Energy efficient carrier aggregation for LTE-Advanced

Traditional wireless and mobile network focuses on higher throughout, coverage and larger capacity. In future, energy efficiency is of vital importance for wireless networks due to a large number of connected and battery constrained mobile devices. In Long Term Evolution Advanced (LTE-Advanced), Carrier Aggregation (CA) is proposed to increase the transmission bandwidth and hence data rate. This paper studies the effect of CA on the total power transmitted by the LTE-Advanced eNodeB based on the Orthogonal Frequency Division Multiple Access (OFDMA) downlink while taking the users Quality of Service (QoS) constraints into consideration. The numerical analysis and results show that by using CA a reduction in total power consumption can be achieved

دور تبسيط الإجراءات في تحسين رضا المواطن

يهدف هذا البحث إلى دراسة دور تبسيط الإجراءات من خلال إعادة تصميمها وتحديثها بشكل مستمر لتلبي رغبات المواطن وتواكب التطورات والتغيرات في الأنظمة والقوانين, وكذلك التركيز على وضوحها وعدم تكرارها واقتصارها على ما يضيف قيمة مضافة للمعاملة, ومعرفة مدى أهمية الآلية التي تقدم فيها هذه الخدمة للمواطن من ناحية توفير الوسائل والمعلومات التي تعطي صورة واضحة للمواطن عن آلية تقديم الخدمة والفترة الزمنية التي يحتاجها للحصول على الخدمة في الوصول إلى تحقيق رضا المواطن. تم توزيع استبانة على عينة مؤلفة من (50) مواطن من المواطنين الذين قصدوا مركز خدمة المواطن في مدينة اللاذقية للحصول على الخدمات التي يريدونها وذلك على مدى خمسة أيام بمعدل عشرة استبانات كل يوم وقد استردت  الاستبانات جميعها, تم الاعتماد على برنامج Spss الإحصائي لتحليل البيانات وقد توصل الباحث إلى أن هناك علاقة معنوية وارتباط قوي بين حداثة الإجراءات ووضوحها وقصرها وبساطة النماذج المستخدمة للحصول عليها وبين رضا المواطن, في حين أنه لا توجد علاقة معنوية بين توافر المعلومات والوسائل المطلوبة لتقديم تلك الخدمات وبين رضا المواطن. وعليه فقد أوصى الباحث بضرورة تحديث إجراءات سير معاملات المواطنين وتقليل عددها قدر الإمكان,  وإعداد أدلة توضح إجراءات سير كل معاملة بشكل واضح ودقيق,  وتبسيط النماذج المعتمدة للحصول على الخدمة ,  وإلغاء الموافقات الخارجية للحصول على الخدمة بما يقلل من الجهد والتكلفة التي يتحملها المواطن ويحسن من رضاه.

Enhancing Transfer Learning Reliability via Block-wise Fine-tuning

Fine-tuning can be used to tackle domain specific tasks by transferring knowledge learned from pre-trained models. However, previous studies on fine-tuning focused on adapting only the weights of a task-specific classifier or re-optimising all layers of the pre-trained model using the new task data. The first type of method cannot mitigate the mismatch between a pre-trained model and the new task data, and the second type of method easily causes over-fitting when processing tasks with limited data. To explore the effectiveness of fine-tuning, we propose a novel block-wise optimisation mechanism, which adapts the weights of a group of layers of a pre-trained model. This work presents a theoretical framework and empirical evaluation of block-wise fine-tuning to find a reliable transfer learning strategy. The proposed approach is evaluated on two datasets, Oxford Flowers and Caltech 101, using 15 commonly used state-of-the-art pre-trained base models. Results indicate that the proposed strategy consistently outperforms the baselines in terms of classification accuracy, although the specific block leading to optimal performance may vary across models. The investigation reveals that selecting a block from the fourth quarter of a base model generally yields improved performance compared to the baselines. Overall, the block-wise approach consistently outperforms the baselines and exhibits higher accuracy and reliability. This study provides valuable insights into the selection of salient blocks and highlights the effectiveness of block-wise fine-tuning in achieving improved classification accuracy in various models and datasets

Plagiarism in AI empowered world

The use of Artificial Intelligence (AI) has revolutionized many aspects of education and research, but it has also introduced new challenges, including the problem of students using AI to create assignments that cannot be detected by plagiarism checkers. The proliferation of AI tools that can generate original sounding text has made it easier for students to pass off the work of others as their own, making it more difficult for educators to identify and prevent plagiarism. This paper identifies the problem of plagiarism in the AI empowered world and considers potential solutions for addressing this issue

How to Measure the Average and Peak Age of Information in Real Networks?

The Age of information (AoI) was proposed in the literature to quantify the freshness of information. The majority of the work done in this area has theoretically evaluated AoI and its Peak (PAoI). In this paper, a method for obtaining the value of AoI and PAoI from experiments is proposed. We conducted an experiment emulating an M/M/1 queue and used the proposed method to evaluate AoI and PAoI. The values were compared to the expressions presented previously in the literature. Our results show that the proposed method is accurate for the M/M/1 queue. A statistical test was conducted to confirm the reliability of this conclusion

Solar Irradiance Forecasting Using a Data-Driven Algorithm and Contextual Optimisation

Solar forecasting plays a key part in the renewable energy transition. Major challenges, related to load balancing and grid stability, emerge when a high percentage of energy is provided by renewables. These can be tackled by new energy management strategies guided by power forecasts. This paper presents a data-driven and contextual optimisation forecasting (DCF) algorithm for solar irradiance that was comprehensively validated using short- and long-term predictions, in three US cities: Denver, Boston, and Seattle. Moreover, step-by-step implementation guidelines to follow and reproduce the results were proposed. Initially, a comparative study of two machine learning (ML) algorithms, the support vector machine (SVM) and Facebook Prophet (FBP) for solar prediction was conducted. The short-term SVM outperformed the FBP model for the 1- and 2- hour prediction, achieving a coefficient of determination (R2) of 91.2% in Boston. However, FBP displayed sustained performance for increasing the forecast horizon and yielded better results for 3-hour and long-term forecasts. The algorithms were optimised by further contextual model adjustments which resulted in substantially improved performance. Thus, DCF utilised SVM for short-term and FBP for long-term predictions and optimised their performance using contextual information. DCF achieved consistent performance for the three cities and for long- and short-term predictions, with an average R2 of 85%

Modelling IoT devices communication employing representative operation modes to reveal traffic generation characteristics

Several traffic models for the Internet of Things (IoT) have been proposed in the literature. However, they can be considered as heuristic models since they only reflect the stochastic characteristic of the generated traffic. In this paper, we propose a model to represent the communication of IoT devices. The model was used to obtain the traffic generated by the devices. Therefore, the proposed model is able to capture a wider understanding of device behaviour than existing, state-of-the-art traffic models. The proposed model illustrates the behaviour of Machine-to-Machine uplink communication in a network with multiple-access limited information capacity shared channels. In this paper, we analysed the number of transmitted packets using the traffic model extracted from our proposed communication model and compared it with the state-of-the-art traffic models using simulations. The simulation results show that the proposed model has significantly higher accuracy in estimating the number of transmitted packets compared with the current models in the literature

Deriving Machine to Machine (M2M) Traffic Model from Communication Model

© 2018 IEEE. The typical traffic models proposed in literature can be considered as heuristic models since they only reflect the stochastic characteristic of the generated traffic. In this paper, we propose a model for M2M communications that generates the traffic. Therefore, the proposed model is able to capture a wider picture than the state-of-the-art traffic models. The proposed model illustrates the behaviour of M2M uplink communication in a network with multiple-access limited information capacity shared channels. In this paper, we analyzed the number of transmitted packets using the traffic model extracted from our proposed communication model and compared it with the state-of- the-art traffic models using simulations. The simulation results show that the proposed model has a significantly higher accuracy in estimating the number of transmitted packets compared with the liteature model

Generalized proportional fair (GPF) scheduler for LTE-A

The growth of wireless traffic and the demand for higher data rates motivated researchers around the world to enhance the Long Term Evolution-Advanced (LTE-A) performance. Recently, a considerable amount of the research had been done to optimise the packet schedulers. The packet schedulers distribute the radio resources among users to increase spectrum efficiency and network performance. In this paper, a Generalized Proportional Fair (GPF) scheduler is used to enhance the scheduler performance compared to the other conventional schedulers. The GPF scheduler performance is compared in terms of users’ throughput, energy efficiency, spectral efficiency and fairness using system level simulations. The simulation results show that the proposed scheduler outperforms the conventional schedulers proposed for LTE-A

Optimising Electrical Power Supply Sustainability Using a Grid-Connected Hybrid Renewable Energy System—An NHS Hospital Case Study

This study focuses on improving the sustainability of electrical supply in the healthcare system in the UK, to contribute to current efforts made towards the 2050 net-zero carbon target. As a case study, we propose a grid-connected hybrid renewable energy system (HRES) for a hospital in the south-east of England. Electrical consumption data were gathered from five wards in the hospital for a period of one year. PV-battery-grid system architecture was selected to ensure practical execution through the installation of PV arrays on the roof of the facility. Selection of the optimal system was conducted through a novel methodology combining multi-objective optimisation and data forecasting. The optimisation was conducted using a genetic algorithm with two objectives (1) minimisation of the levelised cost of energy and (2) CO2 emissions. Advanced data forecasting was used to forecast grid emissions and other cost parameters at two year intervals (2023 and 2025). Several optimisation simulations were carried out using the actual and forecasted parameters to improve decision making. The results show that incorporating forecasted parameters into the optimisation allows to identify the subset of optimal solutions that will become sub-optimal in the future and, therefore, should be avoided. Finally, a framework for choosing the most suitable subset of optimal solutions was presented