http://hdl.handle.net/123456789/10356 2026-04-04T10:50:06Z http://hdl.handle.net/123456789/20902 Communication Link Modelling of Vehicle-To-Everything (V2x) Networks Muhammad Sohail Sajid, 01-244232-006 A crucial enabling technology for intelligent transportation systems is vehicle-toeverything (V2X) communication, which provides dependable wireless connectivity to support applications related to traffc effciency and safety. Because of the high vehicle mobility, heavy traffc, and stringent latency and reliability requirements, accurate performance evaluation of V2X networks is critical. While existing analytical models frequently rely on simplifed assumptions like fxed communication ranges and ideal channel conditions, which limit their practical accuracy, simulation-based approaches offer detailed insights but are computationally costly. The analytical modeling of IEEE 802.11p-based V2X communication links with realistic wireless propagation and interference effects is the main focus of this thesis. The suggested framework takes into consideration changes in signal power, interference from nearby vehicles, hidden terminal effects, and packet reception failures due to channel impairments, in contrast to traditional models that assume error free communication within a predetermined range. Packet Delivery Ratio (PDR) and the likelihood of transmission failure due to propagation errors, packet collisions, receiver busy states, and sensing errors are among the important performance metrics that the model assesses. Extensive simulations with different vehicle densities, transmission ranges, and data rates are used to validate the analytical results. The accuracy of the suggested model is confrmed by the comparison, which shows strong agreement between analytical predictions and simulation results. The fndings also demonstrate how mobility and interference signifcantly affect the reliability of V2X communication, especially in situations with heavy traffc. All things considered, this work offers an analytical framework for assessing V2X communication performance that is more practical and scalable. In addition to supporting the creation and improvement of dependable V2X systems for upcoming intelligent transportation applications, the suggested model can help researchers and system designers better understand network behavior. Supervised by Dr. Junaid Imtiaz 2026-01-01T00:00:00Z http://hdl.handle.net/123456789/20903 Ai-Driven Satellite-Assisted Route Optimization for Enhanced Mother-Daughter Drone Coordination in Efficient Parcel Delivery Faiq Afzal, 09-244241-001 The fast-paced development in artifcial intelligence (AI), satellite communication, and autonomous aerial vehicles are transforming contemporary logistics and delivery systems. Conventional drone-based delivery systems which include single drone per trip have a number of challenges, such as restricted range, high energy consumption, and limited payload capacity. To cater these challenges, this study investigates an AI-based satellite-assisted mother daughter drone coordination system for the optimize of lastmile delivery operations. In this envisioned system, a giant mother drone serves as a carrier, sending out several smaller daughter drones to effectively deliver light packages to different locations and mother drone for heavy packages. The system uses AI based hybrid algorithms involving Mixed-Integer Linear Programming (MILP) and Genetic Algorithm (GA) AI, for route optimization real-time weather data to adaptively modify flight routes to decrease travel distance, reduce delivery time, reduce energy expenditure, and enhance delivery effciency. This study utilizes simulation-based approach using Python. The important metrics include delivery time, total distance, energy consumption, scalability, and feasibility. The possible outcome of this study is the creation of an AI-based Mother daughter drone delivery system that is much more effcient, adaptable, and sustainable than traditional single-drone delivery systems. The results of this study have broad implications for e-commerce logistics, medical supply chain distribution, emergency response, and smart city infrastructure, and thus represent a pioneering contribution to the future of autonomous aerial transport Supervised by Dr. Junaid Imtiaz 2026-01-01T00:00:00Z http://hdl.handle.net/123456789/20459 Resource Management of UAVs in 6G Network Anisa Zafar, 01-244202-027 Ground-based communication infrastructure is often damaged by natural disasters, disrupting the network connectivity, making it diffcult for effcient rescue and relief operations. In such scenarios, unmanned aerial vehicles (UAVs) can serve as aerial base stations to provide emergency network services. In this study, resource management of UAVs in disaster scenarios using 6G Network is investigated by providing a keen insight into effcient channel and bandwidth allocation to maximize the data rates. The three assignment algorithms, i.e., the Hungarian, Greedy, and Random, along with an artificial intelligence-based intra-band carrier aggregation (IBCA) approach, were analyzed. The results depicted that Hungarian provides globally optimal results when it comes to channel assignment and maximum data rates over fxed bandwidth, outperforming the other two algorithms. To address dynamic and heterogeneous bandwidth demands, IBCA was employed. The suggested AI-based model estimates the additional bandwidth required by the user and dynamically allocates available contiguous bandwidth chunks to satisfy heterogeneous users’ data rate requirements. The proposed AI-based IBCA model outperforms traditional scheduling algorithms in terms of data rates, bandwidth, and channel allocation. It also emphasizes the potential of UAVs and emerging 6G technologies in enabling resilient communication systems. Supervised by Dr. Saleem Aslam 2026-01-01T00:00:00Z http://hdl.handle.net/123456789/20904 RIS-Assisted UAV-Enabled Cell-Free MIMO Systems for 6G Communications Farhan Siddiqui, 09-244241-002 The exponential surge in global mobile data traffic and the stringent requirements of emerging applications such as extended reality, holographic telepresence, and advanced industrial automation expose fundamental limitations in 5G networks, particularly in achieving ultra-reliable low-latency communication (URLLC) and massive connectivity in challenging environments. This thesis investigates the performance enhancements provided by four pivotal 6G enabling technologies: conventional massive MIMO, cell-free massive MIMO, Reconfgurable Intelligent Surfaces (RIS), and UAV-assisted communication, with a focus on their ability to deliver superior coverage probability in high signal-to-interference-plus-noise ratio (SINR) regimes essential for future wireless systems, including Industry 4.0 deployments. Despite extensive theoretical and simulation-based research demonstrating individual benefits of these technologies, a notable research gap persists in the absence of unified, comparative evaluations conducted under consistent modelling assumptions and realistic propagation conditions. To address this gap, this study employs large-scale Monte Carlo simulations (10,000 independent drops per scenario) to systematically quantify coverage probability across SINR thresholds ranging from 0 to 20 dB. The primary objectives are to: (1) model and evaluate the SINR coverage performance of each technology against its respective baseline; (2) assess implications and limitations for 6G network design. Key findings indicate that cell-free massive MIMO achieves near-perfect coverage (100 %) even at 20 dB SINR, outperforming all others due to macro-diversity and cell-edge elimination. RIS delivers substantial passive gains, pushing coverage close to 100% in obstructed scenarios. Conventional massive MIMO provides reliable and consistent array gain improvements, while UAV-assisted communication offers modest but valuable line-of-sight enhancements under favourable conditions. Overall, the technologies rank as cell-free MIMO > RIS > conventional MIMO > UAV for high-reliability applications. These results highlight the critical role of technology integration in realising robust, energy-efficient 6G networks capable of supporting extreme reliability demands in industrial settings. Supervised by Dr. Adil Ali Raja 2026-01-01T00:00:00Z