arXiv subjects

Ahmad M. Nazar

Publications and source records attributed to Ahmad M. Nazar.

5 recordsLinked to original sources

arXiv2026

Enwar 3.0: An Agentic Multi-Modal LLM Orchestrator for Situation-Aware Beamforming, Blockage Prediction, and Handover Management

Maintaining robust millimeter-wave (mmWave) connectivity in vehicular networks requires real-time adaptation to environmental dynamics, sensor degradation, and link variability. This paper presents Enwar 3.0, an environment-aware reasoning framework that unifies multi-modal sensing, agentic large language models (LLMs), and context-driven model selection for predictive beamforming, blockage detection, and handover management. Building upon prior iterations of Enwar, the proposed architecture integrates a classifier-driven assessment of sensor health with a primed LLM that orchestrates multiple specialized agents through structured, task-aware prompting. A novel synthetic degradation pipeline enables the training of a sensor degradation classifier that detects real-time impairments across camera, radar, LiDAR, and GPS inputs, achieving over 99% accuracy. The LLM, trained via chain-of-thought (CoT) priming and human-in-the-loop feedback, coordinates agent calls for beam selection, blockage forecasting, and environment perception while dynamically loading sensor-specific models based on environmental context. Extensive evaluations across 15 sensor combinations demonstrate that Enwar 3.0 delivers state-of-the-art performance in both predictive accuracy and interpretability, with beam selection accuracy exceeding 88%, blockage F1-scores surpassing 98%, and reasoning correctness reaching 87% on complex decision prompts. This work establishes a scalable foundation for LLM-integrated wireless systems that reason, perceive, and adapt in real-time.

cs.MA↗

arXiv2025

Multi-Modal Sensor Fusion for Proactive Blockage Prediction in mmWave Vehicular Networks

Vehicular communication systems operating in the millimeter wave (mmWave) band are highly susceptible to signal blockage from dynamic obstacles such as vehicles, pedestrians, and infrastructure. To address this challenge, we propose a proactive blockage prediction framework that utilizes multi-modal sensing, including camera, GPS, LiDAR, and radar inputs in an infrastructure-to-vehicle (I2V) setting. This approach uses modality-specific deep learning models to process each sensor stream independently and fuses their outputs using a softmax-weighted ensemble strategy based on validation performance. Our evaluations, for up to 1.5s in advance, show that the camera-only model achieves the best standalone trade-off with an F1-score of 97.1% and an inference time of 89.8ms. A camera+radar configuration further improves accuracy to 97.2% F1 at 95.7ms. Our results display the effectiveness and efficiency of multi-modal sensing for mmWave blockage prediction and provide a pathway for proactive wireless communication in dynamic environments.

cs.LG↗

arXiv2025

NextG-GPT: Leveraging GenAI for Advancing Wireless Networks and Communication Research

Artificial intelligence (AI) and wireless networking advancements have created new opportunities to enhance network efficiency and performance. In this paper, we introduce Next-Generation GPT (NextG-GPT), an innovative framework that integrates retrieval-augmented generation (RAG) and large language models (LLMs) within the wireless systems' domain. By leveraging state-of-the-art LLMs alongside a domain-specific knowledge base, NextG-GPT provides context-aware real-time support for researchers, optimizing wireless network operations. Through a comprehensive evaluation of LLMs, including Mistral-7B, Mixtral-8x7B, LLaMa3.1-8B, and LLaMa3.1-70B, we demonstrate significant improvements in answer relevance, contextual accuracy, and overall correctness. In particular, LLaMa3.1-70B achieves a correctness score of 86.2% and an answer relevancy rating of 90.6%. By incorporating diverse datasets such as ORAN-13K-Bench, TeleQnA, TSpec-LLM, and Spec5G, we improve NextG-GPT's knowledge base, generating precise and contextually aligned responses. This work establishes a new benchmark in AI-driven support for next-generation wireless network research, paving the way for future innovations in intelligent communication systems.

cs.ET↗

arXiv2024

Revolutionizing Undergraduate Learning: CourseGPT and Its Generative AI Advancements

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.

cs.CY↗

arXiv2024

ENWAR: A RAG-empowered Multi-Modal LLM Framework for Wireless Environment Perception

Large language models (LLMs) hold significant promise in advancing network management and orchestration in 6G and beyond networks. However, existing LLMs are limited in domain-specific knowledge and their ability to handle multi-modal sensory data, which is critical for real-time situational awareness in dynamic wireless environments. This paper addresses this gap by introducing ENWAR, an ENvironment-aWARe retrieval augmented generation-empowered multi-modal LLM framework. ENWAR seamlessly integrates multi-modal sensory inputs to perceive, interpret, and cognitively process complex wireless environments to provide human-interpretable situational awareness. ENWAR is evaluated on the GPS, LiDAR, and camera modality combinations of DeepSense6G dataset with state-of-the-art LLMs such as Mistral-7b/8x7b and LLaMa3.1-8/70/405b. Compared to general and often superficial environmental descriptions of these vanilla LLMs, ENWAR delivers richer spatial analysis, accurately identifies positions, analyzes obstacles, and assesses line-of-sight between vehicles. Results show that ENWAR achieves key performance indicators of up to 70% relevancy, 55% context recall, 80% correctness, and 86% faithfulness, demonstrating its efficacy in multi-modal perception and interpretation.

cs.NI↗