arXiv subjects

Koteswararao Kondepu

Publications and source records attributed to Koteswararao Kondepu.

8 recordsLinked to original sources

arXiv2026

ADORN: Adaptive Drift handling for Open RAN using Reinforcement Learning

Dynamic traffic variations in Open Radio Access Networks (O-RAN) lead to drift, which degrades the performance of Artificial Intelligence/Machine Learning (AI/ML) models. Traditional retraining approaches maintain forecasting accuracy but incur high computational cost and may lead to violations of Service Level Agreements (SLAs). This work proposes a Q-learning-based adaptive retraining approach that formulates the retraining decision as a Markov Decision Process (MDP), where a Reinforcement Learning (RL) agent learns a policy that balances forecasting accuracy and retraining cost. The proposed approach incorporates a multi-expert Long Short-Term Memory (LSTM) ensemble to mitigate catastrophic forgetting and improve robustness across diverse traffic conditions. Experimental results show that the proposed approach effectively reduces retraining overhead compared to greedy and random baselines, while maintaining system performance within predefined limits.

cs.NI↗

arXiv2025

FALCON: A Framework for Fault Prediction in Open RAN Using Multi-Level Telemetry

O-RAN has brought in deployment flexibility and intelligent RAN control for mobile operators through its disaggregated and modular architecture using open interfaces. However, this disaggregation introduces complexities in system integration and network management, as components are often sourced from different vendors. In addition, the operators who are relying on open source and virtualized components -- which are deployed on commodity hardware -- require additional resilient solutions as O-RAN deployments suffer from the risk of failures at multiple levels including infrastructure, platform, and RAN levels. To address these challenges, this paper proposes FALCON, a fault prediction framework for O-RAN, which leverages infrastructure-, platform-, and RAN-level telemetry to predict faults in virtualized O-RAN deployments. By aggregating and analyzing metrics from various components at different levels using AI/ML models, the FALCON framework enables proactive fault management, providing operators with actionable insights to implement timely preventive measures. The FALCON framework, using a Random Forest classifier, outperforms two other classifiers on the predicted telemetry, achieving an average accuracy and F1-score of more than 98%.

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arXiv2024

An Efficient Error Estimation Method in Quantum Key Distribution

Error estimation is an important step for error correction in quantum key distribution. Traditional error estimation methods require sacrificing a part of the sifted key, forcing a trade-off between the accuracy of error estimation and the size of the partial sifted key to be used and discarded. In this paper, we propose a hybrid approach that aims to preserve the entire sifted key after error estimation while preventing Eve from gaining any advantage. The entire sifted key, modified and extended by our proposed method, is sent for error estimation in a public channel. Although accessible to an eavesdropper, the modified and extended sifted key ensures that the number of attempts to crack it remains the same as when no information is leaked. The entire sifted key is preserved for subsequent procedures, indicating the efficient utilization of quantum resources.

quant-ph↗

arXiv2024

Generative-AI for AI/ML Model Adaptive Retraining in Beyond 5G Networks

Beyond fifth-generation (B5G) networks aim to support high data rates, low-latency applications, and massive machine communications. Artificial Intelligence/Machine Learning (AI/ML) can help to improve B5G network performance and efficiency. However, dynamic service demands of B5G use cases cause AI/ML model performance degradation, resulting in Service Level Agreements (SLA) violations, over- or under-provisioning of resources, etc. Retraining is essential to address the performance degradation of the AI/ML models. Existing threshold and periodic retraining approaches have potential disadvantages, such as SLA violations and inefficient resource utilization for setting a threshold parameter in a dynamic environment. This paper proposes a novel approach that predicts when to retrain AI/ML models using Generative Artificial Intelligence. The proposed predictive approach is evaluated for a Quality of Service Prediction use case on the Open Radio Access Network (O-RAN) Software Community platform and compared to the predictive approach based on the classifier and a threshold approach. Also, a realtime dataset from the Colosseum testbed is considered to evaluate Network Slicing (NS) use case with the proposed predictive approach. The results show that the proposed predictive approach outperforms both the classifier-based predictive and threshold approaches.

cs.NI↗

arXiv2022

An Analytical Study on Functional Split in Martian 3D Networks

As space agencies are planning manned missions to reach Mars, researchers need to pave the way for supporting astronauts during their sojourn. This will also be achieved by providing broadband and low-latency connectivity through wireless network infrastructures. In such a framework, we propose a Martian deployment of a 3-Dimensional (3D) network acting as Cloud Radio Access Network (C-RAN). The scenario consists, mostly, of unmanned aerial vehicles (UAVs) and nanosatellites. Thanks to the thin Martian atmosphere, CubeSats can stably orbit at very-low-altitude. This allows to meet strict delay requirements to split functions of the baseband processing between drones and CubeSats. The detailed analytical study, presented in this paper, confirmed the viability of the proposed 3D architecture, under some constraints and trade-off concerning the involved Space communication infrastructures, that are discussed in detail.

cs.NI↗

arXiv2021

A Comprehensive Study of Virtual Machine and Container Based Core Network Components Migration in OpenROADM SDN-Enabled Network

With the increasing demand for openness, flexibility, and monetization the Network Function Virtualization (NFV) of mobile network functions has become the embracing factor for most mobile network operators. Early reported field deployments of virtualized Evolved Packet Core (EPC) - the core network component of 4G LTE and 5G non-standalone mobile networks - reflect this growing trend. To best meet the requirements of power management, load balancing, and fault tolerance in the cloud environment, the need for live migration for these virtualized components cannot be shunned. Virtualization platforms of interest include both Virtual Machines (VMs) and Containers, with the latter option offering more lightweight characteristics. The first contribution of this paper is the implementation of a number of custom functions that enable migration of Containers supporting virtualized EPC components. The current CRIU-based migration of Docker Container does not fully support the mobile network protocol stack. CRIU extensions to support the mobile network protocol stack are therefore required and described in the paper. The second contribution is an experimental-based comprehensive analysis of live migration in two backhaul network settings and two virtualization technologies. The two backhaul network settings are the one provided by CloudLab and one based on a programmable optical network testbed that makes use of OpenROADM dense wavelength division multiplexing (DWDM) equipment. The paper compares the migration performance of the proposed implementation of OpenAirInterface (OAI) based containerized EPC components with the one utilizing VMs, running in OpenStack. The presented experimental comparison accounts for a number of system parameters and configurations, image size of the virtualized EPC components, network characteristics, and signal propagation time across the OpenROADM backhaul network.

cs.NI↗

arXiv2021

Dimensioning of V2X Services in 5G Networks through Forecast-based Scaling

With the increasing adoption of intelligent transportation systems and the upcoming era of autonomous vehicles, vehicular services (such as, remote driving, cooperative awareness, and hazard warning) will face an ever changing and dynamic environment. Traffic flows on the roads is a critical condition for these services and, therefore, it is of paramount importance to forecast how they will evolve over time. By knowing future events (such as, traffic jams), vehicular services can be dimensioned in an on-demand fashion in order to minimize Service Level Agreements (SLAs) violations, thus reducing the chances of car accidents. This research departs from an evaluation of traditional time-series techniques with recent Machine Learning (ML)-based solutions to forecast traffic flows in the roads of Torino (Italy). Given the accuracy of the selected forecasting techniques, a forecast-based scaling algorithm is proposed and evaluated over a set of dimensioning experiments of three distinct vehicular services with strict latency requirements. Results show that the proposed scaling algorithm enables resource savings of up to a 5% at the cost of incurring in an increase of less than 0.4% of latency violations.

cs.NI↗

arXiv2018

5G fronthaul-latency and jitter studies of CPRI over ethernet

Common Public Radio Interface (CPRI) is a successful industry cooperation defining the publicly available specification for the key internal interface of radio base stations between the radio equipment control (REC) and the radio equipment (RE) in the fronthaul of mobile networks. However, CPRI is expensive to deploy, consumes large bandwidth, and currently is statically configured. On the other hand, an Ethernet-based mobile fronthaul will be cost-efficient and more easily reconfigurable. Encapsulating CPRI over Ethernet (CoE) is an attractive solution, but stringent CPRI requirements such as delay and jitter are major challenges that need to be met to make CoE a reality. This study investigates whether CoE can meet delay and jitter requirements by performing FPGA-based Verilog experiments and simulations. Verilog experiments show that CoE encapsulation with fixed Ethernet frame size requires about tens of microseconds. Numerical experiments show that the proposed scheduling policy of CoE flows on Ethernet can reduce jitter when redundant Ethernet capacity is provided. The reduction in jitter can be as large as 1 μs, hence making Ethernet-based mobile fronthaul a credible technology.

cs.NI↗