Analysis of the effects of CQI Feedback for LTE Networks on ns-3

Hurtado, Juan Guillermo Torres; Bustamante, Roberto Cervantes

doi:10.1109/tla.2015.7387927

Cited by 5 publications

(5 citation statements)

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“…Most of the research in this field is focused on network simulators and performance models that are generic, with some assumptions in the model that can limit the model's accuracy. Furthermore, they are typically focused on system and algorithm design, and rarely deals with network planning problems [12][13][14][15]. The reason for this is academics and researchers are rarely involved in industry closely enough to have access to data from real networks.…”

Section: Problem Definition and Research Methodologymentioning

confidence: 99%

Predictive Capacity Planning for Mobile Networks—ML Supported Prediction of Network Performance and User Experience Evolution

2022

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Network performance prediction is crucial for enabling agile capacity planning in mobile networks. One of the key problems is predicting evolution of spectral efficiency in growing network load conditions. The main factor driving network performance and spectral efficiency is reportedly the Channel Quality Indicator (CQI). In this paper, the performance of different Machine Learning (ML) models were examined, and XGBoost was selected as the best performing model. Furthermore, to improve modeling accuracy, several features were introduced (operating frequency band, Physical Resource Block (PRB) utilization in surrounding cells, number of surrounding cells within a radius, heavy data factor and higher order modulation usage). The impact of these features on CQI prediction were examined.

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Section: Problem Definition and Research Methodologymentioning

confidence: 99%

Predictive Capacity Planning for Mobile Networks—ML Supported Prediction of Network Performance and User Experience Evolution

2022

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“…This procedure was signi cantly affected by both LTE-A RAT (Annamalai et al, 2016) and other RAT (Dat et al, 2017). Based on the channel quality indicator (CQI), active users at the centre and edge of the network were grouped into MU teams that successfully reduced inter-small cell interference (Torres & Bustamante, 2015).…”

Section: Proposed Cooperative Radio Resources Allocation In Lte-amentioning

confidence: 99%

Novel Solution Proposal for Internet of Things (IoT) Security-Based Cooperative Radio Resources System

Hamrouni

2023

Preprint

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The deployment technologies are expected to spread more widely to support the long-term evolution of advanced (LTE-A) small-cells. Small-cell users indoors can take advantage of robust connections. This study focuses on maintaining the performance control systems to enable effective monitoring and administration of intelligent building operations since security services and access control have grown in importance in systems for smart buildings. It used a cooperative Radio Resource allocation approach for LTE-A systems. Within smart buildings, the goal is to improve Quality of Service (QoS) and reduce interference from users and small cells. This paper proposed an approach to effectively accommodating active mobile users within small cells. This study used an innovative approach for selecting and allocating physical resource blocks to enhance resource use. The efficacy of this technique was substantiated by research findings, which were based on simulation results. These results highlight the achievement of optimal system throughput while maintaining the QoS for users.

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“…[79] focused on advanced antenna and filtering solutions as the main enabler for operation at mmWave bands. The authors also emphasized that there is a potential spectrum under-used in the frequency bands below 6 GHz as 3300-4200 MHz, 4400-5000 MHz, and 700 MHz [102,103]. [70] showed that adapting LTE cellular technologies by means of formal LBT and ABS proposals (which became part of LTE standards) is feasible for improving the system QoS and coexistence management.…”

Section: Cbrs Perspectivesmentioning

confidence: 99%

Literature review of deploying small transmitters on a spectrum sharing scheme

Hurtado¹,

Ramírez

Caicedo

2021

Rev.Fac.Ing.Univ.Antioquia

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Nowadays, there are a lot of misinformation and myths related to both the deployment of antennas for 5G and the frequency bands used in such technology. Many of these myths arise from the lack of serious research documents released in common language to be understood by different knowledge areas. This work makes a small contribution in this regard and shows a bibliographic review of the most relevant characteristics related to the problem of deployment of transmitters (a.k.a. base stations), and also takes into account a spectrum sharing scheme. As a case study, we will analyze the CBRS regulation used in the United States for the deployment of 5G, it will provide an overview of engineering problems and also, the areas of research that still need to be explored.

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Analysis of the effects of CQI Feedback for LTE Networks on ns-3

Cited by 5 publications

References 0 publications

Predictive Capacity Planning for Mobile Networks—ML Supported Prediction of Network Performance and User Experience Evolution

Predictive Capacity Planning for Mobile Networks—ML Supported Prediction of Network Performance and User Experience Evolution

Novel Solution Proposal for Internet of Things (IoT) Security-Based Cooperative Radio Resources System

Literature review of deploying small transmitters on a spectrum sharing scheme

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