The evolution of next-generation wireless networks demands intelligent, adaptive, and explainable decision-making for robust communication in dynamic environments. This paper presentsEnwar 2.0, the first agentic large language model (LLM) framework integrating adaptive retrieval-augmented generation (RAG) and chain-of-thought (CoT) reasoning into situation-aware and explainable wireless network management.Enwar 2.0introduces two specialized agents: a transformer-fusion (TransFusion)-based beam prediction agent and an environment perception agent, both of which fuse multi-modal sensory inputs—including camera, LiDAR, radar, and GPS—from the DeepSense6G dataset. The beam prediction agent enables infrastructure-to-vehicle (I2V) target-in-the-loop beam tracking and real-time adaptation based on dynamic environmental conditions. In contrast, the environment perception agent provides situation-aware reasoning and justifications for beam decisions. Unlike its predecessor,Enwar 1.0, which relied on static knowledge bases (KBs) and text-only LLMs,Enwar 2.0is designed for CoT reasoning, leverages LLaMa3.2-3B/LLaMa3.1-8B/LLaMa3.3-70B for text-generation, the multi-modal capabilities of LLaMa 3.2, and employs LlamaIndex for fine-grained, dynamic context retrieval, eliminating retrieval ambiguities and enhancing response relevance. Numerical results show that the beam prediction agent achieves up to 90.0% Top-3 accuracy at$t+3$, effectively predicting optimal beam selections three time steps ahead. Overall,Enwar 2.0achieves state-of-the-art performance, with up to 89.7%/83.5% interpretation/perception correctness, 81.6%/80.9% faithfulness, and 89.9%/88.2% relevancy. In comparison, the baseline pretrained LLaMa3 models without adaptive RAG achieves up to 80.3%/77.3% correctness, and the baseline without RAG performs significantly worse at 67.1%/64.8%. Additionally,Enwar 2.0reduces processing time by over 100% relative to the baseline, while its adaptive RAG improves performance by up to 13.7% compared to static RAG.

We present a novel deep learning (DL) framework for indoor localization in millimeter wave (mmWave) environments using received signal strength indicator (RSSI)-based measurements from a reconfigurable intelligent surface (RIS)-aided system. We address non-line-of-sight (NLoS) conditions and orientation variability through a dual-stream architecture that combines an orientation-gated convolutional neural network (OGCNN) with statistical feature extraction. Our approach processes RSSI matrices representing signal strengths across different user equipment (UE) and RIS beam patterns, and incorporates temporal modeling through bidirectional long short-term memory (BiLSTM) networks to capture user movement dynamics. The framework is validated using real-world experimental data collected in an indoor environment with a controlled RIS setup at 28 GHz, demonstrating superior performance with mean and median localization errors of 0.25 m and 0.19 m, respectively, outperforming the median error of classical baseline approaches by 74.7% and conventional DL baselines by 89.6%. The proposed solution maintains decimeter-level accuracy across various orientations and distances using only RSSI-based measurements, making it suitable for high-precision indoor positioning applications in next-generation wireless networks.

Date of Conference: 04-07 August 2025. Conference Location: Tokyo, Japan

The domain of software design is gaining a long overdue recognition as a vital discipline within software engineering, necessitating innovative approaches to its teaching in undergraduate education. Despite the growing importance of software design, academic programs often treat it as a secondary skill, overshadowed by the strong emphasis on coding. Only a few schools offer a design degree or dedicated design courses. This disparity between industry demands and educational practices underscores the need for novel pedagogical strategies. In our innovative work, we discuss a new way of effectively teaching software design-by-analogy for undergraduates to help them rapidly acquire the essential skills needed to design complex software without getting entangled in complex code generation and management. Software design does not necessarily follow the same clear delineation/separation between modules and components naturally apparent in tangible engineering domains. We employ “Conceptual Blending” to help students map their everyday experiences onto software design concepts. The process begins with students analyzing a simple two-arm watch to identify its user interface and create a finite state automaton for its interaction design. Success rates decline as the complexity of the watches increases, underscoring the software design challenges. By comparing these exercises to software interfaces, students learn to apply design techniques such as navigation modeling and prototyping, ensuring they can create intuitive, user-friendly software.

ARA is an at-scale Platform for Advanced Wireless Research (PAWR), specifically tailored to the unique community, application, and economic context of rural regions. It features the first-of-its-kind real-world implementation of long-distance, high-capacity wireless backhaul and access systems spanning over 30 km in diameter. Leveraging both software-defined radios and programmable Commercial Off-The-Shelf (COTS) systems, ARA orchestrates the wireless resources alongside the networking and compute resources for enabling end-to-end experiments involving user equipment, base stations, edge computing, and cloud infrastructure. Such an integration facilitates the coevolution of rural-focused wireless innovation and applications, while helping to advance the frontiers of advanced Next-G wireless systems such as Open RAN. As of summer 2024, ARA is publicly accessible with 7 base stations (BSes) and over 30 user equipment (UEs). In this demo, we share advanced wireless research experiments enabled by ARA, involving MU-MIMO in TV White Space (TVWS) bands, long-range mmWave and microwave backhaul communications, and open-source 5G NR protocol stacks such as srsRAN and OpenAirInterface (OAI).

The current approach in software engineering curricula largely stresses learning programming principles, code building, and large-scale testing but often neglects the importance of applying domain-specific knowledge and original ideas in creating practical software solutions. To tackle this shortcoming, we orchestrated an experiment encouraging students to comprehend the necessity of domain expertise and initial high-level design before plunging into coding and system development. In our experiment, students were tasked with designing a software application that calculates the maximum cube volume from a given length of wood. The first phase, which saw the students mainly focus on coding, resulted in a flawed calculation as it disregarded two dimensions of the wood. The second phase of the experiment introduced a physical component: students had to physically build the cube using their software. The realization of their calculation error prompted an understanding of the importance of careful design and prototyping. Afterward, students adjusted their approach, incorporating careful drawing and calculation into their process. This led to the correct algorithms to find the cube's maximum volume and precise dimensions. Further projects involving different geometric shapes reinforced this learning. The experiment demonstrated the value of incorporating domain knowledge and user needs at the onset of the software design process, proving the effectiveness of a more physically engaged, project-based learning approach.

The Robotics Innovative Technologies and Education (RITE) Lab provides an interdisciplinary environment for designing and constructing intelligent social robots leveraging artificial intelligence (AI) and Human-Computer Interaction (HCI). This paper is divided into two parts. In the first part, we delineate five distinct generations of robotics technology identified through a comprehensive literature review. The second part showcases our lab's successful strategy in covering these five generations by offering a comprehensive multi-disciplinary theoretical and hands-on experience using nine individual modules. This is achieved by using a unique ‘black box/white box’ approach and continuous improvement since 2012, ensuring a 100% success rate, with students capable of building and programming their robots from scratch. Our program's broad scope spans imminent robotics innovations (2023-2028) and envisions long-term future developments. This paper serves as a blueprint for similar educational endeavors, encapsulating the RITE Lab's successful ten-year journey.

Team-Based Learning (TBL), a pedagogical approach that positively influences classroom attendance, still needs help with student absenteeism. Current attendance tracking tools are designed for something other than TBL environments and require manual interaction from the instructor or students, consuming valuable class time. This paper introduces a novel approach to this problem, proposing an automated attendance tool for TBL classes using fixed sensors, either a camera or a Light Detection and Ranging (LiDAR), combined with a machine learning classification algorithm. The paper delves into a comparative study of using cameras and LiDAR for this purpose, evaluating them based on privacy, accuracy, efficiency, and perceptions of students and instructors. The results indicate that while both methods successfully record attendance, the LiDAR system was more efficient and reliable. Although the camera offered a higher accuracy rate, a better customized LiDAR dataset designed for the classroom environment could enhance the machine learning algorithm's accuracy in identifying students and recording attendance. Finally, the LiDAR system was favored by both students and instructors for its ease of use, non-intrusiveness, and privacy preservation.

As the Platform for Advanced Wireless Research (PAWR) in rural broadband, the ARA wireless living lab features the deployment of first-of-its-kind wireless access and backhaul platforms in real-world agriculture and rural settings, and preliminary experiments have demonstrated very promising results, e.g., up to 3.2 Gbps wireless access throughput and more than 10 Gbps throughput across a wireless backhaul link of over 10 km. ARA is expected to be publicly released for broad community use starting in September 2023. Through this demo, we plan to share, for the first time, with the wireless research community the transformative research experiments enabled by ARA. To stimulate discussion and community participation, we will demonstrate a few example experiments ranging from MU-MIMO in TV White Space (TVWS) bands to long-range mmWave and microwave backhaul communications, as well as open-source 5G NR protocol stacks such as srsRAN and OpenAirInterface.

Due to the rapid increase of connected devices and network traffic, the data transport from end-user devices to destination (connected device, cloud, edge servers, etc) can be interrupted because of obstacles and problems. In this paper, we propose to integrate edge servers with the intelligent reflecting surface (IRS) in a vehicular edge computing (VEC) environment. The IRS is deployed in fixed places inside the city (fixed IRS-Edge Nodes) and in taxis and buses (mobile IRS-Edge Nodes), where it is used for both reflecting signals and executing the different client vehicles' tasks. We propose an Optimal IRS-Edge Selection (OIES) model to select the optimal IRS-Edge Node(s) that satisfy the client vehicles' requirements. Moreover, we propose an Efficient IRS-Edge Selection (EIES) algorithm to deal with the high number of client vehicles in dense networks. The numerical results demonstrate the efficiency and the feasibility of the proposed solution.

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.

Recently, there has been a flurry of research on the use of Reconfigurable Intelligent Surfaces (RIS) in wireless networks to create dynamic radio environments. In this paper, we investigate the use of an RIS panel to improve bi-directional communications. Assuming that the RIS will be located on the facade of a building, we propose to connect it to a solar panel that harvests energy to be used to power the RIS panel’s smart controller and reflecting elements. Therefore, we present a novel framework to optimally decide the transmit power of each user and the number of elements that will be used to reflect the signal of any two communicating pair in the system (user-user or base station-user). An optimization problem is formulated to jointly minimize a scalarized function of the energy of the communicating pair and the RIS panel and to find the optimal number of reflecting elements used by each user. Although the formulated problem is a mixed-integer nonlinear problem, the optimal solution is found by linearizing the non-linear constraints. Besides, a more efficient close to the optimal solution is found using Bender decomposition. Simulation results show that the proposed model is capable of delivering the minimum rate of each user even if line-of-sight communication is not achievable.

The ever-evolving internet of things (IoT) has led to the growth of numerous wireless sensors, communicating through the internet infrastructure. When designing a network using these sensors, one critical aspect is the longevity and self-sustainability of these devices. For extending the lifetime of these sensors, radio frequency energy harvesting (RFEH) technology has proved to be promising. In this paper, we propose CURE, a novel framework for RFEH that effectively combines the benefits of cell-free massive MIMO (CFmMIMO), unmanned aerial vehicles (UAVs), and reconfigurable intelligent surfaces (RISs) to provide seamless energy harvesting to IoT devices. We consider UAV as an access point (AP) in the CFmMIMO framework. To enhance the signal strength of the RFEH and information transfer, we leverage RISs owing to their passive reflection capability. Based on an extensive simulation, we validate our framework’s performance by comparing the max-min fairness (MMF) algorithm for the amount of harvested energy.

Data forwarding from a source to a sink node when they are not within the communication range is a challenging problem in wireless networking. With the increasing demand of wireless networks, several applications have emerged where a group of users are disconnected from their targeted destinations. Therefore, we consider in this paper a multi-Unmanned Aerial Vehicles (UAVs) system to convey collected data from isolated fields to the base station. In each field, a group of sensors or Internet of Things devices are distributed and send their data to one UAV. The UAVs collaborate in forwarding the collected data to the base station in order to maximize the minimum battery level for all UAVs by the end of the service time. Hence, a group of UAVs can meet at a waypoint along their path to the base station such that one UAV collects the data from all other UAVs and moves forward to another meeting point or the base station. All other UAVs that relayed their messages return back to their initial locations. All collected data from all fields reach to the base station within a certain maximum time to guarantee a certain quality of service. We formulate the problem as a Mixed Integer Nonlinear Program (MINLP), then we reformulated the problem as Mixed Integer Linear Program (MILP) after we linearize the mathematical model. Simulations results show the advantages of adopting the proposed model in using the UAVs' energy more efficiently.

The use of Unmanned Aerial Vehicles (UAVs) has gained interest in wireless networks for its many uses and advantages such as rapid deployment and multi-purpose functionality. This is why wide deployment of UAVs has the potential to be integrated in the upcoming 5G standard. They can be used as flying base-stations, which can be deployed in case of ground Base-Stations (GBSs) failures. Such failures can be short-term or longterm. Based on the type and duration of the failure, we propose a framework that uses drones or helikites to mitigate GBS failures. Our proposed short-term and long-term cell outage compensation framework aims to mitigate the effect of the failure of any GBS in 5G networks. Within our framework, outage compensation is done with the assistance of sky BSs (UAVs), An optimization problem is formulated to jointly minimize communication power of the UAVs and maximize the minimum rates of the Users' Equipment (UEs) affected by the failure. Also, the optimal placement of the UAVs is determined. Simulation results show that the proposed framework guarantees the minimum quality of service for each UE in addition to minimizina the UAVs' consumed energy.

Unmanned Aerial Vehicles (UAVs) enabled communications is a novel and attractive area of research in cellular communications. It provides several degrees of freedom in time, space and it can be used for multiple purposes. This is why wide deployment of UAVs has the potential to be integrated in the upcoming 5G standard. In this paper, we present a novel cell outage compensation (COC) framework to mitigate the effect of the failure of any outdoor Base Station (BS) in 5G networks. Within our framework, the outage compensation is done with the assistance of sky BSs (UAVs) and Ground BSs (GBSs). An optimization problem is formulated to jointly minimize the energy of the Drone BSs (DBSs) and GBSs involved in the healing process which accordingly will minimize the number of DBSs and determine their optimal 2D positions. In addition, the DBSs will mainly heal the users that the GBS cannot heal due to capacity issues. Simulation results show that the proposed hybrid approach outperforms the conventional COC approach. Moreover, all users receive the minimum quality of service in addition to minimizing the UAVs' consumed energy.

4G/5G Heterogeneous Networks (HetNets), which are expected to have a very dense multi-layer network structure, have emerged as a solution to satisfy the increasing demand for high data rates. These networks, similar to other networks, are subject to failures of communication components, which may occur due to many reasons. Self-Healing (SH) is the ability of the network to continue its normal operation in the presence of failures. The contribution of this paper is to introduce a novel SH approach for all network base-stations (BSs) back-hauling in a HetNet. New SH radios are proposed with enabled Cognitive Radio (CR) capabilities for utilizing the spectrum. A Software Defined Wireless Network Controller (SDWNC) is used to handle all control information between all network elements (except user equipment). This novel pre-planned reactive SH approach ensures network reliability under multiple failures. A simulation study is conducted to assess the performance of our approach through the evaluation of the Degree of Recovery (DoR) under single and multiple failures. Our approach can achieve a DoR of at least 10% using only 1 SHR and an enhanced DoR can be achieved using a greater number of SHRs.