sravanikurma

  Metaverse aims to blend physical and virtual worlds for next-gen web and social networking apps using technologies like extended reality, digital twins, AI, and Blockchain. However, energy-inefficient extensive resource interactions hinder their large-scale deployment for low-cost mobile users and create challenges for next-gen URLLC services. Digital twin (DT) and mobile edge computing (MEC) have emerged as potential solutions to manifest the realistic deployment of the metaverse and minimize total computations and end-to-end latency. RIS has been identified as a key enabler for smart propagation environments, which significantly boosts channel gain diversity and improves MEC performance w.r.t. energy and spectral efficiency, task offloading rates, e2e delay, computations capabilities, etc. DT-enabled metaverse architectures are contemplated as a holistic digital mapping technology for intelligent resource allocation and network management, while RIS-aided communication captivat...

FIRST WE COVER THIS, THEN WE WALK ON OUR WAY TOWARDS THAT. Good day everyone. My name is Sravani Kurma, and I'm here today to present a topic on the Deep Reinforcement Learning (DRL) approach on the spectral-energy efficiency tradeoff in RIS-aided FD MU-MIMO systems. Introduction: To begin with, RIS are planar arrays of low-cost, passive elements that can reflect and manipulate electromagnetic waves, allowing them to shape the propagation environment and enhance the signal quality. This means that RIS can be used to create customized wireless channels that are optimized for specific user requirements, resulting in improved signal quality and higher data rates. One of the main advantages of using RIS is their ability to significantly enhance the spatial multiplexing capabilities of wireless systems, especially when used in combination with full-duplex multi-user MIMO. With RIS, multiple users can transmit and receive data simultaneously without interfering with each other, which is...