Interference-Aware Adaptive Beam Alignment for Hyper-Dense IEEE 802.11ax Internet-of-Things Networks

Kwon, Dohyun; Kim, Sang–Wook; Kim, Joongheon; Mohaisen, Aziz

doi:10.3390/s18103364

Cited by 6 publications

(2 citation statements)

References 23 publications

(26 reference statements)

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“…The 802.11ax is a new technique in the Wi-Fi with OFDMA and Multi-User Multiple Inputs Multiple Outputs (MU-MIMO), so most previous work was to explore the difference that exists at the physical layer and energy saving advances through the tutorial (Khorov et al, 2019;Bellalta & Kosek-Szott, 2019), surveys (Qu et al, 2019) and the main challenges (Afaqui et al, 2016), performance analysis (K. H. Lee, 2019a), QoS evaluation (Sanchez-Mahecha et al, 2018), and solving some of the problems faced by the IoT when using 802.11ax (Kwon et al, 2018).…”

Section: 11ax Scheduling Basedmentioning

confidence: 99%

Developing A New Scheduling Algorithm for Wi-Fi 6 Technology Based on Machine Learning

MASRİ¹,

ERDAL²

2022

Uluslararası Muhendislik Arastirma Ve Gelistirme Dergisi

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Wireless mobile communications have experienced tremendous growth in the number of users, data rate requirements, and coverage in recent years. As the data rate and system throughput requirements increase, researchers and system designers need to develop efficient methods to meet these requirements with reasonable effort and cost. In this paper, we discuss an efficient approach to deal with diminishing the overhead of Downlink (DL) and feedback using Machine Learning (ML) for 802.11ax. In particular, the antennas that are related to the router were divided into two gatherings. We used good samples of Channel State Information (CSI) that were taken from an open-access dataset and used it to train our linear regression model. The first group of antennas was used as input to our model and the second group was used as the output of the model. In the online mode, we need to estimate only one group of antennas and for the second group, we can predict it using the trained linear regression model by using the estimated CSI group as input to the model. Therefore, the output of the model using as the CSI for the second group. In this way, we can reduce the overhead of the DL in the router as shown in the result table so the router will work more efficiently compared to the existing systems. From the results table in the last section, the average sum rate has increased between 20% and 30%.

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Section: 11ax Scheduling Basedmentioning

confidence: 99%

Developing A New Scheduling Algorithm for Wi-Fi 6 Technology Based on Machine Learning

MASRİ¹,

ERDAL²

2022

Uluslararası Muhendislik Arastirma Ve Gelistirme Dergisi

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“…To make good use of the advantages, it is necessary to decide what to offload and when to offload. The execution delay and energy consumption of each terminal device can be distinguished into two parts, i.e., local computing and edge computing as explained in (1) and (2). In edge computing architectures, the values include the transmission to edge server, processing on the edge server, and downloading from the edge server.…”

Section: Offloading Decision: Trade-off Between Energy Consumption Anmentioning

confidence: 99%

Adaptive Real-Time Offloading Decision-Making for Mobile Edges: Deep Reinforcement Learning Framework and Simulation Results

et al. 2020

Self Cite

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This paper proposes a novel dynamic offloading decision method which is inspired by deep reinforcement learning (DRL). In order to realize real-time communications in mobile edge computing systems, an efficient task offloading algorithm is required. When the decision of actions (offloading enabled, i.e., computing in clouds or offloading disabled, i.e., computing in local edges) is made by the proposed DRL-based dynamic algorithm in each unit time, it is required to consider real-time/seamless data transmission and energy-efficiency in mobile edge devices. Therefore, our proposed dynamic offloading decision algorithm is designed for the joint optimization of delay and energy-efficient communications based on DRL framework. According to the performance evaluation via data-intensive simulations, this paper verifies that the proposed dynamic algorithm achieves desired performance. IntroductionAccording to the fact that the 5G era has been realized based on networking technology innovation, many new computing and communications paradigms are introduced such as millimeter-wave communications, hyper-dense networks, and device-to-device proximal networking [1-3]. Among them, mobile edge computing (MEC) is one of the major technologies for realizing data/computing distribution in order to improve cellular network performance [4]. Based on the benefits of MEC technologies such as data rate improvement and quality enhancement, mobile cellular users can enjoy high quality, seamless, and real-time communication networking services.Together with the MEC technologies, many networked components are also of interest such as cloud servers and connected devices/vehicles. As the number of connected devices/vehicles increases, the amount of data transmitted to the MEC is also rapidly increasing. This obviously introduces serious network limitations such as data processing performance limits, storage capacity limits, and the battery use of terminal devices. Under the consideration of these limitations and problems, the use of cloud computing server is more efficient to deal with big data (gathered from connected vehicles/devices via MEC edges) than the local computing on terminals. On the other hand, for any cases where the data cannot be handled in the cloud servers due to delay requirements and security reasons, MEC devices should be able to handle or process the data from the connected vehicles/devices. Therefore, we can observe the trade-off between cloud computing servers and MEC servers (local edge computing servers). In summary, cloud servers have more power and centralized computing benefits comparing to MEC servers, whereas, it may have limitations when we have delay requirements and security requirements/regulations. Figure 1 shows the combined architecture of local edge computing and cloud computing. As the transmission from connected vehicles/devices (i.e., data upload and download) delay goes down, it is much more suitable for real-time processing. It enables real-time data processing and transmission using high-quality ...

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Dynamic Resource Allocation and Streaming in Mobile Edges: A Deep Reinforcement Learning Approach

Khan

Pervaiz

2021

Machine Learning and Intelligent Communications

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Interference-Aware Adaptive Beam Alignment for Hyper-Dense IEEE 802.11ax Internet-of-Things Networks

Cited by 6 publications

References 23 publications

Developing A New Scheduling Algorithm for Wi-Fi 6 Technology Based on Machine Learning

Developing A New Scheduling Algorithm for Wi-Fi 6 Technology Based on Machine Learning

Adaptive Real-Time Offloading Decision-Making for Mobile Edges: Deep Reinforcement Learning Framework and Simulation Results

Dynamic Resource Allocation and Streaming in Mobile Edges: A Deep Reinforcement Learning Approach

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