DeepMux: Deep-Learning-Based Channel Sounding and Resource Allocation for IEEE 802.11ax
DeepMux: Deep-Learning-Based Channel Sounding and Resource Allocation for IEEE 802.11ax
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DOI：
10.1109/jsac.2021.3087246
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发表时间：
2021-08
影响因子：
16.4
通讯作者：
Pedram Kheirkhah Sangdeh;Huacheng Zeng
中科院分区：
文献类型：
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作者：
Pedram Kheirkhah Sangdeh;Huacheng Zeng
MU-MIMO和OFDMA是IEEE 802.11AX标准中的两种关键技术。尽管这两种技术已经在蜂窝网络中进行了深入研究，但在802.11ax中首次将其在Wi-Fi网络中很少引入Wi-Fi网络中的关节优化。 Wi-Fi网络中这两种技术的婚姻在MAC-Layer协议和算法的实践设计中既创造了机会和挑战，又可以优化Air Time开销，光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，用于802.11AX网络。 DeepMux主要包括两个组成部分：基于深度学习的频道（DLC）和基于深度学习的资源分配（DLRA），它们都驻留在接入点（APS）中，并且对Wi-Fi客户端没有计算/通信负担。 DLC通过利用深神经网络（DNN）来减少802.11协议的通话时间开销。它使用上行链路频道来训练DNNS进行下行链路频道，从而易于实现培训过程。 DLRA采用DNN来解决混合企业资源分配问题，从而使AP在多项式时间内获得了近乎理想的解决方案。我们已经建立了一个无线测试床，以检查在现实世界环境中DeepMux的性能。我们的实验结果表明，DeepMux将声音开销降低了$ 62.0 \％\ sim 90.5 \％$，并将网络吞吐量增加$ 26.3 \％\％\ sim 43.6 \％$。
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 $26.3\%\sim 43.6\%$ .