http://repository.iiitd.edu.in/xmlui/handle/123456789/1795 Year-2026 2026-06-17T18:01:19Z http://repository.iiitd.edu.in/xmlui/handle/123456789/1986 Design, modeling, and optimization of hybrid LiFi-WiFi networks for high-performance indoor wireless communication Paramita, Saswati; Bohara, Vivek Ashok (Advisor); Srivastava, Anand (Advisor) The exponential growth of bandwidth-intensive applications such as ultra-high definition (UHD) video streaming, cloud computing, augmented and virtual reality (AR/VR), and the Internet of Things (IOT) has pushed existing wireless communication technologies to their limits. Conventional RF-based networks, such as Wi-Fi and LTE, are increasingly strained by spectrum scarcity and escalating user demands. This has spurred interest in alternative or complementary communication paradigms capable of delivering higher data rates, lower latency, improved security, and better energy efficiency. Light Fidelity (Li-Fi), operating within the visible light spectrum, has emerged as a promising candidate for next-generation indoor wireless systems. Leveraging existing lighting infrastructure, Li-Fi offers vast unlicensed bandwidth, inherent security through confined coverage, and high potential data rates. However, despite these advantages, Li-Fi faces significant deployment challenges—most notably limited coverage, susceptibility to line-of-sight (LOS) blockages, sensitivity to device orientation, and degraded performance in high mobility scenarios. Furthermore, existing MAC-Iayer protocols in Li-Fi often borrow from Wi-Fi standards, such as CSMA/CA, which are not fully optimized for the unique characteristics of visible light communication (VLC) networks. The initial stage of this research addressed MAC-Iayer inefficiencies in heterogeneous Li-Fi environments. A hybrid CSMA/CA—HCCA uplink MAC protocol was developed to dynamically switch between contention-based and contention-free modes depending on device types and network load, significantly improving throughput, delay, and collision probability in diverse traffic scenarios [1]. While this approach enhanced medium access efficiency, it did not fully address performance degradation caused by user mobility and dynamic channel conditions. To tackle mobility-induced challenges, an Orientation-Aware Multi-AP Li-Fi Network (OAM-LiFiNet) was proposed [2]. This framework leveraged real-time SINR measurements and channel metrics to dynamically adjust device orientation, thereby mitigating interference and improving throughput under user movement. Although effective, Li-Fi's dependence on LOS links meant that the system remained vulnerable to blockage events caused by static obstacles or transient movement within the environment. Recognizing the importance of blockage modeling, the research introduced FixOM and SAM [3], two novel approaches for quantifying the impact of obstacles in Li-Fi environments. FixOM modeled stationary obstructions using geometric analysis, while SAM incorporated both complete and partial shadowing effects for a more realistic performance representation. These models provided valuable insights for Li-Fi deployment planning but also highlighted that blockage mitigation within a pure Li-Fi framework could not entirely eliminate service interruptions. This realization motivated the transition toward hybrid Li-Fi/Wi-Fi networks (HLWNs), which combine Li-Fi's high-speed links with Wi-Fi's broader coverage and robustness. Prototype testbeds were developed [4, 5] to evaluate hybrid systems in realistic indoor scenarios, demonstrating superior throughput, handover performance, and service continuity compared to standalone technologies. While hybrid networks improved coverage and throughput, modern MU-MIMO Wi-Fi still suffered from one significant drawback—the high overhead of channel state information (CSI) feedback. This feedback is essential for spatial multiplexing but consumes substantial wireless resources, reducing spectral efficiency and limiting achievable throughput, especially in dense multi-user scenarios. In this thesis, this disadvantage is addressed by utilizing LiFi links to carry CSI feedback through the proposed WiLiConnect and WiLiConnect-Opt systems [6, 7]. By offloading CSI transmission to Li-Fi access points, Wi-Fi capacity is freed for user data, drastically reducing overhead and substantially improving sum-rate performance in hybrid deployments. Building on this foundation, the research advanced to link aggregation and mobilityaware resource allocation strategies for HLWNs. The final stage of the research focused on advanced link aggregation algorithms—LA-SINR, LA-EQ0S, and FLADA [8, maximized combined Li-Fi/Wi-Fi throughput while meeting QoS and fairness requirements. These were complemented by a mobility-aware handover optimization strategy [10] that used a two-stage approach: offline linear programming for static users and a search-space pruning mechanism with re-optimization for mobile users near AP borders. Analytical modeling of outage probabilities [11] in LA-enabled HLWNs further validated their superior robustness compared to standalone networks. Through these interconnected contributions, this thesis presents a holistic, multi-layered framework for high-capacity, mobility-friendly indoor networks. By starting from MAC-Iayer improvements in standalone Li-Fi, addressing mobility and blockage, overcoming Wi-Fi's CSI feedback bottleneck via Li-Fi, integrating hybrid Li-Fi/WiFi architectures, and culminating in advanced link aggregation and handover optimization, the research provides a comprehensive roadmap for deploying next-generation indoor wireless systems that meet the evolving demands of smart homes, offices, and industrial environments. 2026-06-01T00:00:00Z http://repository.iiitd.edu.in/xmlui/handle/123456789/1825 Inverse synthetic aperture radar imaging of automotive targets Pandey, Neeraj; Ram, Shobha Sundar (Advisor) Advanced driver assistance systems (ADAS) aim to improve road safety, reduce fatalities, and enable autonomous driving. Modern vehicles rely on multiple sensors such as lidar, cameras, thermal detectors, and radar to detect and classify road users. Among these, millimeter-wave automotive radars offer robust range and velocity estimation, all-weather operation, and unobtrusive bumper integration. High-resolution two-dimensional radar images, and in particular inverse synthetic aperture radar (ISAR) images, can provide detailed information on target size, shape, and motion. However, existing ISAR studies of ground vehicles at automotive radar frequencies have produced only limited datasets for a few targets under controlled conditions, and these datasets are not publicly available. As a result, there is a lack of realistic, large-scale ISAR data suitable for modern machine learning (ML) algorithms for automotive applications. This thesis develops a framework for simulating high-fidelity ISAR images of automotive targets at millimeter-wave (mm-wave) frequencies. The simulation model incorporates vehicle kinematics, radar scattering phenomenology, range–Doppler clutter, and receiver noise for a 77 GHz automotive radar. Static and dynamic mm-wave clutter for automotive scenarios is modelled using measurements acquired on Indian roads, taking into account different surface types and roughness conditions. The resulting clutter statistics are used to parameterise phenomenological clutter models in the simulator. The framework is validated qualitatively and quantitatively against measurement data gathered from real automotive radars. The simulated ISAR images are then used as inputs to traditional ML classifiers and deep neural networks for the classification of automotive targets; the results show that ISAR radar images are excellent features for accurately classifying different road vehicles. The thesis further investigates the reliability and interpretability of these classification decisions. It is shown that misclassifications can occur even when noise and clutter are relatively low. To analyse such cases, a method based on counterfactual explanations is proposed, using generative adversarial networks (GANs) to perturb ISAR images of a query class until they are classified as a distractor class, while enforcing that the perturbations remain realistic and consistent with the original class distribution. The resulting counterfactual images provide physics-based insight into which target regions and micro-Doppler features drive the classifier’s decisions. Finally, two application-oriented studies using ISAR images are presented: (i) an automated parking test framework in which an externally mounted radar generates high-resolution images of a car parking in a designated slot and a polynomial trajectory fit is used to assess parking performance; and (ii) an around-the-corner radar (ACR) proof-of-concept at 77 GHz for non-line-of-sight collision avoidance, where sparsity-based dictionary learning is used to separate overlapping range–Doppler returns and reconstruct the signatures of multiple dynamic targets in an urban NLOS configuration 2026-02-01T00:00:00Z