arXiv subjects

Kishor Chandra Joshi

Publications and source records attributed to Kishor Chandra Joshi.

9 recordsLinked to original sources

arXiv2026

Tool Use as Action: Towards Agentic Control in Mobile Core Networks

Artificial Intelligence (AI) will play an essential role in 6G. It will fundamentally reshape the network architecture itself and drive major changes in the design of network entities, interfaces, and procedures. The adoption of agentic AI in next-generation networks is expected to enhance network intelligence and autonomy through agents capable of planning, reasoning, and acting, while also opening up new business opportunities. Under this vision, existing network functions are expected to evolve into AI-enabled agents and tools that deliver both connectivity and beyond-connectivity services. As an initial attempt to move toward this vision, this paper presents a tool-based interface design and an experimental prototype that are based on agentic AI for the mobile core network, with the Model Context Protocol (MCP) and the Agent2Agent (A2A) protocol as foundational protocols. MCP is selected to design the interface between the agent and network tools, and the A2A protocol is used for message exchange between AI agents. In such an experimental setup, we analyze packet-level message flows between the agents, tools, and network functions and break down the latency of end-to-end operations, starting from the prompt injection until the completion of the input task. This work demonstrates how an AI agent-based core network combined with network-specific tools can be utilized in next generation mobile systems to execute intent-based tasks.

cs.NI↗

arXiv2026

Beyond State Machines: Executing Network Procedures with Agentic Tool-Calling Sequences

Agentic AI will be an essential enabling technology for designing future mobile communication systems, which could provide flexible and customized services, automate complex network operations, and drive autonomous decision-making across the network. This work studies how Large Language Model (LLM)-based network AI agents can be utilized to execute network procedures expressed as sequences of tool invocations. We investigate four approaches, which differ in how the agent obtains the procedure and in how execution is distributed between the agent and the underlying tools. We evaluated the latency and execution correctness across these approaches using a User Equipment (UE) IP allocation procedure as a case study. Furthermore, we conduct a stress test to examine how many sequential procedural steps an LLM agent can reliably execute before failure. Our results show that approaches relying on iterative agent-side reasoning incur higher latency and are more prone to execution errors, while approaches where the procedure is encapsulated within a single tool, which internally orchestrates the required steps by invoking other tools, reduce latency by limiting repeated reasoning. The stress-test results further show that the model with advanced tool-calling capability maintains reliable execution over longer procedures than the other evaluated models; however, all models exhibit reliability degradation as procedure length increases, revealing clear execution limits in multi-step tool-based workflows. To systematically analyze failures in procedure execution, we introduce a procedure-specific error taxonomy that categorizes deviations in multi-step procedural execution.

cs.NI↗

arXiv2025

Accurate Performance Predictors for Edge Computing Applications

Accurate prediction of application performance is critical for enabling effective scheduling and resource management in resource-constrained dynamic edge environments. However, achieving predictable performance in such environments remains challenging due to the co-location of multiple applications and the node heterogeneity. To address this, we propose a methodology that automatically builds and assesses various performance predictors. This approach prioritizes both accuracy and inference time to identify the most efficient model. Our predictors achieve up to 90% accuracy while maintaining an inference time of less than 1% of the Round Trip Time. These predictors are trained on the historical state of the most correlated monitoring metrics to application performance and evaluated across multiple servers in dynamic co-location scenarios. As usecase we consider electron microscopy (EM) workflows, which have stringent real-time demands and diverse resource requirements. Our findings emphasize the need for a systematic methodology that selects server-specific predictors by jointly optimizing accuracy and inference latency in dynamic co-location scenarios. Integrating such predictors into edge environments can improve resource utilization and result in predictable performance.

cs.DC↗

arXiv2025

Morpheus: Lightweight RTT Prediction for Performance-Aware Load Balancing

Distributed applications increasingly demand low end-to-end latency, especially in edge and cloud environments where co-located workloads contend for limited resources. Traditional load-balancing strategies are typically reactive and rely on outdated or coarse-grained metrics, often leading to suboptimal routing decisions and increased tail latencies. This paper investigates the use of round-trip time (RTT) predictors to enhance request routing by anticipating application latency. We develop lightweight and accurate RTT predictors that are trained on time-series monitoring data collected from a Kubernetes-managed GPU cluster. By leveraging a reduced set of highly correlated monitoring metrics, our approach maintains low overhead while remaining adaptable to diverse co-location scenarios and heterogeneous hardware. The predictors achieve up to 95% accuracy while keeping the prediction delay within 10% of the application RTT. In addition, we identify the minimum prediction accuracy threshold and key system-level factors required to ensure effective predictor deployment in resource-constrained clusters. Simulation-based evaluation demonstrates that performance-aware load balancing can significantly reduce application RTT and minimize resource waste. These results highlight the feasibility of integrating predictive load balancing into future production systems.

cs.DC↗

arXiv2025

Machine Learning-Driven Anomaly Detection for 5G O-RAN Performance Metrics

The ever-increasing reliance of critical services on network infrastructure coupled with the increased operational complexity of beyond-5G/6G networks necessitate the need for proactive and automated network fault management. The provision for open interfaces among different radio access network\,(RAN) elements and the integration of AI/ML into network architecture enabled by the Open RAN\,(O-RAN) specifications bring new possibilities for active network health monitoring and anomaly detection. In this paper we leverage these advantages and develop an anomaly detection framework that proactively detect the possible throughput drops for a UE and minimize the post-handover failures. We propose two actionable anomaly detection algorithms tailored for real-world deployment. The first algorithm identifies user equipment (UE) at risk of severe throughput degradation by analyzing key performance indicators (KPIs) such as resource block utilization and signal quality metrics, enabling proactive handover initiation. The second algorithm evaluates neighbor cell radio coverage quality, filtering out cells with anomalous signal strength or interference levels. This reduces candidate targets for handover by 41.27\% on average. Together, these methods mitigate post-handover failures and throughput drops while operating much faster than the near-real-time latency constraints. This paves the way for self-healing 6G networks.

cs.NI↗

arXiv2020

Performance of Intelligent Reconfigurable Surface-Based Wireless Communications Using QAM Signaling

Intelligent reconfigurable surface (IRS) is being seen as a promising technology for 6G wireless networks. The IRS can reconfigure the wireless propagation environment, which results in significant performance improvement of wireless communications. In this paper, we analyze the performance of bandwidth-efficient quadrature amplitude modulation (QAM) techniques for IRS-assisted wireless communications over Rayleigh fading channels. New closed-form expressions of the generic average symbol error rate (ASER) for rectangular QAM, square QAM and cross QAM schemes are derived. Moreover, simplified expressions of the ASER for low signal-to-noise-ratio (SNR) and high SNR regions are also presented, which are useful to provide insights analytically. We comprehensively analyze the impact of modulation parameters and the number of IRS elements employed. We also verify our theoretical results through simulations. Our results demonstrate that employing IRS significantly enhances the ASER performance in comparison to additive white Gaussian noise channel at a low SNR regime. Thus, IRS-assisted wireless communications can be a promising candidate for various low powered communication applications such as internet-of-things (IoT).

cs.IT↗

arXiv2019

Reinforcing Edge Computing with Multipath TCP Enabled Mobile Device Clouds

In recent years, enormous growth has been witnessed in the computational and storage capabilities of mobile devices. However, much of this computational and storage capabilities are not always fully used. On the other hand, popularity of mobile edge computing which aims to replace the traditional centralized powerful cloud with multiple edge servers is rapidly growing. In particular, applications having strict latency requirements can be best served by the mobile edge clouds due to a reduced round-trip delay. In this paper we propose a Multi-Path TCP (MPTCP) enabled mobile device cloud (MDC) as a replacement to the existing TCP based or D2D device cloud techniques, as it effectively makes use of the available bandwidth by providing much higher throughput as well as ensures robust wireless connectivity. We investigate the congestion in mobile-device cloud formation resulting mainly due to the message passing for service providing nodes at the time of discovery, service continuity and formation of cloud composition. We propose a user space agent called congestion handler that enable offloading of packets from one sub-flow to the other under link quality constraints. Further, we discuss the benefits of this design and perform preliminary analysis of the system.

cs.NI↗

arXiv2019

Association, Blockage and Handoffs in IEEE 802.11ad based 60GHz Picocells- A Closer Look

The link misalignment and high susceptibility to blockages are the biggest hurdles in realizing 60GHz based wireless local area networks (WLANs). However, much of the previous studies investigating 60GHz alignment and blockage issues do not provide an accurate quantitative evaluation from the perspective of WLANs. In this paper, we present an in-depth quantitative evaluation of commodity IEEE 802.11ad devices by forming a 60GHz WLAN with two docking stations mimicking as access points (APs). Through extensive experiments, we provide important insights about directional coverage pattern of antennas, communication range and co-channel interference and blockages. We are able to measure the IEEE 802.11ad link alignment and association overheads in absolute time units. With a very high accuracy (96-97%), our blockage characterization can differentiate between temporary and permanent blockages caused by humans in the indoor environment, which is a key insight. Utilizing our blockage characterization, we also demonstrate intelligent handoff to alternate APs using consumergrade IEEE 802.11ad devices. Our blockage-induced handoff experiments provide important insights that would be helpful in integrating millimeter wave based WLANs into future wireless networks.

cs.NI↗

arXiv2019

Analyzing the Trade-offs in Using Millimeter Wave Directional Links for High Data Rate Tactile Internet Applications

Ultra-low latency and high reliability communications are the two defining characteristics of Tactile Internet (TI). Nevertheless, some TI applications would also require high data-rate transfer of audio-visual information to complement the haptic data. Using Millimeter wave (mmWave) communications is an attractive choice for high datarate TI applications due to the availability of large bandwidth in the mmWave bands. Moreover, mmWave radio access is also advantageous to attain the airinterface-diversity required for high reliability in TI systems as mmWave signal propagation significantly differs to sub-6GHz propagation. However, the use of narrow beamwidth in mmWave systems makes them susceptible to link misalignment-induced unreliability and high access latency. In this paper, we analyze the trade-offs between high gain of narrow beamwidth antennas and corresponding susceptibility to misalignment in mmWave links. To alleviate the effects of random antenna misalignment, we propose a beamwidth-adaptation scheme that significantly stabilize the link throughput performance.

cs.NI↗