DeepMux: Deep-Learning-Based Channel Sounding and Resource Allocation for IEEE 802.11ax

Sangdeh, Pedram Kheirkhah; Zeng, Huacheng

doi:10.1109/jsac.2021.3087246

Cited by 24 publications

(8 citation statements)

References 29 publications

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“…Additionally, Sangdeh and Zeng [144] address joint MU-MIMO and OFDMA optimization by using deep supervised learning (DSL). The solution, called DeepMux, is executed at the APs and relies on DNNs to minimize the impact of channel sounding and find a near-optimal resource allocation policy.…”

Section: B Multi-user Communicationmentioning

confidence: 99%

Wi-Fi Meets ML: A Survey on Improving IEEE 802.11 Performance With Machine Learning

Szott

Kosek-Szott

Gawłowicz

et al. 2022

IEEE Commun. Surv. Tutorials

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Wireless local area networks (WLANs) empowered by IEEE 802.11 (Wi-Fi) hold a dominant position in providing Internet access thanks to their freedom of deployment and configuration as well as the existence of affordable and highly interoperable devices. The Wi-Fi community is currently deploying Wi-Fi 6 and developing Wi-Fi 7, which will bring higher data rates, better multi-user and multi-AP support, and, most importantly, improved configuration flexibility. These technical innovations, including the plethora of configuration parameters, are making next-generation WLANs exceedingly complex as the dependencies between parameters and their joint optimization usually have a non-linear impact on network performance. The complexity is further increased in the case of dense deployments and coexistence in shared bands. While classical optimization approaches fail in such conditions, machine learning (ML) is able to handle complexity. Much research has been published on using ML to improve Wi-Fi performance and solutions are slowly being adopted in existing deployments. In this survey, we adopt a structured approach to describe the various Wi-Fi areas where ML is applied. To this end, we analyze over 250 papers in the field, providing readers with an overview of the main trends. Based on this review, we identify specific open challenges and provide general future research directions.

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Section: B Multi-user Communicationmentioning

confidence: 99%

Wi-Fi Meets ML: A Survey on Improving IEEE 802.11 Performance With Machine Learning

Szott

Kosek-Szott

Gawłowicz

et al. 2022

IEEE Commun. Surv. Tutorials

View full text Add to dashboard Cite

show abstract

“…This DNN-based WiFi control strategy achieves significant improvements in system throughput, average transmission delay, and packet retransmission rate. In order to improve the efficiency of downlink MU-MIMO-OFDMA transmission in 802.11ax networks, a deep learning-based channel detection (DLCS) and deep learning-based resource allocation (DLRA) approach is proposed in the literature [30]. DLCS utilizes the compression capability of DNN to compress the frequency domain CSI during the feedback process.…”

Section: Resource Allocationmentioning

confidence: 99%

AI for UAV-Assisted IoT Applications : A Comprehensive Review

Chen¹,

Wu²,

Wang³

et al. 2022

Preprint

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With the rapid development of the Internet of Things (IoT), there are dramatic increasing number of devices in the network, which causes the challenge that only using infrastructure such as based station cannot provide service with all devices with high quality. Therefore, due to their flexibility and economy, unmanned aerial vehicles (UAV) are widely used to increase the performance of IoT networks. UAVs can not only provide communication services for IoT devices in the absence of a network, but they can also perform video surveillance, cargo transportation, pesticide spraying, and other specialized tasks. However, due to the complexity of the scenario and the need for real-time decision making, it is challenging to schedule UAVs in the network using traditional optimization methods, and growing attention has focused on using AI to optimize UAVs in the network. In this paper, we focus on the AI-enabled UAV optimization method in IoT networks and give a comprehensive scope on what and how to use AI-enabled methods to increase the performance of UAV-assisted IoT networks. Moreover, a brief analysis of the challenges of using AI methods in IoT networks and some potential research directions are given.

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“…We store the raw angle values (before converting to the corresponding quantized index) of all the Ψ angles for all the subcarriers of all the simulated packets. From this generated data, we obtain the 50th percentile values of all the Ψ angles and use them as our fixed values instead of computing them for the compressed beamforming representation as shown in (6). The beamformee and the beamformer both will have complete information about the fixed vector of the Ψ angle values.…”

Section: Partial Compressed Beamforming Feedbackmentioning

confidence: 99%

“…An alternative approach to reduce the amount of information required in beamforming feedback is to transmit partial compressed beamforming feedback information. In the compressed beamforming methodology described in Section III, information about two types of angles is transmitted as the beamforming feedback, namely, Φ in (5) and Ψ in (6). However, if only Φ angle information is transmitted in the feedback, the number of angles that need to be reported in the feedback is halved.…”

Section: Partial Compressed Beamforming Feedbackmentioning

confidence: 99%