Wi-Fi Channel Selection Based on Urban Interference Measurement

Kajita, Shugo; Amano, Tatsuya; Yamaguchi, Hirozumi; Higashino, Teruo; Takai, Mineo

doi:10.1145/2994374.2994402

Cited by 6 publications

(3 citation statements)

References 9 publications

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“…In some cases, interference avoidance schemes can be used to avoid ACI. For instance, while the 2.4 GHz Wi-Fi band supports a total of 11 channels, it is recommended that Wi-Fi users in the United States utilize non-overlapping channels (take for example: channels 1, 6 and 11) [29].…”

Section: ) Adjacent Channel Interferencementioning

confidence: 99%

Interference Suppression Using Deep Learning: Current Approaches and Open Challenges

2022

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In light of the finite nature of the wireless spectrum and the increasing demand for spectrum use arising from recent technological breakthroughs in wireless communication, the problem of interference continues to persist. Despite recent advancements in resolving interference issues, interference still presents a difficult challenge to effective usage of the spectrum. This is partly due to the rise in the use of license-free and managed shared bands as well as other opportunistic spectrum access solutions. As a result of this, the need for efficient spectrum usage schemes that are robust against interference has never been more important. In the past, most solutions to interference have addressed the problem by using avoidance techniques as well as mitigation approaches based on expert systems. More recently, researchers have successfully explored artificial intelligence/machine learning enabled physical layer techniques, especially deep learning which reduces or compensates for the interfering signal instead of simply avoiding it. In this paper, we address the knowledge gap in literature with respect to the state-of-the-art in deep learning-based interference suppression. Specifically, we review a wide range of techniques that have used deep learning to suppress interference by learning interference characteristics directly from data, rather than relying on expert systems. We provide a thorough technical discussion of the prominent deep learning algorithms that have been proposed in the literature and provide comparison and guidelines regarding their successful implementation in this application. In addition, we highlight challenges and potential future research directions for the successful adoption of deep learning in this critical field.

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Section: ) Adjacent Channel Interferencementioning

confidence: 99%

Interference Suppression Using Deep Learning: Current Approaches and Open Challenges

2022

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“…For example, Alice will send her recorded timstamps to Bob, who will compare his timestamps and keep the common ones. Bob 3 In the urban area, the interference may be chaotic, because of the densely deployed access points [40]. The interference will impact the channel measurements accuracy but will not affect randomness nature of the wireless link between users.…”

Section: A Proceduresmentioning

confidence: 99%

“…In the urban area, the interference may be chaotic, because of the densely deployed access points[40]. The interference will impact the channel measurements accuracy but will not affect randomness nature of the wireless link between users.…”

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confidence: 99%

Securing Wireless Communications of the Internet of Things from the Physical Layer, An Overview

Zhang

Duong

Woods

et al. 2017

Entropy

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Abstract:The security of the Internet of Things (IoT) is receiving considerable interest as the low power constraints and complexity features of many IoT devices are limiting the use of conventional cryptographic techniques. This article provides an overview of recent research efforts on alternative approaches for securing IoT wireless communications at the physical layer, specifically the key topics of key generation and physical layer encryption. These schemes can be implemented and are lightweight, and thus offer practical solutions for providing effective IoT wireless security. Future research to make IoT-based physical layer security more robust and pervasive is also covered.

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Outdoor Wi-Fi RSSI Map Construction Based on Crowdsourcing and Simulation

Amano

Higashino

2019

2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)

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Wi-Fi Channel Selection Based on Urban Interference Measurement

Cited by 6 publications

References 9 publications

Interference Suppression Using Deep Learning: Current Approaches and Open Challenges

Interference Suppression Using Deep Learning: Current Approaches and Open Challenges

Securing Wireless Communications of the Internet of Things from the Physical Layer, An Overview

Outdoor Wi-Fi RSSI Map Construction Based on Crowdsourcing and Simulation

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