Department of Electrical and Electronics Engineering
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Item Design and simulation of energy efficient OLEDS for flexible electronics applications(IEEE, 2025-05) Gupta, NavneetOrganic Light Emitting Diodes (OLEDs) are a promising technology known for their thin, energy-efficient, and high-quality light emission, making them ideal for displays and lighting applications. Flexible OLEDs, an emerging development from conventional rigid OLEDs, can be integrated into curved or bendable surfaces, enabling new design possibilities in wearable electronics, foldable screens, and innovative lighting solutions. In this work, we designed and simulated a multilayer OLED structure using TCAD (Technology Computer-Aided Design) software, focusing on material selection for the emission layer to optimize energy efficiency and performance. Three candidate materials—Alq3, PPV, and PFO—were evaluated, with PFO demonstrating superior luminescent power and energy efficiency. Through geometry optimization of the PFO layer, we achieved an energy efficiency of 14.08%, highlighting its potential as a suitable alternative to Alq3 for flexible OLED applications.Item Sustainable marine surveillance sensor network aided by swipt-enabled auvs(IEEE, 2025-08) Tripathi, ShardaOceans are vital for Earth's climate stability, oxygen production, and as sources of food and energy for countless organisms. However, human-induced climate change significantly disrupts marine ecosystems, emphasizing the need for advanced underwater monitoring. The Internet of Underwater Things (IoUT), composed of marine sensor networks, offers a promising solution, nonetheless, challenges such as limited communication range and constrained power supplies. To address these issues, this work proposes using simultaneous wireless information and power transfer (SWIPT) from autonomous underwater vehicles (AUVs) to enhance sensor node efficiency. We formulate an integer linear programming (ILP) problem aimed at optimizing AUV trajectories through marine sensor networks, minimizing propulsion energy and mission duration while ensuring adequate energy harvesting at each node. The problem is proven to be NP-hard, resembling the well-known traveling salesman problem (TSP). Further, we introduce CLEAR, a multi-agent deep-Q-network (DQN) framework that effectively selects optimal path for AUV-based data muling. Experimental results demonstrate that CLEAR significantly improves network energy-efficiency, reduces mission duration, boosts harvested energy, and decreases age-of-information (AoI) of sensor data.Item Environment-Aware Green UAV-Assisted, CubeSat Communication Network Energy Efficiency and Outage Probability Analysis(IEEE, 2024-08) Bitragunta, SainathRapid advancements in internet-of-things (IoT), unmanned aerial vehicles (UAVs), and energy harvesting (EH) technologies can be leveraged to design and develop green and reliable cooperative Cube satellite communication (CSC) systems and networks. In this work, we propose a novel cooperative CSC system model comprising green UAVs as intelligent relays equipped with IoT sensors, intelligent processing and EH modules, and transceivers. Using a novel and intelligent probabilistic transmission policy (PTP) that we propose, CubeSats can conserve energy by deactivating transmissions in unfavorable weather conditions based on control signals from the smart UAV via a telemetry link. We extend this model to include multiple CubeSats and analyze it by deriving and evaluating network energy efficiency and its lower bound. Our numerical plots show that the proposed PTP significantly outperforms the continuous transmission policy (CTP). At a specific transmission probability of 0.125, PTP is 40 times more energy efficient than CTP. We extend the work and develop a novel and insightful performance analysis for energy efficiency outage (EEO) probability. Specifically, we derive closed-form approximate expressions for EEO probability and present numerical results. Furthermore, we analyze the performance of clustered CSC networks and present numerical results to assess EEO probability, providing valuable insights for future large-scale green CSC network design and deployment.Item Multi-Objective MDP-Based Routing in UAV Networks for Search-Based Operations(IEEE, 2024-05) Chalapathi, G.S.S.; Chamola, VinayUnmanned aerial vehicle (UAV) systems have gained widespread recognition due to their versatility and autonomy. Their deployment for disaster mitigation and management operations is seen as one of their most important applications over the past decade. In such UAV networks, routing plays a crucial role in determining network performance parameters such as network lifetime, data transmission latency, and packet delivery ratio. This paper presents a novel routing mechanism - Multi-Objective Markov Decision Based Routing (MOBMDP) for UAV networks carrying out search-based operations. MOBMDP models routing decisions in a Markov Decision Process (MDP) framework and uses Q-learning to take decisions. It compares routing paths using three metrics, viz., Remaining Energy of the Minimum Energy Node (REMEN), Power Distance ratio (PD), and Expected Delay (ED). Amongst these metrics, PD is a novel metric proposed by this work. PD simultaneously optimizes the energy efficiency and energy distribution in the network. Further, MOBMDP uses a novel reinforcement learning inspired method to estimate transmission delay in a given path. Intensive simulation studies compare MOBMDP to four state-of-the-art routing protocols. Results show a significant improvement in network lifetime, packet delivery ratio, energy efficiency, average data transmission delay, and error in delay estimation.Item Energy Efficient Hardware Implementation of 2-D Convolution for Convolutional Neural Network(IEEE, 2022) Gupta, AnuOver the last year, Deep neural networks (DNN) have been significantly accepted for computer vision applications because of high classification accuracy and versatility. Convolutional Neural Network (CNN) is one of the most popular architectures of DNN which is widely adopted for image, speech and video recognition. Extensive computation and large memory requirement of CNN s poses the bottleneck on its application. Field Programmable Gate Arrays (FPGAs) are considered to be suitable hardware platforms for deployment of CNNs with low power requirements. This paper focus on the design and implementation of hardware accelerator to perform the convolution product (matrix-matrix multiplication. We have used two optimization techniques to achieve energy efficiency. First, dataflow of the convolution phase is rescheduled to reduce the undesired on-chip memory accesses. Further, efficiency is enhanced by reducing the internal parallelism of structure as much as possible. Our architecture is implemented on the Xilinx ZCU104 evaluation board. The implemented design attains 98.1 GOPS/Joule and 32.77 GOPS/Joule for 8-bit and 16-bit data width respectively.Item Novel Dynamic Bandwidth and Wavelength Allocation Algorithm for Energy Efficiency in TWDM-PON(IEEE, 2019) Garg, SukritiIn this paper, we propose a novel dynamic bandwidth and wavelength allocation (DBWA) algorithm that makes use of bin-packing approach resulting in the improved energy efficiency for time and wavelength division multiplexed passive optical networks (TWDM-PONs). Simulation results show that this novel DBWA algorithm not only improves the energy efficiency of TWDM-PON but also its delay performance in comparison to the state-of-art DBWA algorithm.Item Battery lifetime estimation for energy efficient telecommunication networks in smart cities(Elsevier, 2021-08) Chamola, VinayThere has been a surge in telecommunication network deployments across the globe to facilitate advanced communication infrastructure which is necessary for smart cities. This has in turn increased the power consumption of telecommunication networks, thus motivating the need to adopt green energy solutions like solar energy to power them. Base stations (BSs) are the primary entities contributing to the power consumption in the telecommunication network. To efficiently deploy solar powered base stations, it is imperative to optimally provision them with appropriate Photo Voltaic (PV) panel and battery resources. The ultimate goal of such dimensioning is to provide best possible quality of service (QoS) to the consumers while maintaining an optimal cost of deployment and operation. Both PV panels and the batteries are major contributors while calculating the overall cost of deployment and operation for a solar powered BSs. Therefore an accurate calculation of battery lifetime with respect to different PV panel dimension and battery sizes is an important step in cost optimal resource provisioning for the solar powered BSs. This issue is addressed in this paper by presenting an analytical scheme to estimate the battery lifetime for a particular resource provisioning of PV panels and batteries. This is then used for evaluating the cost-optimal photo-voltaic panel dimensions and battery size for the base station with acceptable limit of outage probability. The proposed methodology would find great relevance in developing energy efficient sustainable telecommunication networks for upcoming smart cities.Item An Energy and Delay Aware Downlink Power Control Strategy for Solar Powered Base Stations(IEEE, 2016-05) Chamola, VinayUsing renewable resources like solar energy to power the base stations (BSs) has emerged as a promising solution for greening cellular networks. One of the key challenges in operating a network of such BSs is to intelligently manage the green energy available to the BSs while ensuring reliable quality of service (QoS). This letter presents a methodology for maximizing the QoS, in terms of the network latency, given the constraints on the energy availability at the solar-powered BSs. In contrast to existing approaches based on user association reconfiguration, our methodology uses a combination of intelligent energy allocation and BS downlink power control. Using a real BS deployment scenario from U.K., we show the efficacy of our algorithm and demonstrate its superior performance compared to existing benchmarks.Item Optimizing Fading-Averaged Bandwidth Efficiency of Underlay Cognitive Radio System by Transmit Power Adaptation(IEEE, 2019) Bitragunta, SainathPower adaptation has been widely studied in the literature, given its significance in designing power efficient adaptive wireless systems. Specifically, transmit power adaptation is an important technique that is naturally appealing for underlay cognitive radio systems, which are intelligent and reconfigurable. In this paper, we consider a secondary underlay transmitter whose transmissions are constrained by an average interference threshold, also it adapts its transmit power as a function of its local channel state information (CSI). In this paper, we derive the optimal power adaptation factor (PAF) that maximizes the fading-averaged bandwidth efficiency (FABE) and also calculate the corresponding energy efficiency. We develop insightful analysis for average spectral efficiency, that is, FABE and energy efficiency for two scenarios: i). CSI-independent PAF and ii). CSI-dependent PAF. Our numerical results reveal that FABE for transmit power adaptation with CSI-dependent PAF delivers superior performance than the PAF that does not depend on instantaneous CSI at the expense of slight decrease in energy efficiencyItem Doubly Constrained Underlay Cognitive Radio System: Optimization and Analysis(IEEE, 2019) Bitragunta, SainathIn this paper, we consider a doubly constrained underlay cognitive radio (CR) system. In it, a secondary user (SU) transmitter (Tx) is subject to average interference constraint, and spectral efficiency constraint. For the underlay system, we investigate both average energy efficiency and average spectral efficiency. Specifically, we develop an algorithm that determines the suboptimal operating point of transmit power with which the SU-Tx must operate for maximum energy efficiency. We derive an analytical expression for the fading-averaged spectral efficiency assuming Rayleigh fading with path loss and shadowing. To validate the analytical results, we simulate the model in MATLAB using Monte-Carlo simulations and investigate the system performance. The analysis that we present is useful for constrained cognitive radio systems and networks.