Search

Integrating Generative AI (GenAI) into educational contexts presents a transformative potential for enhancing learning experiences. This paper introduces CourseGPT, a generative AI tool designed to support instructors and enhance the educational experiences of undergraduate students. Built on open-source Large Language Models (LLMs) from Mistral AI, CourseGPT offers continuous instructor support and regular updates to course materials, enriching the learning environment. By utilizing course-specific content, such as slide decks and supplementary readings and references, CourseGPT provides precise, dynamically generated responses to student inquiries. Unlike generic AI models, CourseGPT allows instructors to manage and control the responses, thus extending the course scope without overwhelming details. The paper demonstrates the application of CourseGPT using the CPR E 431 - Basics of Information System Security course as a pilot. This course, with its large enrollments and diverse curriculum, serves as an ideal testbed for CourseGPT. The tool aims to enhance the learning experience, accelerate feedback processes, and streamline administrative tasks. The study evaluates CourseGPT's impact on student outcomes, focusing on correctness scores, context recall, and faithfulness of responses. Results indicate that the Mixtral-8x7b model, with a higher parameter count, outperforms smaller models, achieving an 88.0% correctness score and a 66.6% faithfulness score. Additionally, feedback from former students and teaching assistants on CourseGPT's accuracy, helpfulness, and overall performance was collected. The outcomes revealed that a significant majority found CourseGPT to be highly accurate and beneficial in addressing their queries, with many praising its ability to provide timely and relevant information., Comment: 8 pages

In the 6G era, integrating Mobile Edge Computing (MEC) and Digital Twin (DT) technologies presents a transformative approach to enhance network performance through predictive, adaptive control for energy-efficient, low-latency communication. This paper presents the EcoEdgeTwin model, an innovative framework that harnesses the synergy between MEC and DT technologies to ensure efficient network operation. We optimize the utility function within the EcoEdgeTwin model to balance enhancing users' Quality of Experience (QoE) and minimizing latency and energy consumption at edge servers. This approach ensures efficient and adaptable network operations, utilizing DT to synchronize and integrate real-time data seamlessly. Our framework achieves this by implementing robust mechanisms for task offloading, service caching, and cost-effective service migration. Additionally, it manages energy consumption related to task processing, communication, and the influence of DT predictions, all essential for optimizing latency and minimizing energy usage. Through the utility model, we also prioritize QoE, fostering a user-centric approach to network management that balances network efficiency with user satisfaction. A cornerstone of our approach is integrating the advantage actor-critic algorithm, marking a pioneering use of deep reinforcement learning for dynamic network management. This strategy addresses challenges in service mobility and network variability, ensuring optimal network performance matrices. Our extensive simulations demonstrate that compared to benchmark models lacking DT integration, EcoEdgeTwin framework significantly reduces energy usage and latency while enhancing QoE.

Massive multiple input multiple output (M-MIMO) technology plays a pivotal role in fifth-generation (5G) and beyond communication systems, offering a wide range of benefits, from increased spectral efficiency (SE) to enhanced energy efficiency and higher reliability. However, these advantages are contingent upon precise channel state information (CSI) availability at the base station (BS). Ensuring precise CSI is challenging due to the constrained size of the coherence interval and the resulting limitations on pilot sequence length. Therefore, reusing pilot sequences in adjacent cells introduces pilot contamination, hindering SE enhancement. This paper reviews recent advancements and addresses research challenges in mitigating pilot contamination and improving channel estimation, categorizing the existing research into three broader categories: pilot assignment schemes, advanced signal processing methods, and advanced channel estimation techniques. Salient representative pilot mitigation/assignment techniques are analyzed and compared in each category. Lastly, possible future research directions are discussed., Comment: Accepted At IWCMC 2024 Comm & SP Symposium

Due to significant blockage conditions in wireless networks, transmitted signals may considerably degrade before reaching the receiver. The reliability of the transmitted signals, therefore, may be critically problematic due to blockages between the communicating nodes. Thanks to the ability of Reconfigurable Intelligent Surfaces (RISs) to reflect the incident signals with different reflection angles, this may counter the blockage effect by optimally reflecting the transmit signals to receiving nodes, hence, improving the wireless network's performance. With this motivation, this paper formulates a RIS-aided wireless communication problem from a base station (BS) to a mobile user equipment (UE). The BS is equipped with an RGB camera. We use the RGB camera at the BS and the RIS panel to improve the system's performance while considering signal propagating through multiple paths and the Doppler spread for the mobile UE. First, the RGB camera is used to detect the presence of the UE with no blockage. When unsuccessful, the RIS-assisted gain takes over and is then used to detect if the UE is either "present but blocked" or "absent". The problem is determined as a ternary classification problem with the goal of maximizing the probability of UE communication blockage detection. We find the optimal solution for the probability of predicting the blockage status for a given RGB image and RIS-assisted data rate using a deep neural learning model. We employ the residual network 18-layer neural network model to find this optimal probability of blockage prediction. Extensive simulation results reveal that our proposed RIS panel-assisted model enhances the accuracy of maximization of the blockage prediction probability problem by over 38\% compared to the baseline scheme.

Recent research on reconfigurable intelligent surfaces (RIS) suggests that the RIS panel, containing passive elements, enhances channel performance for the internet of things (IoT) systems by reflecting transmitted signals to the receiving nodes. This paper investigates RIS panel assisted-wireless network to instigate minimal base station (BS) transmit power in the form of energy harvesting for batteryless IoT sensors to maximize bits transmission in the significant multi-path environment, such as urban areas. Batteryless IoT sensors harvest energy through the RIS panel from external sources, such as from nearby BS radio frequency (RF) signal in the first optimal time frame, for a given time frame. The bits transmission among IoT sensors, followed by a device-to-device (D2D) communications protocol, is maximized using harvested energy in the final optimal time frame. The bits transmission is at least equal to the number of bits sampled by the IoT sensor. We formulate a non-convex mixed-integer non-linear problem to maximize the number of communicating bits subject to energy harvesting from BS RF signals, RIS panel energy consumption, and required time. We propose a robust solution by presenting an iterative algorithm. We perform extensive simulation results based on the 3GPP Urban Micro channel model to validate our model.

The problem of class imbalance is extensive for focusing on numerous applications in the real world. In such a situation, nearly all of the examples are labeled as one class called majority class, while far fewer examples are labeled as the other class usually, the more important class is called minority. Over the last few years, several types of research have been carried out on the issue of class imbalance, including data sampling, cost-sensitive analysis, Genetic Programming based models, bagging, boosting, etc. Nevertheless, in this survey paper, we enlisted the 24 related studies in the years 2003, 2008, 2010, 2012 and 2014 to 2019, focusing on the architecture of single, hybrid, and ensemble method design to understand the current status of improving classification output in machine learning techniques to fix problems with class imbalances. This survey paper also includes a statistical analysis of the classification algorithms under various methods and several other experimental conditions, as well as datasets used in different research papers., Comment: 12 Pages, 2 Figures

It has become very essential to address the limited spectrum capacity and their efficient utilization to support the increasing number of Internet of Things devices. When it comes to medical infrastructure, it becomes very imperative for medical devices to communicate with the base station. In such situations, communication over the wireless medium must provide optimized throughput (data rate) with effectual energy usage, which will ensure precise medical feedback by the responsible staff. Taking into account, it is necessary to operate wireless communication precisely at a higher frequency with more substantial bandwidth and low latency. Cognitive Radio (CR) is traditionally a viable choice, where it identifies and utilizes the vacant spectrum, thus maximizing the primary user's capacity and achieving spectral efficiency. To ensure such outcomes, the Non-Orthogonal Multiple Access (NOMA) techniques have proven to deliver an effective solution to the increasing number of devices with unimpaired performance, especially when the communication shifts towards a higher frequency band such as the mmWave band. In this chapter, IoT based CR network in uplink communication is proposed alongside employing NOMA techniques for optimal throughput, and energy efficiency for a medical infrastructure. Numerical results show that effectual throughput and energy efficiency for a High Reliable Communication (HRC) device and Moderate Reliable Communication (MRC) device improve over 83.13% and 73.95%, respectively and their corresponding energy efficacy values show vast improvement (83.11% and 73.96% respectively). Likewise, for interference case both the throughput and the energy efficiency improve approximately over 93% for all devices.