arXiv subjects

Prasanna Chaporkar

Publications and source records attributed to Prasanna Chaporkar.

At least 19 recordsLinked to original sources

arXiv2026

Handover-Optimal User Association Policy for LEO Satellite-based 5G NTN

The integration of Non Terrestrial Networks into 5G and beyond cellular systems has introduced a significant paradigm shift, enabling ubiquitous connectivity and extending services to previously unconnected and underserved remote regions. In particular, Low Earth Orbit satellites, operating close to the Earth surface, can provide communication latency comparable to that of terrestrial networks. However, due to their high mobility, LEO satellites trigger frequent handovers, which degrade users quality of experience and increase signaling overhead. In this work, our objective is to minimize the number of handovers in a LEO satellite system while preventing satellite overloading. We formulate the problem within a game theoretic framework and apply the Spatial Adaptive Play algorithm to obtain a handover efficient and load balanced solution. Additionally, we propose a low complexity heuristic algorithm to achieve similar objectives with reduced computational overhead.

cs.ET↗

arXiv2026

A Survey of UAV Communication Networks: Roles, Power Sources, and Security

Driven by the demands of 5G/Beyond 5G and 6G networks, Unmanned Aerial Vehicles (UAVs) have surfaced in critical roles for aerial communications. In the present survey, we explore the multi-mode roles of UAVs as relays, User Equipment (UE), gNB and Reconfigurable Intelligent Surfaces (RIS), along with their deployment scenarios, architectural frameworks, and different communication models incorporating Artificial Intelligence (AI) configurations. We consider the effects of alternate power sources on the communication payload. The survey also aims to address security issues in the UAV communications. As an advancement, we propose a novel UAV-Network-in-a-Box (NIB) architecture for disaster recovery and temporary coverage as an alternative to traditional network infrastructure.

cs.ET↗

arXiv2026

Post-Decision State-Based Online Learning for Delay-Energy-Aware Flow Allocation in Wireless Systems

We develop a structure-aware reinforcement learning (RL) approach for delay- and energy-aware flow allocation in 5G User Plane Functions (UPFs). We consider a dynamic system with $K$ heterogeneous UPFs of varying capacities that handle stochastic arrivals of $M$ flow types, each with distinct rate requirements. We model the system as a Markov decision process (MDP) to capture the stochastic nature of flow arrivals and departures (possibly unknown), as well as the impact of flow allocation in the system. To solve this problem, we propose a post-decision state (PDS) based value iteration algorithm that exploits the underlying structure of the MDP. By separating action-controlled dynamics from exogenous factors, PDS enables faster convergence and efficient adaptive flow allocation, even in the absence of statistical knowledge about exogenous variables. Simulation results demonstrate that the proposed method converges faster and achieves lower long-term cost than standard Q-learning, highlighting the effectiveness of PDS-based RL for resource allocation in wireless networks.

eess.SP↗

arXiv2025

ITU-T Y.2325: NGN Evolution Towards Future

International Telecommunications Union (ITU) defined Next Generation Network (NGN) underlies most wireline and wireless packet-based telecommunications networks. A key design principle of NGN is decoupling of service-related functions from the underlying transport stratum, making user services independent of transport technologies. Interestingly, the NGN architecture, as defined in ITU standards, did not follow this design principle for internal network services, e.g., mobility, or authentication though adhering for external user services like IPTV or Multimedia services. These internal services are handled by the NGN transport control plane, making them an intrinsic part of the transport stratum, resulting in a tightly coupled service and transport functionality as opposed to the proclaimed design goal. This design choice may force each transport technology to support internal services individually, e.g., separate authentication service for each transport, leading to duplication. Since the NGN architecture is the base underlying architecture for most packet-based telecommunications network including advanced cellular networks like 4th/5th Generation cellular networks, the limitation persists in these cellular networks as well. To remedy the situation, the decoupling of service and transport can be generalized to include internal services like mobility and authentication also. In this context, the recently published ITU Y.2325 recommendation, defines an evolved NGN architecture, wherein all services, including internal network services, are decoupled from the transport stratum. The proposal results in a more scalable and modular evolved NGN architecture that can be used as a template for all future telecom networks including IMT-2030 (6th generation mobile networks). In this article, we review the evolved NGN architecture, as proposed in ITU-T Y.2325.

cs.NI↗

arXiv2025

AI/ML in 3GPP 5G Advanced -- Services and Architecture

The 3rd Generation Partnership Project (3GPP), the standards body for mobile networks, is in the final phase of Release 19 standardization and is beginning Release 20. Artificial Intelligence/ Machine Learning (AI/ML) has brought about a paradigm shift in technology and it is being adopted across industries and verticals. 3GPP has been integrating AI/ML into the 5G advanced system since Release 18. This paper focuses on the AI/ML related technological advancements and features introduced in Release 19 within the Service and System Aspects (SA) Technical specifications group of 3GPP. The advancements relate to two paradigms: (i) enhancements that AI/ML brought to the 5G advanced system (AI for network), e.g. resource optimization, and (ii) enhancements that were made to the 5G system to support AI/ML applications (Network for AI), e.g. image recognition.

cs.ET↗

arXiv2025

Energy-Aware 6G Network Design: A Survey

6th Generation (6G) mobile networks are envisioned to support several new capabilities and data-centric applications for unprecedented number of users, potentially raising significant energy efficiency and sustainability concerns. This brings focus on sustainability as one of the key objectives in the their design. To move towards sustainable solution, research and standardization community is focusing on several key issues like energy information monitoring and exposure, use of renewable energy, and use of Artificial Intelligence/Machine Learning (AI/ML) for improving the energy efficiency in 6G networks. The goal is to build energy-aware solutions that takes into account the energy information resulting in energy efficient networks. Design of energy-aware 6G networks brings in new challenges like increased overheads in gathering and exposing of energy related information, and the associated user consent management. The aim of this paper is to provide a comprehensive survey of methods used for design of energy efficient 6G networks, like energy harvesting, energy models and parameters, classification of energy-aware services, and AI/ML-based solutions. The survey also includes few use cases that demonstrate the benefits of incorporating energy awareness into network decisions. Several ongoing standardization efforts in 3GPP, ITU, and IEEE are included to provide insights into the ongoing work and highlight the opportunities for new contributions. We conclude this survey with open research problems and challenges that can be explored to make energy-aware design feasible and ensure optimality regarding performance and energy goals for 6G networks.

cs.NI↗

arXiv2025

Threshold-Based Optimal Arm Selection in Monotonic Bandits: Regret Lower Bounds and Algorithms

In multi-armed bandit problems, the typical goal is to identify the arm with the highest reward. This paper explores a threshold-based bandit problem, aiming to select an arm based on its relation to a prescribed threshold $τ$. We study variants where the optimal arm is the first above $τ$, the $k^{th}$ arm above or below it, or the closest to it, under a monotonic structure of arm means. We derive asymptotic regret lower bounds, showing dependence only on arms adjacent to $τ$. Motivated by applications in communication networks (CQI allocation), clinical dosing, energy management, recommendation systems, and more. We propose algorithms with optimality validated through Monte Carlo simulations. Our work extends classical bandit theory with threshold constraints for efficient decision-making.

cs.LG↗

arXiv2025

Clus-UCB: A Near-Optimal Algorithm for Clustered Bandits

We study a stochastic multi-armed bandit setting where arms are partitioned into known clusters, such that the mean rewards of arms within a cluster differ by at most a known threshold. While the clustering structure is known a priori, the arm means are unknown. We derive an asymptotic lower bound on the regret that improves upon the classical bound of Lai & Robbins (1985). We then propose Clus-UCB, an efficient algorithm that closely matches this lower bound asymptotically. Clus-UCB is designed to exploit the clustering structure and introduces a new index to evaluate an arm, which depends on other arms within the cluster. In this way, arms share information among each other. We present simulation results of our algorithm and compare its performance against KL-UCB and other wellknown algorithms for bandits with dependent arms. Finally, we address some limitations of this work and conclude by mentioning some possible future research.

cs.LG↗

arXiv2024

Joint User and Beam Selection in Millimeter Wave Networks

We study the problem of selecting a user equipment (UE) and a beam for each access point (AP) for concurrent transmissions in a millimeter wave (mmWave) network, such that the sum of weighted rates of UEs is maximized. We prove that this problem is NP-complete. We propose two algorithms -- Markov Chain Monte Carlo (MCMC) based and local interaction game (LIG) based UE and beam selection -- and prove that both of them asymptotically achieve the optimal solution. Also, we propose two fast greedy algorithms -- NGUB1 and NGUB2 -- for UE and beam selection. Through extensive simulations, we show that our proposed greedy algorithms outperform the most relevant algorithms proposed in prior work and perform close to the asymptotically optimal algorithms.

eess.SY↗

arXiv2023

Multi-Connectivity for Multicast Video Streaming in Cellular Networks (Extended Abstract)

In video streaming applications especially during live streaming events (such as the Super Bowl), video traffic can account for a significant portion of network traffic and can lead to severe network congestion. During such events, multicast transmission can be used to avoid network congestion since the same video content is being streamed to multiple users simultaneously. However, providing seamless connectivity to cellular users in multicast streaming remains an open problem. To address this issue, this paper explores the potential of using multi-connectivity (MC) in wireless multicast streaming. Our results reveal that MC significantly improves the performance of multicast services, especially for cell edge users who often suffer from poor channel conditions. We prove that optimal resource allocation in MC multicast streaming is an NP-hard problem. Therefore, we propose a greedy approximation algorithm for this problem with an approximation factor of $(1-1/e)$. We also prove that no other polynomial-time algorithm can provide a better approximation.

cs.NI↗

arXiv2022

Leveraging Multi-Connectivity for Multicast Video Streaming

Multi-connectivity has emerged as a key enabler for providing seamless connectivity in cellular mobile networks. However, its potential for improving the quality of multicast transmissions has remained unexplored. In this paper, we investigate the use of multi-connectivity in wireless multicast streaming. Multi-connectivity can significantly improve the performance of multicast services. It especially benefits the cell edge users who often suffer from poor channel conditions. In this work, we assess the impact of multi-connectivity on the performance of multicast streaming. We propose procedures for establishing multi-connectivity in a multicast system and address the associated resource allocation problem. We prove that the optimal resource allocation problem is NP-hard. We propose a greedy approximation algorithm for this problem and prove that no other polynomial-time algorithm can provide a better approximation. Since video streaming is the primary use case under consideration here, we use traces from actual videos to generate realistic video traffic patterns in our simulations. Our simulation results clearly establish that multi-connectivity results in considerable performance improvement in multicast streaming.

cs.NI↗

arXiv2022

A Flexible IAB Architecture for Beyond 5G Network

IAB is an innovative wireless backhaul solution to provide cost-efficient deployment of small cells for successful 5G adoption. Besides, IAB can utilize the same spectrum for access and backhaul purposes. The 3GPP standardized IAB in Release 16 and would incorporate a few enhancements in the upcoming releases. The 3GPP IAB architecture, however, suffers from some limitations, such as it does not support mobile relays or dual-connectivity. This article presents a novel IAB architecture that addresses these limitations and is transparent to legacy operations of the 5G system. The architecture also supports multi-RAT coexistence where access and backhaul may belong to different RATs. These factors (and many others) enable operators to capitalize on the architecture for deploying IAB anywhere in a plug-and-play manner. We also show the merits of the architecture by evaluating its capacity and mobility robustness compared to the 3GPP architecture. Simulation results corroborate our design approach. Owing its robust design, the architecture can contend for standardization in B5G system.

cs.NI↗

arXiv2021

Efficient Coding Approach Towards Non-Linear Spectro-Temporal Receptive Fields

Linear Non-Linear(LN) models are widely used to characterize the receptive fields of early-stage auditory processing. We apply the principle of efficient coding to the LN model of Spectro-Temporal Receptive Fields(STRFs) of the neurons in primary auditory cortex. The Efficient Coding Principle has been previously used to understand early visual receptive fields and linear STRFs in auditory processing. Efficient coding is realized by jointly optimizing the mutual information between stimuli and neural responses subjected to the metabolic cost of firing spikes. We compare the predictions of the efficient coding principle with the physiological observations, which match qualitatively under realistic conditions of noise in stimuli and the spike generation process.

q-bio.NC↗

arXiv2021

Online Reinforcement Learning of Optimal Threshold Policies for Markov Decision Processes

To overcome the curses of dimensionality and modeling of Dynamic Programming (DP) methods to solve Markov Decision Process (MDP) problems, Reinforcement Learning (RL) methods are adopted in practice. Contrary to traditional RL algorithms which do not consider the structural properties of the optimal policy, we propose a structure-aware learning algorithm to exploit the ordered multi-threshold structure of the optimal policy, if any. We prove the asymptotic convergence of the proposed algorithm to the optimal policy. Due to the reduction in the policy space, the proposed algorithm provides remarkable improvements in storage and computational complexities over classical RL algorithms. Simulation results establish that the proposed algorithm converges faster than other RL algorithms.

cs.LG↗

arXiv2020

Proportional Fairness through Dual Connectivity in Heterogeneous Networks

Proportional Fair (PF) is a scheduling technique to maintain a balance between maximizing throughput and ensuring fairness to users. Dual Connectivity (DC) technique was introduced by the 3rd Generation Partnership Project (3GPP) to improve the mobility robustness and system capacity in heterogeneous networks. In this paper, we demonstrate the utility of DC in improving proportional fairness in the system. We propose a low complexity centralized PF scheduling scheme for DC and show that it outperforms the standard PF scheduling scheme. Since the problem of dual association of users for maximizing proportional fairness in the system is NP-hard, we propose three heuristic user association schemes for DC. We demonstrate that DC, along with the proposed PF scheme, gives remarkable gains on PF utility over single connectivity and performs almost close to the optimal PF scheme in heterogeneous networks.

cs.NI↗

arXiv2020

Open5G: A Software-Defined Networking Protocol for 5G Multi-RAT Wireless Networks

Mobile Networks today comprise of multiple Radio Access Technologies (RATs), e.g., 4G LTE, Wireless Local Area Network (WLAN), and the upcoming 5G-New Radio (5G-NR). The access networks of these RATs are controlled by RAT-specific entities, e.g., the resource management function located inside an individual LTE eNB is used for the eNB control, or access controllers are used for controlling WLAN Access Points. Even in the 3GPP's 5G architecture, which has a common Core supporting multiple RATs, radio access related decisions are taken independently within individual RATs. Due to the fragmented nature of control-plane in multi-RAT Radio Access Network (RAN), a unified global view of network resources is unavailable, hindering optimized allocation of resources. It also brings complexity to the features involving multiple RATs, e.g., dual connectivity. To address these issues, we introduced an SDN-based Multi-RAT RAN architecture (SMRAN) in our earlier work [arXiv:1812.11825], where the RAN control-plane is segregated from the data-plane. As part of the SMRAN architecture, we defined a logically centralized multi-RAT RAN Controller and individual RAT-specific data-plane functions. In the current work, we define a protocol, called Open5G, to be used for control and management of the SMRAN data-plane. Open5G is based on OpenFlow (OF) and OF-Config, which are commonly used protocols in the SDN-based wired networks and data centers.

cs.NI↗

arXiv2019

MIST: A Novel Training Strategy for Low-latency Scalable Neural Net Decoders

In this paper, we propose a low latency, robust and scalable neural net based decoder for convolutional and low-density parity-check (LPDC) coding schemes. The proposed decoders are demonstrated to have bit error rate (BER) and block error rate (BLER) performances at par with the state-of-the-art neural net based decoders while achieving more than 8 times higher decoding speed. The enhanced decoding speed is due to the use of convolutional neural network (CNN) as opposed to recurrent neural network (RNN) used in the best known neural net based decoders. This contradicts existing doctrine that only RNN based decoders can provide a performance close to the optimal ones. The key ingredient to our approach is a novel Mixed-SNR Independent Samples based Training (MIST), which allows for training of CNN with only 1\% of possible datawords, even for block length as high as 1000. The proposed decoder is robust as, once trained, the same decoder can be used for a wide range of SNR values. Finally, in the presence of channel outages, the proposed decoders outperform the best known decoders, {\it viz.} unquantized Viterbi decoder for convolutional code, and belief propagation for LDPC. This gives the CNN decoder a significant advantage in 5G millimeter wave systems, where channel outages are prevalent.

eess.SP↗

arXiv2019

Minimum Cost Feedback Selection in Structured Systems: Hardness and Approximation Algorithm

In this paper, we study output feedback selection in linear time-invariant structured systems. We assume that the inputs and the outputs are dedicated, i.e., each input directly actuates a single state and each output directly senses a single state. Given a structured system with dedicated inputs and outputs and a cost matrix that denotes the cost of each feedback connection, our aim is to select an optimal set of feedback connections such that the closed-loop system satisfies arbitrary pole-placement. This problem is referred to as the optimal feedback selection problem for dedicated i/o. We first prove the NP-hardness of the problem using a reduction from a well known NP-hard problem, the weighted set cover problem. In addition, we also prove that the optimal feedback selection problem for dedicated i/o is inapproximable to a constant factor of log n, where n denotes the system dimension. To this end, we propose an algorithm to find an approximate solution to the optimal feedback selection problem for dedicated i/o. The proposed algorithm consists of a potential function incorporated with a greedy scheme and attains a solution with a guaranteed approximation ratio. Then we consider two special network topologies of practical importance, referred to as back-edge feedback structure and hierarchical networks. For the first case, which is NP-hard and inapproximable to a multiplicative factor of log n, we provide a (log n)-approximate solution, where n denotes the system dimension. For hierarchical networks, we give a dynamic programming based algorithm to obtain an optimal solution in polynomial time.

math.OC↗