A Deep Learning Model for Wireless Channel Quality Prediction

Herath, J. Dinal; Seetharam, A.; Ramesh, A.

doi:10.1109/icc.2019.8761934

Cited by 26 publications

(15 citation statements)

References 13 publications

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“…At the PHY layer, a variety of actions can be supported by ML techniques to improve the performance of WiFi networks. Issues that can be addressed include collision detection characterization [65] and its mitigation [66], [67], interference power-level characterization [70] and its mitigation [73], signal de-noising [69], source detection to improve spectral efficiency [95], prediction of signal strength variability [72], or the enhanced modeling of the PHY and MAC layer interactions to improve throughput [68]. As depicted in Fig.…”

Section: E Phy Featuresmentioning

confidence: 99%

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

Szott,

Kosek-Szott,

Gawłowicz

et al. 2021

Preprint

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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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Section: E Phy Featuresmentioning

confidence: 99%

“…The received signal strength can be predicted though deep learning techniques [72]. In an RNN model, encoder and decoder components are implemented to capture the CSI and predict its variability, respectively.…”

Section: E Phy Featuresmentioning

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

show abstract

“…The UE continues to move until a total of T = 3000 samples spaced 20 ms apart (60 s for each trajectory, in accordance to the beam measurement periodicity from 3GPP [33]) are collected. We refer to these T = 3000 samples as a trajectory 14 . A total of 200 trajectories (100 for training and 100 for testing) are generated.…”

Section: B Mobilitymentioning

confidence: 99%

“…The range signifies 13 It is ensured that the UE does not start inside any of the buildings (through a binary occupancy grid). 14 Multiple routes might be generated during the trajectory until the required number of samples are collected. Multiple routes are connected together by their end/start points i.e., the end point of the older route becomes the start point of the new route, ensuring continuity.…”

Section: Hand Blockage Modelingmentioning

confidence: 99%

Multi-cell Multi-beam Prediction using Auto-encoder LSTM for mmWave systems

Shah¹,

Rangan²

2021

Preprint

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“…As shown in Table VI, supervised deep learning has been employed for channel prediction, mobility prediction, traffic prediction, and QoS or QoE prediction. The reason why supervised deep learning algorithms are very powerful in prediction is that it is very convenient to collect a large number of labeled training samples from the history data, such as trajectories [45,218], channel and traffic variations [191,210], and QoS experienced by users [219].…”

Section: B Predictions In Urllcmentioning

confidence: 99%

A Tutorial on Ultra-Reliable and Low-Latency Communications in 6G: Integrating Domain Knowledge into Deep Learning

She¹,

Sun²,

Gu³

et al. 2020

Preprint

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As one of the key communication scenarios in the 5th and also the 6th generation (6G) cellular networks, ultra-reliable and low-latency communications (URLLC) will be central for the development of various emerging mission-critical applications. The state-of-the-art mobile communication systems do not fulfill the end-to-end delay and overall reliability requirements of URLLC. A holistic framework that takes into account latency, reliability, availability, scalability, and decision-making under uncertainty is lacking. Driven by recent breakthroughs in deep neural networks, deep learning algorithms have been considered as promising ways of developing enabling technologies for URLLC in future 6G networks. This tutorial illustrates how to integrate theoretical knowledge (models, analysis tools, and optimization frameworks) of wireless communications into different kinds of deep learning algorithms for URLLC. We first introduce the background of URLLC and review promising network architectures and deep learning frameworks in 6G. To better illustrate how to improve learning algorithms with theoretical knowledge, we revisit model-based analysis tools and cross-layer optimization frameworks for URLLC. Following that, we examine the potential of applying supervised/unsupervised deep learning and deep reinforcement learning in URLLC and summarize related open problems. Finally, we provide simulation and experimental results to validate the effectiveness of different learning algorithms and discuss future directions.

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A Deep Learning Model for Wireless Channel Quality Prediction

Cited by 26 publications

References 13 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

Multi-cell Multi-beam Prediction using Auto-encoder LSTM for mmWave systems

A Tutorial on Ultra-Reliable and Low-Latency Communications in 6G: Integrating Domain Knowledge into Deep Learning

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