arXiv subjects

Lopamudra Kundu

Publications and source records attributed to Lopamudra Kundu.

8 recordsLinked to original sources

arXiv2026

AI-Native 6G for Distributed Intelligence: Traffic Characteristics, Awareness, and AI Grid

The sixth-generation (6G) of mobile networks will be shaped not only by artificial intelligence (AI)-enabled network automation and optimization, but also by the need to serve AI as a 6G-native workload. Emerging AI services introduce traffic and compute demands that differ from conventional mobile broadband. Their user experience depends on how quickly useful information is delivered, how bursty and asymmetric multimodal flows are handled, and where inference, retrieval, caching, and content processing are executed. This article presents a joint connectivity-compute view of AI-native 6G. We first characterize representative AI service traffic in terms of uplink/downlink throughput skew, burstiness, and token latency. Next, we discuss how fifth-generation extended reality awareness mechanisms can evolve toward AI traffic characteristics awareness in 6G. Finally, we introduce AI Grid as a distributed AI infrastructure platform for placing workloads according to latency, cost, policy, and service-level constraints. Together, AI-aware connectivity and AI Grid enable 6G as a distributed intelligence platform.

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arXiv2025

AI-RAN: Transforming RAN with AI-driven Computing Infrastructure

The radio access network (RAN) landscape is undergoing a transformative shift from traditional, communication-centric infrastructures towards converged compute-communication platforms. This article introduces AI-RAN which integrates both RAN and artificial intelligence (AI) workloads on the same infrastructure. By doing so, AI-RAN not only meets the performance demands of future networks but also improves asset utilization. We begin by examining how RANs have evolved beyond mobile broadband towards AI-RAN and articulating manifestations of AI-RAN into three forms: AI-for-RAN, AI-on-RAN, and AI-and-RAN. Next, we identify the key requirements and enablers for the convergence of communication and computing in AI-RAN. We then provide a reference architecture for advancing AI-RAN from concept to practice. To illustrate the practical potential of AI-RAN, we present a proof-of-concept that concurrently processes RAN and AI workloads utilizing NVIDIA Grace-Hopper GH200 servers. Finally, we conclude the article by outlining future work directions to guide further developments of AI-RAN.

cs.AI↗

arXiv2024

A Primer on Generative AI for Telecom: From Theory to Practice

The rise of generative artificial intelligence (GenAI) is transforming the telecom industry. GenAI models, particularly large language models (LLMs), have emerged as powerful tools capable of driving innovation, improving efficiency, and delivering superior customer services in telecom. This paper provides an overview of GenAI for telecom from theory to practice. We review GenAI models and discuss their practical applications in telecom. Furthermore, we describe the key technology enablers and best practices for applying GenAI to telecom effectively. We highlight the importance of retrieval augmented generation (RAG) in connecting LLMs to telecom domain specific data sources to enhance the accuracy of the LLMs' responses. We present a real-world use case on RAG-based chatbot that can answer open radio access network (O-RAN) specific questions. The demonstration of the chatbot to the O-RAN Alliance has triggered immense interest in the industry. We have made the O-RAN RAG chatbot publicly accessible on GitHub.

cs.NI↗

arXiv2024

Towards Energy Efficient RAN: From Industry Standards to Trending Practice

As 5G deployments continue throughout the world, concerns regarding its energy consumption have gained significant traction. This article focuses on radio access networks (RANs) which account for a major portion of the network energy use. Firstly, we introduce the state-of-the-art 3GPP and O-RAN standardization work on enhancing RAN energy efficiency. Then we highlight three unique ways for enabling energy optimization in telecommunication networks, including full stack acceleration, network functions consolidation, and shared infrastructure between communication and artificial intelligence. These network design strategies not only allow for considerable overall reduction in the energy footprint, but also deliver several added benefits including improved throughput, reduced cost of ownership, and increased revenue opportunities for telcos.

cs.NI↗

arXiv2023

Hardware Acceleration for Open Radio Access Networks: A Contemporary Overview

Radio access networks (RAN) are going through a paradigm shift towards interoperable, intelligent, software-defined, and cloud-native open RAN solutions. A key challenge towards the adoption and deployment of open RAN at scale is performance. Hence, it is critical to leverage the power of hardware acceleration to offload compute-heavy RAN workloads to specialized hardware devices to enable accelerated compute for open RAN deployments. In this article, we provide a state-of-the-art overview of hardware acceleration for open RAN in the fifth generation (5G) wireless networks. We also present a practical implementation of inline hardware acceleration for open RAN layer 1 processing and identify several areas for future exploration towards the sixth generation (6G) wireless networks.

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arXiv2022

6G Digital Twin Networks: From Theory to Practice

Digital twin networks (DTNs) are real-time replicas of physical networks. They are emerging as a powerful technology for design, diagnosis, simulation, what-if-analysis, and artificial intelligence (AI)/machine learning (ML) driven real-time optimization and control of the sixth-generation (6G) wireless networks. Despite the great potential of what digital twins can offer for 6G, realizing the desired capabilities of 6G DTNs requires tackling many design aspects including data, models, and interfaces. In this article, we provide an overview of 6G DTNs by presenting prominent use cases and their service requirements, describing a reference architecture, and discussing fundamental design aspects. We also present a real-world example to illustrate how DTNs can be built upon and operated in a real-time reference development platform - Omniverse.

cs.NI↗

arXiv2022

Embracing AI in 5G-Advanced Towards 6G: A Joint 3GPP and O-RAN Perspective

Artificial intelligence (AI) has emerged as a powerful technology that improves system performance and enables new features in 5G and beyond. Standardization, defining functionality and interfaces, is essential for driving the industry alignment required to deliver the mass adoption of AI in 5G-Advanced and 6G. However, fragmented efforts in different standards bodies, such as the third generation partnership project (3GPP) and the open radio access network (O-RAN) Alliance, can lead to confusion and uncertainty about which standards to follow and which aspects of the standards to embrace. This article provides a joint 3GPP and O-RAN perspective on the state of the art in AI adoption in mobile communication systems, including the fundamentals of 5G architecture and its evolution towards openness and intelligence, AI for 5G-Advanced evolution, and a case study on AI-enabled traffic steering. We also identify several areas for future exploration to accelerate AI adoption on the path towards 6G.

cs.NI↗

arXiv2018

Physical Uplink Control Channel Design for 5G New Radio

The next generation wireless communication system, 5G, or New Radio (NR) will provide access to information and sharing of data anywhere, anytime by various users and applications with diverse multi-dimensional requirements. Physical Uplink Control Channel (PUCCH), which is mainly utilized to convey Uplink Control Information (UCI), is a fundamental building component to enable NR system. Compared to Long Term Evolution (LTE), more flexible PUCCH structure is specified in NR, aiming to support diverse applications and use cases. This paper describes the design principles of various NR PUCCH formats and the underlying physical structures. Further, extensive simulation results are presented to explain the considerations behind the NR PUCCH design.

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