arXiv subjects

Madan Mohan Rayguru

Publications and source records attributed to Madan Mohan Rayguru.

4 recordsLinked to original sources

arXiv2026

DRASTIC: A Dynamic Resource Allocation Framework over 6G Network Slicing in Task-aware Closed-Loop Tactile Internet Applications

This work proposes a novel learning driven bandwidth optimization framework called DRASTIC (Dynamic Resource Allocation for Slicing in Task aware Closed loop tactile Internet applications). The proposed framework dynamically allocates resources among network slices supporting both enhanced Mobile Broadband (eMBB) and high reliable low latency communication (HRLLC) users. The algorithm ensures queue stability and meets delay targets with high probability under a Markov-modulated Poisson traffic, exploiting a Lyapunov guided advantage actor critic reinforcement learning technique. The proposed network model includes an open-loop eMBB queue whose arrival and departure are mainly driven by throughput demand, as well as a closed loop HRLLC queue that captures feedback and task execution effects. A task execution dependent dexterity index adjusts the effective arrival rate, creating a feedback aware interaction between the network and the task. A probabilistic delay constraint is incorporated into the objective via Lagrangian relaxation, yielding a min_max optimization framework that enforces latency guarantees while maximizing throughput for both types of users. Simulation results demonstrate that the proposed framework meets diverse Quality of Service (QoS) requirements, maintains queue stability under dynamic wireless and robotic task variation conditions, and outperforms other approaches.

cs.NI↗

arXiv2025

A Hierarchical Optimization Framework Using Deep Reinforcement Learning for Task-Driven Bandwidth Allocation in 5G Teleoperation

The evolution of 5G wireless technology has revolutionized connectivity, enabling a diverse range of applications. Among these are critical use cases such as real time teleoperation, which demands ultra reliable low latency communications (URLLC) to ensure precise and uninterrupted control, and enhanced mobile broadband (eMBB) services, which cater to data-intensive applications requiring high throughput and bandwidth. In our scenario, there are two queues, one for eMBB users and one for URLLC users. In teleoperation tasks, control commands are received in the URLLC queue, where communication delays occur. The dynamic index (DI) controls the service rate, affecting the telerobotic (URLLC) queue. A separate queue models eMBB data traffic. Both queues are managed through network slicing and application delay constraints, leading to a unified Lagrangian-based Lyapunov optimization for efficient resource allocation. We propose a DRL based hierarchical optimization framework that consists of two levels. At the first level, network optimization dynamically allocates resources for eMBB and URLLC users using a Lagrangian functional and an actor critic network to balance competing objectives. At the second level, control optimization finetunes the best gains for robots, ensuring stability and responsiveness in network conditions. This hierarchical approach enhances both communication and control processes, ensuring efficient resource utilization and optimized performance across the network.

cs.NI↗

arXiv2024

Personalized Speech Emotion Recognition in Human-Robot Interaction using Vision Transformers

Emotions are an essential element in verbal communication, so understanding individuals' affect during a human-robot interaction (HRI) becomes imperative. This paper investigates the application of vision transformer models, namely ViT (Vision Transformers) and BEiT (BERT Pre-Training of Image Transformers) pipelines, for Speech Emotion Recognition (SER) in HRI. The focus is to generalize the SER models for individual speech characteristics by fine-tuning these models on benchmark datasets and exploiting ensemble methods. For this purpose, we collected audio data from different human subjects having pseudo-naturalistic conversations with the NAO robot. We then fine-tuned our ViT and BEiT-based models and tested these models on unseen speech samples from the participants. In the results, we show that fine-tuning vision transformers on benchmark datasets and and then using either these already fine-tuned models or ensembling ViT/BEiT models gets us the highest classification accuracies per individual when it comes to identifying four primary emotions from their speech: neutral, happy, sad, and angry, as compared to fine-tuning vanilla-ViTs or BEiTs.

eess.AS↗

arXiv2015

Contraction based stabilization of nonlinear singularly perturbed systems and application to high gain feedback

Recent development of contraction theory based analysis of singularly perturbed system has opened the door for inspecting differential behavior of multi time-scale systems. In this paper a contraction theory based framework is proposed for stabilization of singularly perturbed systems. The primary objective is to design a feedback controller to achieve bounded tracking error for both standard and non-standard singularly perturbed systems. This framework provides relaxation over traditional quadratic Lyapunov based method as there is no need to satisfy interconnection conditions during controller design algorithm. Moreover, the stability bound does not depend on smallness of singularly perturbed parameter. Combined with high gain scaling, the proposed technique is shown to assure contraction of approximate feedback linearizable systems. These findings extend the class of nonlinear systems which can be made contracting.

eess.SY↗