Design and Implementation for Deep Learning Based Adjustable Beamforming Training for Millimeter Wave Communication Systems

Shen, Li-Hsiang; Chang, Ting-Wei; Feng, Kai-Ten; Huang, Po-Tsang

doi:10.1109/tvt.2021.3058715

Cited by 21 publications

(8 citation statements)

References 33 publications

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“…To facilitate the beam sector pair selection, Chang et al [119] replace the standard method of an exhaustive beam search with one of three neural network (NN)-based algorithms to predict the optimal beam sector, including with historical data. This work is extended by Shen et al [120] with the training duration reduced through a combination of SL-based feature extraction and RL-based training beam selection. Meanwhile, Polese et al [121] develop DeepBeam, a framework for beam selection that replaces the time-consuming beam sweeping procedure with inferring the beam sector to use through deep learning based on passive listening to other transmissions.…”

Section: A Beamformingmentioning

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: A Beamformingmentioning

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

“…To facilitate the beam sector pair selection, Chang et al [101] propose to replace the standard method of exhaustive beam search with one of three neural network (NN)-based algorithms proposed to predict the optimal beam sector, including the use of historical data. This work is then extended in [111], where the training duration is reduced through a combination of SL-based feature extraction and RL-based training beam selection. Meanwhile, Polese et al [112] developed DeepBeam, Alternatively, improved ML-based beam alignment predictions can be performed with the use of camera images.…”

Section: A Beamformingmentioning

confidence: 99%

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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“…Here, an LSTM‐based deep learning model has been developed to model the traffic pattern for resource allocation under Quality of Service (QoS) requirements. A learning‐based adjustable beam number training (LABNT) algorithm has been developed in [34] for optimal beam direction and reduced training overhead. The trade‐off between beam alignment accuracy and spectral efficiency in beamforming training for non‐line‐of‐sight mmWave systems has been demonstrated.…”

Section: Introductionmentioning

confidence: 99%

Deep learning integrated reinforcement learning for adaptive beamforming in B5G networks

et al. 2022

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In this paper, a deep learning integrated reinforcement learning (DLIRL) algorithm is proposed for comprehending intelligent beamsteering in Beyond Fifth Generation (B5G) networks. The smart base station in B5G networks aims to steer the beam towards appropriate user equipment based on the acquaintance of isotropic transmissions. The foremost methodology is to optimize beam direction through reinforcement learning that delivers significant improvement in signal to noise ratio (SNR). This includes alternate path finding during path obstruction and steering the beam appropriately between the smart base station and user equipment. The DLIRL is realized through supervised learning with deep neural networks and deep Q-learning schemes. The proposed algorithm comprises of an online learning phase for training the weights and a working phase for carrying out the prediction. Results confirm that the performance of the B5G system is improved considerably as compared to its counterparts with a spectral efficiency of 11 bps/Hz at SNR = 10 dB for a bit error rate performance of 10 −5 . As compared to reinforced learning and deep neural network with a deviation of ±3 o and ±5 • , respectively, the DLIRL beamforming displays a deviation of ±2 o . Moreover, the DLIRL can track the user equipment and steer the beam in its direction with an accuracy of 92%.

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Design and Implementation for Deep Learning Based Adjustable Beamforming Training for Millimeter Wave Communication Systems

Cited by 21 publications

References 33 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 learning integrated reinforcement learning for adaptive beamforming in B5G networks

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