MU-MIMO and OFDMA are two key techniques in IEEE 802.11ax standard. Although these two techniques have been intensively studied in cellular networks, their joint optimization in Wi-Fi networks has been rarely explored as OFDMA was introduced to Wi-Fi networks for the first time in 802.11ax. The marriage of these two techniques in Wi-Fi networks creates both opportunities and challenges in the practical design of MAC-layer protocols and algorithms to optimize airtime overhead, spectral efficiency, and computational complexity. In this paper, we present DeepMux, a deep-learning-based MU-MIMO-OFDMA transmission scheme for 802.11ax networks. DeepMux mainly comprises two components: deep-learning-based channel sounding (DLCS) and deep-learning-based resource allocation (DLRA), both of which reside in access points (APs) and impose no computational/communication burden on Wi-Fi clients. DLCS reduces the airtime overhead of 802.11 protocols by leveraging the deep neural networks (DNNs). It uses uplink channels to train the DNNs for downlink channels, making the training process easy to implement. DLRA employs a DNN to solve the mixed-integer resource allocation problem, enabling an AP to obtain a near-optimal solution in polynomial time. We have built a wireless testbed to examine the performance of DeepMux in real-world environments. Our experimental results show that DeepMux reduces the sounding overhead by $62.0\%\sim 90.5\%$ and increases the network throughput by $26.3\%\sim 43.6\%$ .

中文翻译：

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DeepMux：面向 IEEE 802.11ax 的基于深度学习的信道探测和资源分配

MU-MIMO 和 OFDMA 是 IEEE 802.11ax 标准中的两项关键技术。尽管这两种技术已在蜂窝网络中得到深入研究，但由于在 802.11ax 中首次将 OFDMA 引入 Wi-Fi 网络，因此很少探索它们在 Wi-Fi 网络中的联合优化。这两种技术在 Wi-Fi 网络中的结合为 MAC 层协议和算法的实际设计创造了机遇和挑战，以优化通话时间开销、频谱效率和计算复杂性。在本文中，我们介绍了 DeepMux，这是一种基于深度学习的 MU-MIMO-OFDMA 传输方案，适用于 802.11ax 网络。DeepMux 主要包括两个部分：基于深度学习的信道探测（DLCS）和基于深度学习的资源分配（DLRA），两者都驻留在接入点 (AP) 中，并且不会对 Wi-Fi 客户端施加计算/通信负担。DLCS 通过利用深度神经网络 (DNN) 减少 802.11 协议的通话时间开销。它使用上行通道来训练下行通道的 DNN，使训练过程易于实施。DLRA 使用 DNN 来解决混合整数资源分配问题，使 AP 能够在多项式时间内获得接近最优的解决方案。我们已经构建了一个无线测试平台来检查 DeepMux 在真实环境中的性能。我们的实验结果表明，DeepMux 减少了探测开销 使培训过程易于实施。DLRA 使用 DNN 解决混合整数资源分配问题，使 AP 能够在多项式时间内获得接近最优的解决方案。我们构建了一个无线测试平台来检查 DeepMux 在真实环境中的性能。我们的实验结果表明，DeepMux 减少了探测开销 使培训过程易于实施。DLRA 使用 DNN 解决混合整数资源分配问题，使 AP 能够在多项式时间内获得接近最优的解决方案。我们已经构建了一个无线测试平台来检查 DeepMux 在真实环境中的性能。我们的实验结果表明，DeepMux 减少了探测开销 $62.0\%\sim 90.5\%$ 并通过以下方式增加网络吞吐量 $26.3\%\sim 43.6\%$ .