制定和解决瓶颈优化问题（BOP），以保持计算，通话时间开销和误码率（BER）低于应用程序的要求。我们在两种不同的环境中使用现成的Wi-Fi设备进行了广泛的实验CSI收集，并将SplitBeam的性能与标准IEEE 802.11 BM反馈算法和最先进的基于DNN的方法LB-SciFi进行了比较。我们的实验结果表明，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BER保持在现有方法的10−3以内。我们还在FPGA硬件上实现了SplitBeam DNN来估计端到端BM报告延迟，并表明后者在最复杂的场景中小于10毫秒，这是现实多用户MIMO场景中的目标信道探测频率。为了实现完全的可重复性，我们将向社区发布我们的代码和数据集。
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. The DNN is designed with an additional “bottleneck” layer to “split” the original DNN into a head model and a tail model, respectively executed by the station and the access point. The head model generates a compressed representation of the BM, which is then used by the AP to produce the BM using the tail model. 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. To allow full reproducibility, we will release our code and datasets to the community.