Increase throughput by expectation channel quality indicator

Al-Zayadi, Haider; Lavriv, Orest; Klymash, Mykhailo; Mushtaq, Al-Shuraifi

doi:10.1109/infocommst.2014.6992322

Cited by 12 publications

(4 citation statements)

References 1 publication

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“…Channel Quality Indicator (CQI) Indicates the quality of the carriers and reports sent from the UE to eNodeB. The LTE contains 15 different CQI values ranging from 1 to 15, and depending on the reports network transmit data with different transport block size [39], [41].…”

Section: ) Physical Resource Blockmentioning

confidence: 99%

“…Step up Solution 1: Adjustment of CQI Switch: Downlink CQI adjustment, interactively compensates for inaccurate CQIs reported by UEs, optimizes MCS (Modulation and Coding Scheme) selection, and increases throughput [41]. If the network has moderate or heavy loads in the downlink, the downlink user-perceived rate will increase by 1% to 3% after CQI adjustment Fig.…”

Section: A Proposed Algorithm For Qos Parameter Estimationmentioning

confidence: 99%

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Deep Learning Based Fusion Model for Multivariate LTE Traffic Forecasting and Optimized Radio Parameter Estimation

et al. 2023

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With the evaluation of cellular network internet data traffic, forecasting and understanding traffic patterns become the critical objectives for managing the network-designed Quality of Service (QoS) benchmark. For this purpose, cellular network planners often use different methodologies for predicting data traffic. However, traditional traffic forecasting approaches are erroneous. As well as most of the time, traditional traffic forecasts are high-level or a generously large regional cluster level. Also, eNodeB-level utilization with concerning traffic forecasting is not readily available. As a result, user experience degradation or unnecessary network expansion is triggered based on the traditional method. This research deals with extensive 6.2 million real network time series Long-Term Evolution (LTE) data traffic and other associate parameters, including eNodeB-wise Physical Resource Block (PRB) utilization, which focuses on building a traffic forecasting model with the help of multivariate feature inputs and deep learning algorithms. A stateof-the-art Deep Learning algorithm-based fusion model is proposed. The combination of different deep learning algorithms, namely Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM) and Gated Recurrent Unit (GRU), enables traffic forecasting at a granular eNodeB-level and also provides eNodeB-wise forecasted PRB utilization. In this research R 2 score value for the proposed fusion model is 0.8034, which outperforms traditional models. Apart from the PRB utilization, QoS threshold was devised as 70% from a real network experience to trigger soft parameter tuning decisions. Based on the forecasted PRB utilization, this research proposed a unique algorithm that estimates eNodeB-level soft capacity parameter optimization for a short-term step-up solution or long-term network expansion to ensure a guaranteed QoS benchmark.

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Section: ) Physical Resource Blockmentioning

confidence: 99%

Section: A Proposed Algorithm For Qos Parameter Estimationmentioning

confidence: 99%

Deep Learning Based Fusion Model for Multivariate LTE Traffic Forecasting and Optimized Radio Parameter Estimation

et al. 2023

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“…Although the cited paper aims to improve 5g radio signals, it lacks bandwidth data when changing the trajectory of the UAV receiver/ transmitter. In [2,3], the model implemented and processed self-driving traffic using the Pareto distribution function and took into consideration the time used in traffic management using M/M/1 and the Weibull distribution. The disadvantage in [2,3] is the lack of information about the response of the channel quality indicator (CQI) in the frequency range exceeding the throughput intended for the equipment of the transmitter-receiver channel.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…In [2,3], the model implemented and processed self-driving traffic using the Pareto distribution function and took into consideration the time used in traffic management using M/M/1 and the Weibull distribution. The disadvantage in [2,3] is the lack of information about the response of the channel quality indicator (CQI) in the frequency range exceeding the throughput intended for the equipment of the transmitter-receiver channel. Industrial traffic requires microsecond latency for IoT communication [4].…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Devising a traffic control method for unmanned aerial vehicles with the use of gNB-IOT in 5G

Qasim

Abu-Alshaeer

Jawad

et al. 2022

EEJET

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UAVs or drones as an alternative solution to providing high-quality Internet service in difficult terrain are environmentally friendly and do not consume electricity during the day as is the case with communication towers. But the developers of the network face difficulties in the drone communication system associated with the need to take into consideration unpredictable weather conditions and terrain, as well as the short life of the drone's batteries. Therefore, the object of this study is the process of managing UAV traffic through the use of gNB-IoT in 5G. The possibility of using a mobile UAV repeater during traffic management using radio resources (RR), radio access network (RAN), the infrastructure with broadcasting tools and dynamic connection using MU-MIMO modulation is shown. The use of these tools makes it possible to connect the drone to the wired base network from the provider and then restore the radio frequency signal and broadcast to another coverage area where this subscriber does not have network coverage, use the channel quality indicator (CQI) representation as a QoE function. Undoubtedly, traffic management is the process of obtaining information about traffic control from one endpoint to another, which confirms the reliability and management of data transmission. Meanwhile, drone traffic management can be used to reduce time delays and remove network interference by relying on Internet of Things programs that use NB-5G technology. The UAV's traffic management improvement process uses a proposed algorithm to generate dynamic flow data management to enhance traffic processing of flow control in the IoT

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The future of an intelligent and responsive architecture design after the COVID 19 epidemic

Aalhashem,

Al-Kaissi,

Khaled

et al. 2023

AIP Conference Proceedings

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An intelligent building is much more than a set of walls; it's a dynamic organism that employs integrated technologies to share data about the building among various systems to enhance efficiency and provide a better experience for its users. There are a number of challenges with intelligent buildings. They must respond to people's requirements and needs, including their health and well-being; they must be resource-efficient, and they must include the most useful aspects of new technologies. The current COVID-19 outbreak has pushed architects to consider the future of architectural design technologies. Is it possible that the epidemic may influence the design of our buildings, causing them to become smarter or more intelligent? What role did architecture play throughout the epidemic and in the post-pandemic stage, as well? This paper aims to discuss the future of intelligent design technologies in light of the current Coronavirus epidemic and how it might reshape our architecture design. Finally, the impact of COVID-19 on people's daily routines in the building design will be discussed.

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Increase throughput by expectation channel quality indicator

Cited by 12 publications

References 1 publication

Deep Learning Based Fusion Model for Multivariate LTE Traffic Forecasting and Optimized Radio Parameter Estimation

Deep Learning Based Fusion Model for Multivariate LTE Traffic Forecasting and Optimized Radio Parameter Estimation

Devising a traffic control method for unmanned aerial vehicles with the use of gNB-IOT in 5G

The future of an intelligent and responsive architecture design after the COVID 19 epidemic

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