Quality of Service Class Identifier (QCI) radio resource allocation algorithm for LTE downlink.
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ABSTRACT:
SUBMITTER: Mamman M
PROVIDER: S-EPMC6347206 | biostudies-literature | 2019
REPOSITORIES: biostudies-literature
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Project description:BackgroundWireless links are fast becoming the key communication mode. However, as compared to the wired link, their characteristics make the traffic prone to time- and location-dependent signal attenuation, noise, fading, and interference that result in time varying channel capacities and link error rate. Scheduling algorithms play an important role in wireless links to guarantee quality of service (QoS) parameters such as throughput, delay, jitter, fairness and packet loss rate. The scheduler has vital importance in current as well as future cellular communications since it assigns resource block (RB) to different users for transmission. Scheduling algorithm makes a decision based on the information of link state, number of sessions, reserved rates and status of the session queues. The information required by a scheduler implemented in the base station can easily be collected from the downlink transmission.MethodsThis paper reflects on the importance of schedulers for future wireless communications taking LTE-A networks as a case study. It compares the performance of four well-known scheduling algorithms including round robin (RR), best channel quality indicator (BCQI), proportional fair (PF), and fractional frequency reuse (FFR). The performance of these four algorithms is evaluated in terms of throughput, fairness index, spectral efficiency and overall effectiveness. System level simulations have been performed using a MATLAB based LTE-A Vienna downlink simulator.ResultsThe results show that the FFR scheduler is the best performer among the four tested algorithms. It also exhibits flexibility and adaptability for radio resource assignment.
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Project description:BackgroundThe dominant paradigm for modeling the complexities of interacting populations and food webs is a system of coupled ordinary differential equations in which the state of each species, population, or functional trophic group is represented by an aggregated numbers-density or biomass-density variable. Here, using the metaphysiological approach to model consumer-resource interactions, we formulate a two-state paradigm that represents each population or group in a food web in terms of both its quantity and quality.Methodology and principal findingsThe formulation includes an allocation function controlling the relative proportion of extracted resources to increasing quantity versus elevating quality. Since lower quality individuals senescence more rapidly than higher quality individuals, an optimal allocation proportion exists and we derive an expression for how this proportion depends on population parameters that determine the senescence rate, the per-capita mortality rate, and the effects of these rates on the dynamics of the quality variable. We demonstrate that oscillations do not arise in our model from quantity-quality interactions alone, but require consumer-resource interactions across trophic levels that can be stabilized through judicious resource allocation strategies. Analysis and simulations provide compelling arguments for the necessity of populations to evolve quality-related dynamics in the form of maternal effects, storage or other appropriate structures. They also indicate that resource allocation switching between investments in abundance versus quality provide a powerful mechanism for promoting the stability of consumer-resource interactions in seasonally forcing environments.Conclusions/significanceOur simulations show that physiological inefficiencies associated with this switching can be favored by selection due to the diminished exposure of inefficient consumers to strong oscillations associated with the well-known paradox of enrichment. Also our results demonstrate how allocation switching can explain observed growth patterns in experimental microbial cultures and discuss how our formulation can address questions that cannot be answered using the quantity-only paradigms that currently predominate.
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Project description:We investigated the effect of diet quality on resource allocation in zebra finches (Taeniopygia guttata) by providing females with a high-quality (HQ) or low-quality (LQ) diet for six weeks prior to pairing, and continuing these diets during egg laying and chick rearing. Diet treatments were then reversed and the experiment repeated. When females laid on the HQ diet, egg mass increased with laying order, but the reverse was true on the LQ diet. Females laid significantly more male eggs on the LQ diet compared with on the HQ diet. In addition, female eggs were more frequent at the end of the clutch when on the HQ diet and at the beginning of the clutch when on the LQ diet. These differences in the primary sex ratio are in line with predictions from sex allocation theory, since in this species females are more vulnerable to nutritional stress than males.
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Project description:BackgroundA variety of approaches have been used to contain escalating hospital costs. One approach is intensifying price competition. The increase in price based competition, which changes the incentives hospitals face, coupled with the fact that consumers can more easily evaluate the quality of hotel services compared with the quality of clinical care, may lead hospitals to allocate more resources into hotel rather than clinical services.MethodsTo test this hypothesis we studied hospitals in California in 1982 and 1989, comparing resource allocations prior to and following selective contracting, a period during which the focus of competition changed from quality to price. We estimated the relationship between clinical outcomes, measured as risk-adjusted-mortality rates, and resources.ResultsIn 1989, higher competition was associated with lower clinical expenditures levels compared with 1982. The trend was stronger for non-profit hospitals. Lower clinical resource use was associated with worse risk adjusted mortality outcomes.ConclusionsThis study raises concerns that cost reductions may be associated with increased mortality.
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Project description:Effective management of radio resources and service quality assurance are two of the essential aspects to furnish high-quality service in Long Term Evolution (LTE) networks. Despite the base station involving several ingenious scheduling schemes for resource allocation, the intended outcome might be influenced by the interference, especially in heterogeneous scenarios, where many kinds of small cells can be deployed under the coverage of macrocell area. To develop the network of small cells, it is essential to take into account such boundaries, in particular, mobility, interference and resources scheduling a strategy which assist getting a higher spectral efficiency in anticipate small cells. Another challenge with small cellular network deployment is further analyzing the impact of power control techniques in the uplink direction for the network performance. With that being said, this article investigates the problem of interference in LTE-advanced heterogeneous networks. The proposed scheme allows mitigation inter-cell interference through fractional self-powered control performed at each femtocell user. This study analyzes a scheme with optimum power value that provides a compromise between the served uplink signal within unwanted interference plus noise ratio to enhance spectral efficiency in terms of throughput. In particular, the maximum transmit power for user equipment in uplink direction should be reviewed for small cells as a major contributor to the interference. The simulation results showed that the proposed fractional power control approach can outperform the traditional power control employed as a full compensation mode in small cell uplinks.
| S-EPMC8216560 | biostudies-literature
Project description:A deep neural network-based artificial intelligence (AI) model was assessed for its utility in predicting vital signs of hemorrhage patients and optimizing the management of fluid resuscitation in mass casualties. With the use of a cardio-respiratory computational model to generate synthetic data of hemorrhage casualties, an application was created where a limited data stream (the initial 10 min of vital-sign monitoring) could be used to predict the outcomes of different fluid resuscitation allocations 60 min into the future. The predicted outcomes were then used to select the optimal resuscitation allocation for various simulated mass-casualty scenarios. This allowed the assessment of the potential benefits of using an allocation method based on personalized predictions of future vital signs versus a static population-based method that only uses currently available vital-sign information. The theoretical benefits of this approach included up to 46% additional casualties restored to healthy vital signs and a 119% increase in fluid-utilization efficiency. Although the study is not immune from limitations associated with synthetic data under specific assumptions, the work demonstrated the potential for incorporating neural network-based AI technologies in hemorrhage detection and treatment. The simulated injury and treatment scenarios used delineated possible benefits and opportunities available for using AI in pre-hospital trauma care. The greatest benefit of this technology lies in its ability to provide personalized interventions that optimize clinical outcomes under resource-limited conditions, such as in civilian or military mass-casualty events, involving moderate and severe hemorrhage.
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Project description:IntroductionIn any health system, choices must be made about the allocation of resources (budget), which are often scarce. Economics has defined frameworks to aid resource allocation, and program budgeting marginal analysis (PBMA) is one such framework. In principle, patient and public values can be incorporated into these frameworks, using techniques such as willingness to pay (WTP). However, this has not been done before, and few formal resource allocation processes have been undertaken in dentistry. This study aimed to undertake a PBMA with embedded WTP values in a national dental setting.MethodsThe PBMA process was undertaken by a panel of participant-researchers representing commissioners, dentists, dental public health staff, and academics. The panel reviewed current allocations and generated a set of weighted criteria to evaluate services against. Services to be considered for removal and investment were determined by the panel and wider discussion and then scored against the criteria. Values from a nationally representative WTP survey of the public contributed to the scores for interventions. Final decisions on removal and investment were taken after panel discussion using individual anonymous electronic voting.ResultsThe PBMA process resulted in recommendations to invest in new program components to improve access to general dentists, care home dentistry, and extra support for dental public health input into local government decisions. Disinvestments were recommended in orthodontics and to remove routine scaling and polishing of teeth.DiscussionThe PBMA process was successful in raising awareness of resource allocation issues. Implementation of findings will depend on the ability of decision makers to find ways of operationalizing the decisions. The process illustrates practical aspects of the process that future dental PBMAs could learn from.Knowledge transfer statementThis study illustrates a framework for resource allocation in dental health services and will aid decision makers in implementing their own resource allocation systems.
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Project description:The time, quality, and cost are three important but contradictive objectives in a building construction project. It is a tough challenge for project managers to optimize them since they are different parameters. This paper presents a time-cost-quality optimization model that enables managers to optimize multiobjectives. The model is from the project breakdown structure method where task resources in a construction project are divided into a series of activities and further into construction labors, materials, equipment, and administration. The resources utilized in a construction activity would eventually determine its construction time, cost, and quality, and a complex time-cost-quality trade-off model is finally generated based on correlations between construction activities. A genetic algorithm tool is applied in the model to solve the comprehensive nonlinear time-cost-quality problems. Building of a three-storey house is an example to illustrate the implementation of the model, demonstrate its advantages in optimizing trade-off of construction time, cost, and quality, and help make a winning decision in construction practices. The computational time-cost-quality curves in visual graphics from the case study prove traditional cost-time assumptions reasonable and also prove this time-cost-quality trade-off model sophisticated.
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