http://repository.iiitd.edu.in/xmlui/handle/123456789/502 Wed, 14 Oct 2026 07:24:30 GMT 2026-10-14T07:24:30Z http://repository.iiitd.edu.in/xmlui/handle/123456789/2173 Sunlight to signals: solar panel as an optical wireless communication receiver for sustainable communication systems Rahul; Bohara, Vivek Ashok (Advisor); Srivastava, Anand (Advisor) The increasing adoption of intelligent transportation systems, smart cities, and energy-efficient wireless networks has further fueled the need for communication technologies that are not only high-speed and reliable but also sustainable and economical. Optical wireless communication (OWC) has been identified as a promising addition to existing radio frequency technologies because of its vast unregulated bandwidth, security features, and resistance to electromagnetic interference. However, the use of traditional optical receivers like photodiodes and avalanche photodiodes is accompanied by the need for separate power sources and is also prone to performance degradation due to high ambient light intensities, smaller receiver area, and the need for a complex optical design at the receiver to collect more energy, therefore increasing the system cost. In recent years, solar panels have gained attention as promising alternatives to conventional optical receivers because they can harvest energy and receive data at the same time. Although photovoltaic devices are primarily designed for power generation, their large sensing area, tolerance to strong ambient light, and widespread availability make them appealing for use in sustainable optical wireless communication systems. However, the inherent electrical properties of commercial silicon solar panels—such as high junction capacitance, which results in limited bandwidth—create major obstacles for achieving high data rates. In addition, much of the existing research has been limited to custom-built photovoltaic devices, controlled laboratory environments, or short-range indoor setups. As a result, the potential of commercially available solar panels for practical, real-world communication applications remains an unexplored research area. This thesis explores the feasibility, design, and optimization of solar panels as optical receivers, with a main emphasis on the use of commercial silicon solar panels and light emitting diode (LED) transmitters for reliable communication. This thesis systematically examines the communication performance of solar panel receivers, derives the bandwidth and sensitivity limits, and introduces new analog front-end designs to improve optical receiver communication performance. The first part of this thesis presents an in-depth experimental study of silicon solar pan els used as communication receivers under various optical and electrical conditions. The performance of solar panel receivers in terms of frequency response, time-domain response, received optical power sensitivity, and communication distance is examined. The effects of transmission distance, optical irradiance, modulation frequency, and ambient light on system performance are comprehensively explored. These experiments show that, although solar panels have very limited bandwidth, their performance can be greatly improved by proper analog signal processing. To overcome these challenges, this thesis develops and experimentally validates new analog front-end receiver designs specifically optimized for commercial solar panels. The proposed architectures employ carefully engineered impedance matching, filtering, and amplification techniques to extend the usable communication bandwidth beyond the photovoltaic device’s inherent electrical limitations. In contrast to earlier approaches based on reverse-biased operation or custom-fabricated receivers, the proposed solutions remain fully compatible with energy harvesting and are built entirely from off-the-shelf components, making them more practical for real-world deployment. Experimental evaluations show significant gains in both bandwidth and achievable data rates, enabling dependable optical communication with standard indoor LED sources under realistic operating conditions. The thesis also investigates simultaneous light information and power transfer (SLIPT) using solar panel–based receivers, with particular attention to the trade-off between harvested energy and communication quality. By examining multiple operating points and receiver configurations, this work offers valuable insight into joint optimization strategies that balance power collection with data throughput. These findings support the development of self-powered or energy-assisted optical wireless systems aimed at low-power and sustainable communication applications. In addition to the indoor environment, this thesis also encompasses the uncharted area of optical wireless communication in vehicular systems with solar-panel receivers. The vehicular system poses distinct challenges, including mobility issues, misalignment, limited orientation, and dead zones. The experimental work of this thesis explores these challenges and validates the use of solar panel receivers in vehicular communication systems, including vehicle-to vehicle and vehicle-to-infrastructure communication. The discussion emphasizes the significant shortcomings of current receiver designs and the need for orientation-resilient, robust receivers in dynamic environments. Overall, this thesis makes the following key contributions: (i) a comprehensive experimen tal characterization of commercial silicon solar panels as optical communication receivers, (ii) the design and validation of novel analog front-end circuits that significantly enhance communication bandwidth using off-the-shelf components, (iii) an in-depth investigation of SLIPT trade-offs in solar panel–based receivers, and (iv) an early exploration of vehicular optical wireless communication using solar panels, identifying open challenges and future research directions. By bridging the gap between laboratory demonstrations and real-world deployment, this work establishes solar panels as viable, sustainable, and low-cost optical receivers, paving the way for their integration into future intelligent transportation systems and energy-efficient wireless networks. Tue, 01 Sep 2026 00:00:00 GMT http://repository.iiitd.edu.in/xmlui/handle/123456789/2173 2026-09-01T00:00:00Z http://repository.iiitd.edu.in/xmlui/handle/123456789/2172 Performance analysis of intelligent reflecting surface assisted underwater wireless optical communication systems Salam, Rehana; Bohara, Vivek Ashok (Advisor); Srivastava, Anand (Advisor) The rapid growth of underwater applications such as ocean exploration, environmental mon itoring, offshore industries, and maritime defense has created a strong demand for reliable and high-data-rate underwater communication systems. Among the available technologies, underwater wireless optical communication (UWOC) has emerged as a promising solution due to its large bandwidth and low latency compared to conventional acoustic and radio-frequency (RF) systems. However, the performance of UWOC is significantly affected by absorption, scattering, oceanic turbulence, beam misalignment, and frequent link blockages. In addition, maintaining reliable communication in non-line-of-sight (NLoS) conditions remains a critical challenge in practical underwater environments. To overcome these limitations, this thesis investigates intelligent reflecting surface (IRS) assisted architectures for enhancing the performance of UWOC systems. The work begins with the analysis of a hybrid RF–UWOC framework, where a programmable RF-based IRS is employed to improve the terrestrial RF link prior to underwater optical transmission. The results demonstrate that IRS-assisted systems provide improved spectral efficiency (SE) and energy efficiency (EE) compared to conventional relaying approaches. Building on this foundation, the thesis develops optical intelligent reflecting surface (OIRS) assisted UWOC systems using mechanically adjustable mirror arrays (MAs) to enable passive beam steering and efficient NLoS communication. A comprehensive analytical framework is established to evaluate key performance metrics, including SE, EE, outage probability (OP), and bit error rate (BER) under realistic underwater channel conditions. The results show that OIRS-assisted systems significantly outperform conventional techniques such as plane mirror surfaces (PMS) and total internal reflection (TIR)-based approaches. The work is further extended to multi-user UWOC scenarios, where efficient resource allocation becomes essential. To address this, intelligent mirror allocation strategies, namely distance-based mirror assignment (DMA) and equal mirror assignment (EMA) , are proposed to improve system throughput and user fairness. The performance of these strategies is analyzed in conjunction with multiple access schemes such as orthogonal multiple access (OMA), non orthogonal multiple access (NOMA), and rate-splitting multiple access (RSMA), demonstrating improved multi-user performance under varying channel conditions. Furthermore, advanced optical architectures, including optical simultaneously transmitting and reflecting IRS (OSTAR-IRS) and liquid-crystal-based OSTAR-IRS (LC-OSTAR-IRS), are introduced to enable enhanced flexibility and adaptive beam control. Optimization frameworks are developed to efficiently design transmission and reflection parameters, improving overall system performance in dynamic environments. Extensive simulation results validate the proposed analytical models and demonstrate significant improvements in reliability, SE, and EE. Overall, this thesis establishes OIRS assisted UWOC systems as effective and practical solutions for next-generation underwater communication networks, providing both theoretical insights and design guidelines for high capacity and energy-efficient underwater connectivity. Tue, 01 Sep 2026 00:00:00 GMT http://repository.iiitd.edu.in/xmlui/handle/123456789/2172 2026-09-01T00:00:00Z http://repository.iiitd.edu.in/xmlui/handle/123456789/2097 Machine learning-driven timing analysis and physical design for VLSI Beniwal, Pooja; Saurabh, Sneha (Advisor) Electronic design automation (EDA) comprises software tools for designing modern integrated circuits that includes synthesis, placement, routing, timing analysis, and verification. As designs scale to billions of transistors, challenges arise from increased complexity, tighter timing margins, higher power–performance–area (PPA) demands, and long runtimes. Traditional EDA methods in VLSI design face several challenges due to their reliance on simplified analytical models and tuned heuristics. These ap- proaches often fail to capture complex, non-linear relationships among design parame- ters, resulting in limited accuracy. They are also computationally expensive and time- consuming because they depend on iterative optimization and repeated computation. As design complexity increases, traditional methods struggle to scale efficiently and require significant manual effort and expert intervention. Additionally, their ability to explore the design space is limited, often leading to suboptimal solutions, and they are less adaptable to variations and changing design conditions. Machine learning (ML) helps overcome these limitations by providing a data-driven approach that can model complex and high-dimensional relationships more efficiently. ML enables faster predic- tions compared to iterative methods, reducing computational cost and design time. It improves scalability for large and complex designs while minimizing the need for man- ual intervention. Furthermore, ML supports more efficient exploration of the design space and adapts better to variations, resulting in improved optimization and overall design quality. In the first part of this research, standard-cell modeling is made more accurate and robust by incorporating Multi-Input Switching (MIS) effects, which are ignored under the Single-Input Switching (SIS) assumption of traditional standard-cell library charac- terization. MIS can cause significant delay variations and glitches at the output, result- ing in inaccurate timing analysis, hold-time violations, signal integrity issues, increased power consumption, and incorrect sequential behavior. A novel satisfiability(SAT)- guided, ML-driven MIS characterization framework is proposed for multi-input combi- national standard cells. First, a logical analysis of the Boolean function of a given logic gate is performed to identify input patterns that can lead to MIS-induced speed-up or glitches. MIS-relevant patterns are extracted by formulating a SAT problem such that the satisfiable instances are the relevant input combinations. For each identified pattern, data is collected through SPICE simulations to capture the impact of MIS on timing attributes and glitches. This data is then used to develop an augmented technology li- brary that accurately captures MIS effects by leveraging ML techniques. Subsequently, MIS-aware STA is performed using traditional STA tool coupled with the augmented technology library. Further, to streamline the entire process of pattern extraction, data generation, and augmented library creation, an automated tool framework named LiMo (Library Model), is developed in this work. This framework integrates a SAT solver, a SPICE simulator, dataset optimization strategies, ML training/testing infrastructure, and multiprocessing capabilities to create MIS-aware augmented timing libraries. The proposed framework is validated through experimental results on ISCAS benchmark circuits. In delay calculation of multi-input combinational cells, ignoring MIS leads to Relative Root Mean Square Error (RRMSE) exceeding 40% compared to golden SPICE results, whereas LiMo-generated libraries achieve RRMSE below 7% compared to golden SPICE. For predicting the attributes of the MIS-induced glitches, employing the augmented libraries model deliver RRMSE less than 6%. Moreover, computation using augmented libraries is 10,000 × faster than the SPICE simulation. Hence, the proposed methodology can be employed in MIS-aware timing analysis for industrial designs. Building on advances in combinational standard-cell characterization, limitations of conventional timing models of sequential elements are addressed in this work. In traditional approaches, setup time (ST), hold time (HT), and clock-to-Q (C2Q) delays are modeled independently using two-dimensional lookup tables. Such a deterministic and decoupled representation fails to capture their interdependence, thereby introduc- ing pessimism that results in artificial violations and unnecessary design iterations. To address these limitations, a novel statistical, ML-driven characterization framework is proposed, in which fixed timing constraints are replaced with a probabilistic formula- tion. Instead of explicitly modeling ST and HT, an ML-based latch prediction model is proposed to estimate the probability of correct data latching under multidimensional operating conditions, including setup/hold skews and process variations. Through this approach, complex parameter dependencies are learned, and a safe operating region is defined within an extended timing space. In addition, a second ML model is developed for the prediction of C2Q delay within this extended timing region, ensuring accu- rate delay estimation even beyond conventionally defined timing constraints. Unlike traditional characterization methods, which assume fixed timing arcs, continuous and context-aware delay modeling is enabled under near-critical conditions. A hierarchi- cal violation-waiver framework is proposed to ensure that violations are safely waived. Along with latching probability, it is verified that the extension of the timing space does not introduce issues for flip-flops that were already operating safely under traditional timing constraint. The proposed framework is validated using TAU benchmark circuits in 45 nm technology and is verified against Monte Carlo SPICE simulations. Experi- mental results demonstrate 100% precision in filtering marginal violations, with no false positives, while maintaining C2Q delay prediction errors below 2% when compared to golden SPICE values within the extended timing region. In the final phase of this research, the focus shifts toward extending the application of machine learning techniques to address inaccuracies in congestion estimation dur- ing placement. Conventional early global routing (eGR) techniques, widely used for congestion-aware placement, rely on simplified models that fail to capture fine-grained variations in routing demand caused by cell dimensions, pin density, and local layout structures. This results in a mismatch with global routing (GR), leading to subopti- mal placement decisions and degraded routing closure.To address this, an ML-driven congestion prediction and alleviation framework is proposed, leveraging the ability of ML to learn complex, high-dimensional relationships between placement features and routing demand. Instead of invoking a routing engine, the model predicts congestion di- rectly from design features-including newly proposed features that capture local struc- tural and pin-access characteristics, thereby enabling fast and accurate estimation. A placement-modification method is also introduced to alleviate predicted congestion hot spots, and its effectiveness is demonstrated through loose integration with an industrial place-and-route (PnR) tool, thereby enabling more robust and congestion-aware physi- cal design flows. The proposed technique is evaluated on ISPD 2014/2015 benchmark designs and validated against golden global routing congestion obtained from a com- mercial PnR tool. An RMSE reduction of 68% in congestion prediction is achieved by the ML model compared to early global routing estimates, and an 8-30% reduction in routing congestion is enabled through improved optimization. Sat, 01 Aug 2026 00:00:00 GMT http://repository.iiitd.edu.in/xmlui/handle/123456789/2097 2026-08-01T00:00:00Z 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