A Pointer Network Based Deep Learning Algorithm for User Pairing in Full-Duplex Wi-Fi Networks

Zhang, Yirun; Wu, Qirui; Shikh-Bahaei, Mohammad

doi:10.1109/tvt.2020.3011613

Cited by 7 publications

(7 citation statements)

References 12 publications

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“…ML techniques also improve the operation of a wide variety of advanced mechanisms that include multi-band WLAN operation [167], multi-AP coordination for network MIMO [168], and in-band full-duplex [169]. Both RL and SL techniques are used.…”

Section: E Multi-link Operation Network Mimo and Full-duplexmentioning

confidence: 99%

“…For example, DRL considers both channel allocation and AP clustering to maximize the performance of distributed MIMO transmissions [168]. Similarly, NNs predict channel states and so improve the performance of multi-band WLANs [167] and to find groups of stations that enable full-duplex communication at the APs [169]. In the following, we overview these solutions in more detail.…”

Section: E Multi-link Operation Network Mimo and Full-duplexmentioning

confidence: 99%

See 1 more Smart Citation

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: E Multi-link Operation Network Mimo and Full-duplexmentioning

confidence: 99%

Section: E Multi-link Operation Network Mimo and Full-duplexmentioning

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

“…E. Multi-band, Network MIMO, and Full-duplex ML techniques are also applied to improve the operation of a wide variety of advanced mechanisms that include multiband WLAN operation [141], multi-AP coordination [142], and in-band full-duplex [143]. In these papers, we find that both RL and SL techniques are used.…”

Section: Channel Bondingmentioning

confidence: 99%

“…For example, DRL is used in [142] to jointly perform channel allocation and AP clustering to maximize the performance of Distributed MIMO transmissions. Similarly, NNs are used in [141] to predict future channel states and so improve the performance of multi-band WLANs, and in [143] to find groups of stations that enable full-duplex communication at the APs. In the following, we overview them in more detail.…”

Section: Channel Bondingmentioning

confidence: 99%

“…In WLANs, a key challenge to solve is the user pairing problem: finding groups of different stations that allow the AP to transmit to one while receiving from another. To solve this combinatorial problem, which becomes impractical when the number of stations is high, in [143] a DL approach is considered through a 'pointer network'. The main benefit of this solution is that the NN does not need to be re-trained when the length of input (e.g., the number of users) changes within an expected range.…”

Section: Channel Bondingmentioning

confidence: 99%

See 1 more Smart Citation

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

Szott,

Kosek-Szott,

Gawłowicz

et al. 2021

Preprint

View full text Add to dashboard Cite

Wireless local area networks (WLANs) empowered by IEEE 802.11 (WiFi) hold a dominant position in providing Internet access thanks to their freedom of deployment and configuration as well as affordable and highly interoperable devices. The WiFi community is currently deploying WiFi 6 and developing WiFi 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 classic optimization approaches fail in such conditions, machine learning (ML) is well known for being able to handle complexity. Much research has been published on using ML to improve WiFi performance and solutions are slowly being adopted in existing deployments. In this survey, we adopt a structured approach to describing the various areas where WiFi can be enhanced using ML. To this end, we analyze over 200 papers in the field providing readers with an overview of the main trends. Based on this review, we identify both open challenges in each WiFi performance area as well as general future research directions.

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Deep reinforcement learning‐based full‐duplex link scheduling in federated learning‐based computing for IoMT

Guan

et al. 2023

Trans Emerging Tel Tech

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Summary The rapid developments in mini‐hardware manufacturing and wireless network communications have enabled the Internet of Medical Things (IoMT) to provide continuous healthcare services over the Internet. Federated learning (FL) combined with blockchain technology has been a popular way to resolve privacy‐preserving data sharing in IoMT‐based wireless body area networks (WBANs), on the other side, communication payloads become much heavier than traditional healthcare sensor network, because central server should aggregate the model updates and orchestrate the training tasks. The high latency will lead to FL's low system efficiency. However, the existing studies on FL mainly focus on the system design and algorithm optimization, which ignore a critical problem of data transmission in the FL system. To improve the communication performance, we proposed a two‐tier scheduling algorithm in which a full‐duplex (FD) multiple access based scheduling algorithm is employed to improve channel utility and network throughput, and decrease the delay in tier II. A deep reinforcement learning (DRL) framework is used to generate the FD links between hubs and access points (APs) which jointly considers the channel state, fairness, and delay. Therefore, the DRL‐based FD Link Scheduling (R‐FDLS) algorithm is proposed. When the traffic volume is different or in various distribution scenarios, the evaluation results demonstrate that the proposed algorithm significantly improves the network communication quality, as well as has obvious advantages compared to several baselines.

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A Pointer Network Based Deep Learning Algorithm for User Pairing in Full-Duplex Wi-Fi Networks

Cited by 7 publications

References 12 publications

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

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

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

Deep reinforcement learning‐based full‐duplex link scheduling in federated learning‐based computing for IoMT

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