http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/1925 2026-05-11T16:10:45Z http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/20932 Design and development of wide bandgap material based semiconductor devices for high power applications Shekhawat, Rajesh Singh Under the Supervision of Prof. Dheerendra Singh and co-supervisor Sumitra Singh 2025-01-01T00:00:00Z http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/20931 Performance analysis of high voltage gain converters for renewable energy applications Sharma, Preeti Under the Supervision of Prof. Rajneesh Kumar and co-supervisor Sara Hasanpour 2025-01-01T00:00:00Z http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/19317 Stochastic diffusivity with time-varying trajectory in mobile molecular communication: performance analysis and channel modeling Joshi, Sandeep This work considers a three-dimensional mobile molecular communication (MC) with intra-body disease spread applications. The communicating devices in the considered mobile MC system are point transmitters and passive spherical receiver nano-machines (NMs) with emitted information-carrying molecules following the Gaussian Brownian motion. These NMs can be used to detect the presence of disease spread and for targeted drug delivery. We propose stochastic diffusivity models for both communicating devices and information-carrying molecules. Using the stochastic diffusivity model and considering initial distance as a reference, we derive the probability density function of the relative distance between the communicating devices. We allocate the time-varying trajectory to the information-carrying molecules moving towards receiver NM and obtain its diffusivity distribution. Through the proposed stochastic diffusivity model, we characterize the mobile MC channel by channel impulse response and derive its statistical mean. We consider the discrete-time statistical channel model at a high inter-symbol interference regime and analyze the channel performance in terms of error analysis and receiver operating characteristics. We also derive the channel for the considered system model. We show the degree of accuracy through root mean square error for the Poisson and Gaussian distribution models. Furthermore, the numerical results are verified through particle-based simulations. 2025-04-01T00:00:00Z http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/19316 Nonlinear anisotropic diffusion-based channel estimation in 5G wireless networks Joshi, Sandeep In the context of the fifth-generation new radio downlink scenario, we introduce an innovative approach for channel estimation in this paper that circumvents the requirement for the prior dataset. We incorporate anisotropic diffusion and bit-plane decomposition to remove the noise in channel estimates. We first pre-process wireless channel coefficients with bit-plane decomposition to partially reduce noise interference and maintain the granularity of the information. In the second stage, anisotropic diffusion is performed based on neighboring coefficients, and the gradient-based denoising takes place without prior training. We assess the mean square error (MSE) across varying noise levels compared to the state-of-the-art method and further explore the impact of key parameters governing anisotropic diffusion. The simulation results indicate that the proposed channel estimation technique achieves a 44.77% reduction in average MSE and a significant reduction in computational complexity compared to the baseline reference technique. 2025-03-01T00:00:00Z