http://repository.iiitd.edu.in/xmlui/handle/123456789/502 Fri, 03 Jul 2026 00:12:42 GMT 2026-07-03T00:12:42Z 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. Mon, 01 Jun 2026 00:00:00 GMT http://repository.iiitd.edu.in/xmlui/handle/123456789/1986 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 Sun, 01 Feb 2026 00:00:00 GMT http://repository.iiitd.edu.in/xmlui/handle/123456789/1825 2026-02-01T00:00:00Z http://repository.iiitd.edu.in/xmlui/handle/123456789/1818 Accelerated reconstruction of HARDI data and fiber orientation estimation Vaish, Ashutosh; Gupta, Anubha (Advisor); Rajwade, Ajit (Advisor) The human brain has a complex network of nerve fibers, particularly in the white matter regions. This nerve fiber network forms the basis of connectivity within brain regions and to the other parts of the body. Traditional imaging techniques like magnetic resonance imaging (MRI) provide an accurate anatomical picture of the brain but offer limited insights into neural connectivity. This limitation is critical, as some neurological disorders can only be diagnosed by examining the alterations in nerve fiber bundles. To address this challenge, diffusion MRI (dMRI) has emerged as a powerful imaging modality capable of accurately characterizing the orientation of axonal fiber bundles. Diffusion MRI is acquired as a collection of MR volumes (referred to as q-samples). Among dMRI techniques, High Angular Resolution Diffusion Imaging (HARDI) is known to produce better fiber orientation representations than Diffusion Tensor Imaging (DTI) and has more practical computational requirements compared to Diffusion Spectrum Imaging (DSI). Despite its potential, HARDI faces challenges in acquisition and post-processing, requiring innovative solutions. For instance, to achieve the better resolution that HARDI offers over DTI, a large number of q-samples are required, which makes the scanning process slow and prone to motion artifacts. Long scanning times can also be inconvenient for patients. On the other hand, reducing the number of q-samples can reduce reconstruction accuracy. To solve this issue, compressive sensing of HARDI data in k-space and/or q-space presents a potential solution to accelerate the scanning process, where the signal is reconstructed later by exploiting the inherent regularity of the signal. There are two prominent sampling schemes in k-space: ‘Cartesian’ and ‘Radial’, with their pros and cons. The Cartesian sampling scheme is more immune to hardware-based distortions, while radial sampling patterns are more immune to motion-induced artifacts. We propose two methods to reconstruct the compressively acquired measurements from the scanner: MSR-HARDI and TL-HARDI. Both methods primarily focus on acquisition through Cartesian sampling schemes in k-space. The first method, MSR-HARDI, utilizes Multiple Sparsity Regularizers in joint (k − q)-space, allowing higher subsampling ratios that are not feasible with only k-space or only q-space subsampling. Additionally, combining regularizers has been shown to yield improved reconstructions compared to individual regularizers. Building upon this work using fixed sparsifying dictionaries, our second method, TL-HARDI, further explores the application of adaptively learned transforms for the accelerated reconstruction of HARDI. This transform is learned using compressively sensed measurements, eliminating the overhead of selecting data-specific fixed sparsifying dictionaries. Further, since the transform is learned on overlapping patches, it captures local image structure effectively, providing an additional denoising effect to the framework. We also recognize that the radial sampling patterns have less pronounced aliasing due to undersampling compared to Cartesian sampling. These advantages can be leveraged particularly in the acquisition of multidimensional signals like HARDI, with a tremendous scope of acceleration. However, despite these benefits, some hardware issues may lead to k-space samples being acquired along deviated radial trajectories, severely degrading the image reconstruction quality. To address this problem, we have proposed a method called CSR-PERT. In CSR-PERT, we investigated a realistic model of gradient delays, which leads to the measurements being acquired from unknown miscentered radial trajectories. This method proposes a joint framework where these perturbed radial trajectories are estimated and used for reconstructing images from the compressively sensed measurements of MRI and HARDI data. After addressing the acquisition aspect, we also explored another important avenue, i.e., the estimation of fiber orientations from HARDI data. Accurate estimation of the local white matter fiber orientations is required for a reliable neural connectivity analysis. In the absence of ground truth, most previous methods relied on assumptions about the physical models of the diffusion signal. This leads to overly simplistic mathematical models, such as DTI, which fail in regions with multiple fiber crossings or excessively complex models that may fail to converge in real time using conventional optimization techniques. Additionally, previous deep learning (DL) methods also attempted to estimate fiber orientations using orientation distribution functions (ODFs), with possible orientations on a predefined set of directions on the sphere, leading to unavoidable discretization errors. To solve these issues, we proposed two methods: FOREST and DL-MuTE. In FOREST, we estimate the peak fiber orientations directly from the diffusion signal using a branched multi-layer perceptron (MLP). In DL-MuTE, we utilize a multi-tensor model to analyze regions with complex tissue structures, such as crossing fibers. This method presents a DL pipeline that uses a branched architecture to estimate individual tensors of a multi-tensor model, which are then used to infer the underlying fiber orientations within an imaging voxel. The models are trained on synthetic data and evaluated on phantoms with known ground truth. The approach is validated on signals with varying noise levels. Wed, 31 Dec 2025 00:00:00 GMT http://repository.iiitd.edu.in/xmlui/handle/123456789/1818 2025-12-31T00:00:00Z http://repository.iiitd.edu.in/xmlui/handle/123456789/1791 Modelling and analysis of UAV-Assisted cellular networks R R, Neetu; Ghatak, Gourab (Advisor); Bohara, Vivek Ashok (Advisor); Srivastava, Anand (Advisor) Emerging as a transformative solution in next-generation wireless networks, unmanned-aerial vehicles (UAVs) provide unprecedented flexibility, rapid deployment, and enhanced connectivity. Their integration into conventional cellular networks presents numerous opportunities, such as dynamic coverage expansion, disaster relief and emergency response, and military and surveillance applications. However, it also brings challenges, including energy constraints, fronthaul and backhaul limitations, and mobility and handover management. This thesis explores three critical aspects of UAV-enabled networks: spectrum management in integrated access and backhaul (IAB) networks, mobility management for seamless handovers, and joint UAV activation control and power optimization in UAV enabled cell-free massive multiple input multiple output (mMIMO) networks under fronthaul capacity limitations. In the first part, we investigate spectrum management in UAV-enabled IAB networks, where the UAVs act as access points and relay data to the core network. We address the challenge of optimally allocating limited spectrum resources between access and backhaul links to maximize network efficiency. Our analysis includes disaster recovery scenarios, where optimal UAV positioning and resource partitioning are derived to sustain user connectivity and maximize throughput. Additionally, in urban environments, we introduce cache-enabled UAVs that reduce reliance on backhaul links, improving content delivery performance. Key metrics such as signal to interference noise ratio (SINR) coverage probability and successful content delivery probability are evaluated. The second focus is on mobility management in UAV-enabled networks with mobile users. Frequent handovers (HOs) due to user mobility present significant challenges to network performance. To address this, we propose a caching-based handover management scheme that reduces handover occurrences by utilizing device caching, thereby enhancing quality of service (QoS). Using spatio-temporal analysis, we assess the scheme’s effectiveness in minimizing handover frequency and ensuring seamless connectivity. Additionally, we examine network reliability by analyzing the conditional success probability (CSP) experienced by users in the presence of blockages. Furthermore, we derive the meta distribution (MD) of SINR and mean local delay (MLD), offering deeper insights into network reliability. The third aspect focuses on the joint optimization of UAV activation and power consumption in UAV-based cell-free mMIMO networks. These networks promise uniform service quality over large areas but are constrained by the limited capacity of wireless fronthaul links connecting UAVs to the central processing unit (CPU). We incorporate functional split options, specifically Options 8 and 7.2, to balance fronthaul capacity, computational complexity, and latency. By formulating and solving a joint placement and power optimization problem, we ensure efficient resource utilization while maintaining fair SINR coverage across users. This thesis provides a structured framework for integrating UAVs into cellular networks, addressing key challenges in spectrum management, mobility, and resource optimization, paving the way for more reliable and efficient UAV-based wireless communication systems. Mon, 01 Dec 2025 00:00:00 GMT http://repository.iiitd.edu.in/xmlui/handle/123456789/1791 2025-12-01T00:00:00Z