Designing WI-FI Wireless Networks with High Density of Subscriber Service

Gadasin, D. V.; Shvedov, A. V.; Vakurin, I. S.

doi:10.1109/ieeeconf48371.2020.9078567

Cited by 8 publications

(1 citation statement)

References 1 publication

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“…Future communication networks need to provide high data rates to users in an energy ef icient way. Wi-Fi is a very popular type of wireless network, and there were 22.2 billion Wi-Fi devices in 2021 [1]. Therefore, a slight future improvement on Wi-Fi can signi icantly improve productivity and make a positive impact to the environment.…”

Section: Introductionmentioning

confidence: 99%

Optimal wireless rate and power control in the presence of jammers using reinforcement learning

Raji¹,

Liu²

2022

ITU J-FET

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Future wireless networks require high throughput and energy efficiency. This paper studies using Reinforcement Learning (RL) to do transmission rate and power control for maximizing a joint reward function consisting of both throughput and energy consumption. We design the system state to include factors that reflect packet queue length, interference from other nodes, quality of the wireless channel, battery status, etc. The reward function is normalized and does not involve unit conversion. It can be used to train three different types of agents: throughput-critical, energy-critical, and throughput and energy balanced. Using the NS-3 network simulation software, we implement and train these agents in an 802.11ac network with the presence of a jammer. We then test the agents with two jamming nodes interfering with the packets received at the receiver. We compare the performance of our RL optimal policies with the popular Minstrel rate adaptation algorithm: our approach can achieve (i) higher throughput when using the throughput-critical reward function; (ii) lower energy consumption when using the energy-critical reward function; and (iii) higher throughput and slightly higher energy when using the throughput and energy balanced reward function. Although our discussion is focused on 802.11ac networks, our method is readily applicable to other types of wireless networks.

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Section: Introductionmentioning

confidence: 99%

Optimal wireless rate and power control in the presence of jammers using reinforcement learning

Raji¹,

Liu²

2022

ITU J-FET

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RETRACTED ARTICLE: Creating Security Modelling Framework Analysing in Internet of Things Using EC-GSM-IoT

et al. 2021

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The article was submitted to be part of a guest-edited issue. An investigation by the publisher found a number of articles, including this one, with a number of concerns, including but not limited to compromised editorial handling and peer review process, inappropriate or irrelevant references or not being in scope of the journal or guest-edited issue. Based on the investigation's findings the Editor-in-Chief therefore no longer has confidence in the results and conclusions of this article.Authors S. Baskar and P. Mohamed Shakeel disagree with this retraction. The other authors have not responded to correspondence regarding this retraction.

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Determining Cluster Distance when Designing High - Density WI-FI

Vakurin

Gadasin

2020

2020 International Conference on Engineering Management of Communication and Technology (EMCTECH)

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Designing WI-FI Wireless Networks with High Density of Subscriber Service

Cited by 8 publications

References 1 publication

Optimal wireless rate and power control in the presence of jammers using reinforcement learning

Optimal wireless rate and power control in the presence of jammers using reinforcement learning

RETRACTED ARTICLE: Creating Security Modelling Framework Analysing in Internet of Things Using EC-GSM-IoT

Determining Cluster Distance when Designing High - Density WI-FI

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