Quality of experience‐driven resource allocation in vehicular cloud long‐term evolution networks

2020 ◽  
Vol 31 (8) ◽  
Author(s):  
Guilu Wu ◽  
Zhengquan Li ◽  
Huilin Jiang
Keyword(s):  
Resource Allocation ◽  
Quality Of Experience ◽  
Long Term Evolution ◽  
Vehicular Cloud ◽  
Term Evolution

scholarly journals Long-Term Evolution of Quality of Life and Symptoms Following Surgical Treatment for Endometriosis: Different Trajectories for Which Patients?

2020 ◽  
Vol 9 (8) ◽  
pp. 2461
Author(s):  
Aurélie Comptour ◽  
Céline Lambert ◽  
Pauline Chauvet ◽  
Claire Figuier ◽  
Anne-Sophie Gremeau ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Surgical Treatment ◽  
Short Form ◽  
Complete Information ◽  
Long Term Evolution ◽  
Epidemiological Methods ◽  
Multicenter Cohort Study ◽  
Term Evolution

Many studies have shown a global efficacy of laparoscopic surgery for patients with endometriosis in reducing painful symptoms and improving quality of life (QoL) in the short and long-term. The aim of this study was to analyze the different trajectories of long-term evolution in QoL and symptoms following surgical treatment for endometriosis, and to identify corresponding patient profiles. This prospective and multicenter cohort study concerned 962 patients who underwent laparoscopic treatment for endometriosis. QoL was evaluated using the Short Form (SF)-36 questionnaire and intensity of pain was reported using a visual analog scale prior to surgery and at 6, 12, 18, 24 and 36 months after surgery. Distinctive trajectories of pain and QoL evolution were identified using group-based trajectory modeling, an approach which gathers individuals into meaningful subgroups with statistically similar trajectories. Pelvic symptom trajectories (models of the evolution of dysmenorrhea, dyspareunia and chronic pelvic pain intensity over years) correspond to (1) patients with no pain or pain no longer after surgery, (2) patients with the biggest improvement in pain and (3) patients with continued severe pain after surgery. Our study reveals clear trajectories for the progression of symptoms and QoL after surgery that correspond to clusters of patients. This information may serve to complete information obtained from epidemiological methods currently used in selecting patients eligible for surgery.

scholarly journals COMPARATIVE ANALYSIS OF THE PERFORMANCE OF RESOURCE ALLOCATION ALGORITHMS IN LONG TERM EVOLUTION NETWORKS

2016 ◽  
Vol 36 (1) ◽  
pp. 163-171
Author(s):  
UN Nwawelu ◽  
CI Ani ◽  
MA Ahaneku
Keyword(s):  
Resource Allocation ◽  
Real Time ◽  
Long Term Evolution ◽  
Proportional Fair ◽  
Term Evolution ◽  
Allocation Algorithms ◽  
Resource Allocation Schemes ◽  
Radio Resource Scheduling ◽  
Good Number

The growth in the good number of real-time and non-real-time applications has sparked a renewed interest in exploring resource allocation schemes that can be efficient and fair to all the applications in overloaded scenarios. In this paper, the performance of six scheduling algorithms for Long Term Evolution (LTE) downlink networks were analyzed and compared. These algorithms are Proportional Fair (PF), Exponential/Proportional Fair (EXP/PF), Maximum Largest Weighted Delay First (MLWDF), Frame Level Scheduler (FLS), Exponential (EXP) rule and Logarithmic (LOG) rule.  The performances of these algorithms were evaluated using an open source simulator (LTE simulator) and compared based on network parameters which include: throughput, delay, Packet Loss Ratio (PLR), and fairness. This work aims at giving insight on the gains made on radio resource scheduling for LTE network and to x-ray the issues that require improvement in order to provide better performance to the users. The results of this work show that FLS algorithm outperforms other algorithms in terms of delay, PLR, throughput, and fairness for VoIP and video flow. It was also observed that for Best Effort (BE) flows, FLS outperforms other algorithms in terms of delay and PLR but performed least in terms of throughput and fairness. http://dx.doi.org/10.4314/njt.v36i1.21

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