arXiv subjects

Goutam Das

Publications and source records attributed to Goutam Das.

At least 37 records · Page 2Linked to original sources

arXiv2024

Defending a Static Target Point with a Slow Defender

This paper studies a target-defense game played between a slow defender and a fast attacker. The attacker wins the game if it reaches the target while avoiding the defender's capture disk. The defender wins the game by preventing the attacker from reaching the target, which includes reaching the target and containing it in the capture disk. Depending on the initial condition, the attacker must circumnavigate the defender's capture disk, resulting in a constrained trajectory. This condition produces three phases of the game, which we analyze to solve for the game of kind. We provide the barrier surface that divides the state space into attacker-win and defender win regions, and present the corresponding strategies that guarantee win for each region. Numerical experiments demonstrate the theoretical results as well as the efficacy of the proposed strategies.

eess.SY↗

arXiv2024

Near-perfect Coverage Manifold Estimation in Cellular Networks via conditional GAN

This paper presents a conditional generative adversarial network (cGAN) that translates base station location (BSL) information of any Region-of-Interest (RoI) to location-dependent coverage probability values within a subset of that region, called the region-of-evaluation (RoE). We train our network utilizing the BSL data of India, the USA, Germany, and Brazil. In comparison to the state-of-the-art convolutional neural networks (CNNs), our model improves the prediction error ($L_1$ difference between the coverage manifold generated by the network under consideration and that generated via simulation) by two orders of magnitude. Moreover, the cGAN-generated coverage manifolds appear to be almost visually indistinguishable from the ground truth.

cs.NI↗

arXiv2023

TCP Slice: A semi-distributed TCP algorithm for Delay-constrained Applications

The TCP congestion control protocol serves as the cornerstone of reliable internet communication. However, as new applications require more specific guarantees regarding data rate and delay, network management must adapt. Thus, service providers are shifting from decentralized to centralized control of the network using a software-defined network controller (SDN). The SDN classifies applications and allocates logically separate resources called slices, over the physical network. We propose TCP Slice, a congestion control algorithm that meets specific delay and bandwidth guarantees. Obtaining closed-form delay bounds for a client is challenging due to dependencies on other clients and their traffic stochasticity. We use network calculus to derive the client's delay bound and incorporate it as a constraint in the Network Utility Maximization problem. We solve the resulting optimization using dual decomposition and obtain a semi-distributed TCP protocol that can be implemented with the help of SDN controller and the use of an Explicit Congestion Notification (ECN) bit. Additionally, we also propose a proactive approach for congestion control using digital twin. TCP Slice represents a significant step towards accommodating evolving internet traffic patterns and the need for better network management in the face of increasing application diversity.

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arXiv2023

Higgs boson rapidity distribution in bottom annihilation at NNLL and beyond

We present precise resummed predictions for Higgs boson rapidity distribution through bottom quark annihilation at next-to-next-to-leading logarithmic (NNLL) accuracy matched to next-to-next-to-leading order (NNLO) and at next-to-next-to-next-to-leading logarithmic (N3LL) accuracy matched to next-to-next-to-next-to-leading order soft-virtual (N3LOsv) in the strong coupling. Exploiting the universal behavior of soft radiation near the threshold, we determine the analytic expressions for the process-dependent and universal perturbative ingredients for threshold resummation in double singular limits of partonic threshold variables $z_1,z_2$. Subsequently, the threshold resummation is performed in the double Mellin space within the standard QCD framework. The new third-order process-dependent non-logarithmic coefficients are determined using three-loop bottom quark form factor and third-order quark soft distribution function in rapidity distribution. The effect of these new resummed coefficients are studied at the $13$ TeV LHC. We observe a better perturbative convergence in the resummed predictions on the Higgs rapidity spectrum in bottom quark annihilation. We also find that the NNLL and N3LL corrections are sizeable which typically are of the order of $-2.5\%$ and $-1.5\%$ over the respective available fixed orders with the scale uncertainty remaining at the same level as the fixed order.

hep-ph↗

arXiv2023

AI-assisted Improved Service Provisioning for Low-latency XR over 5G NR

Extended Reality (XR) is one of the most important 5G/6G media applications that will fundamentally transform human interactions. However, ensuring low latency, high data rate, and reliability to support XR services poses significant challenges. This letter presents a novel AI-assisted service provisioning scheme that leverages predicted frames for processing rather than relying solely on actual frames. This method virtually increases the network delay budget and consequently improves service provisioning, albeit at the expense of minor prediction errors. The proposed scheme is validated by extensive simulations demonstrating a multi-fold increase in supported XR users and also provides crucial network design insights.

cs.NI↗

arXiv2023

$Z, W^{\pm}$ rapidity distributions at NNLL and beyond

In this article, we have studied threshold effects on rapidity distributions of massive gauge bosons ($Z, W^{\pm}$) in the Standard Model at the Large Hadron Collider. By exploiting the universal behavior of soft gluon emissions in the threshold region, we resum the large threshold logarithms arising in the rapidity distribution at next-to-next-to leading logarithmic accuracy and match them to next-to-next-to leading order in QCD. We adapt the double Mellin approach to resum both threshold variables corresponding to partonic threshold and rapidity consistently within the standard QCD framework. Furthermore, we have studied in detail the numerical impact of these threshold effects on the rapidity distribution of massive gauge bosons and found a better perturbative convergence in the resummed rapidity spectrum. As a by-product, we also provide all the perturbative ingredients to extend the analysis to next-to-next-to-next-to leading logarithmic accuracy. As a first application of these third order ingredients, we have estimated their effects by matching them with the third order soft-virtual results. Our results will be useful to understand and possibly constrain parton distributions using the rapidity spectrum.

hep-ph↗

arXiv2023

Threshold enhanced cross sections for colorless productions

We study the threshold effect for neutral and charged Drell-Yan productions, associated production of Higgs boson with a massive vector boson and Higgs production in bottom quark annihilation at LHC to the third order in QCD. Using the third order soft-virtual results for these processes and exploiting the universality of the threshold logarithms, we extract the process-dependent coefficients for these processes and resum large threshold logarithms to next-to-next-to-next-to leading logarithmic (N$^3$LL) accuracy. By matching our results to the recently available N$^3$LO results, we provide the most precise theoretical predictions for these processes. We present numerical results for invariant mass distribution and total production cross-sections. We find the conventional scale uncertainties of about $0.4\%$ at N$^3$LO level in the fixed order results get reduced to as small as less than $0.1\%$ at N$^3$LO+N$^3$LL level in the high invariant mass region.

hep-ph↗

arXiv2023

The NNLO quark beam function for jet-veto resummation

We consider the quark beam function that describes collinear initial-state radiation that is constrained by a veto on reconstructed jets. As the veto is imposed on the transverse momenta of the jets, the beam function is subject to rapidity divergences, and we use the collinear-anomaly framework to extract the perturbative matching kernels to next-to-next-to-leading order (NNLO) in the strong-coupling expansion. Our calculation is based on a novel framework that automates the computation of beam functions in Mellin space and it provides the ingredients to extend jet-veto resummations for quark-initiated processes to NNLL$'$ accuracy.

hep-ph↗

arXiv2022

Guarding a Non-Maneuverable Translating Line with an Attached Defender

In this paper we consider a target-guarding differential game where the defender must protect a linearly translating line-segment by intercepting an attacker who tries to reach it. In contrast to common target-guarding problems, we assume that the defender is attached to the target and moves along with it. This assumption affects the defenders' maximum speed in inertial frame, which depends on the target's direction of motion. Zero-sum differential game of degree for both the attacker-win and defender-win scenarios are studied, where the payoff is defined to be the distance between the two agents at the time of game termination. We derive the equilibrium strategies and the Value function by leveraging the solution for the infinite-length target scenario. The zero-level set of this Value function provides the barrier surface that divides the state space into defender-win and attacker-win regions. We present simulation results to demonstrate the theoretical results.

eess.SY↗

arXiv2022

Deep Learning based Coverage and Rate Manifold Estimation in Cellular Networks

This article proposes Convolutional Neural Network-based Auto Encoder (CNN-AE) to predict location-dependent rate and coverage probability of a network from its topology. We train the CNN utilising BS location data of India, Brazil, Germany, and the USA and compare its performance with stochastic geometry (SG) based analytical models. In comparison to the best-fitted SG-based model, CNN-AE improves the coverage and rate prediction errors by a margin of as large as $40\%$ and $25\%$ respectively. As an application, we propose a low complexity, provably convergent algorithm that, using trained CNN-AE, can compute locations of new BSs that need to be deployed in a network in order to satisfy pre-defined spatially heterogeneous performance goals.

cs.NI↗

arXiv2022

Automated Calculation of Beam Functions at NNLO

We present an automated framework for the calculation of beam functions that describe collinear initial-state radiation at hadron colliders at next-to-next-to leading order (NNLO) in perturbation theory. By exploiting the infrared behaviour of the collinear matrix elements, we factorise the phase-space singularities with suitable observable-independent parametrisations. Our numerical approach applies to a large class of collider observables, and as a check of its validity, we compute the quark beam functions for transverse-momentum resummation and N-jettiness, which are known analytically at this order, finding excellent agreement.

hep-ph↗

arXiv2022

Guarding a Translating Line with an Attached Defender

In this paper we consider a Target-guarding differential game where the Defender must protect a linearly moving line segment by intercepting the Attacker who tries to reach it. In contrast to common Target-guarding problems, we assume that the Defender is attached to the Target and moves along with it. This assumption affects the Defender's maximum speed depending on its heading direction. A zero-sum differential game of degree for the Attacker-winning scenario is studied, where the payoff is defined to be the distance between the two agents at the time of reaching the Target. We derive the equilibrium strategies and the Value function by leveraging the solution for the infinite-length Target scenario. The zero-level set of this Value function provides the barrier surface that divides the state space into Defender-winning and Attacker-winning regions. We present simulation results at the end to demonstrate the theoretical results.

eess.SY↗

arXiv2022

CCOMPASSION: A Hybrid Cloudlet Placement Framework over Passive Optical Access Networks

Cloud-based computing technology is one of the most significant technical advents of the last decade and extension of this facility towards access networks by aggregation of cloudlets is a step further. To fulfill the ravenous demand for computational resources entangled with the stringent latency requirements of computationally-heavy applications related to augmented reality, cognitive assistance and context-aware computation, installation of cloudlets near the access segment is a very promising solution because of its support for wide geographical network distribution, low latency, mobility and heterogeneity. In this paper, we propose a novel framework, Cloudlet Cost OptiMization over PASSIve Optical Network (CCOMPASSION), and formulate a nonlinear mixed-integer program to identify optimal cloudlet placement locations such that installation cost is minimized whilst meeting the capacity and latency constraints. Considering urban, suburban and rural scenarios as commonly-used network deployment models, we investigate the feasibility of the proposed model over them and provide guidance on the overall cloudlet facility installation over optical access network. We also study the percentage of incremental energy budget in the presence of cloudlets of the existing network. The final results from our proposed model can be considered as fundamental cornerstones for network planning with hybrid cloudlet network architectures.

cs.NI↗

arXiv2022

Robust QoT Assured Resource Allocation in Shared Backup Path Protection Based EONs

Survivability is mission-critical for elastic optical networks (EONs) as they are expected to carry an enormous amount of data. In this paper, we consider the problem of designing shared backup path protection (SBPP) based EON that facilitates the minimum quality-of-transmission (QoT) assured allocation against physical layer impairments (PLIs) under any single link/shared risk link group (SRLG) failure for static and dynamic traffic scenarios. In general, the effect of PLIs on lightpath varies based on the location of failure of a link as it introduces different active working and backup paths. To address these issues in the design of SBPP EON, we formulate a mixed integer linear programming (MILP) based robust optimization framework for static traffic with the objective of minimizing overall fragmentation. In this process, we use the efficient bitloading technique for spectrum allocation for the first time in survivable EONs. In addition, we propose a novel SBPP-impairment aware (SBPP-IA) algorithm considering the limitations of MILP for larger networks. For this purpose, we introduce a novel sorting technique named most congested working-least congested backup first (MCW-LCBF) to sort the given set of static requests. Next, we employ our SBPP-IA algorithm for dynamic traffic scenario and compare it with existing algorithms in terms of different QoT parameters. We demonstrated through simulations that our study provides around 40% more QoT guaranteed requests compared to existing ones.

cs.NI↗

arXiv2022

Achieving AI-enabled Robust End-to-End Quality of Experience over Radio Access Networks

Emerging applications such as Augmented Reality, the Internet of Vehicles and Remote Surgery require both computing and networking functions working in harmony. The End-to-end (E2E) quality of experience (QoE) for these applications depends on the synchronous allocation of networking and computing resources. However, the relationship between the resources and the E2E QoE outcomes is typically stochastic and non-linear. In order to make efficient resource allocation decisions, it is essential to model these relationships. This article presents a novel machine-learning based approach to learn these relationships and concurrently orchestrate both resources for this purpose. The machine learning models further help make robust allocation decisions regarding stochastic variations and simplify robust optimization to a conventional constrained optimization. When resources are insufficient to accommodate all application requirements, our framework supports executing some of the applications with minimal degradation (graceful degradation) of E2E QoE. We also show how we can implement the learning and optimization methods in a distributed fashion by the Software-Defined Network (SDN) and Kubernetes technologies. Our results show that deep learning-based modelling achieves E2E QoE with approximately 99.8\% accuracy, and our robust joint-optimization technique allocates resources efficiently when compared to existing differential services alternatives.

cs.NI↗

arXiv2022

Online Slice Reconfiguration for End-to-End QoE in 6G Applications

End-to-end (E2E) quality of experience (QoE) for 6G applications depends on the synchronous allocation of networking and computing resources, also known as slicing. However, the relationship between the resources and the E2E QoE outcomes is typically stochastic and non-stationary. Existing works consider known resource demands for slicing and formulate optimization problems for slice reconfiguration. In this work, we create and manage slices by learning the relationship between E2E QoE and resources. We develop a gradient-based online slice reconfiguration algorithm (OSRA) to reconfigure and manage slices in resource-constrained scenarios for radio access networks (RAN). We observe that our methodology meets the QoE requirements with high accuracy compared to existing approaches. It improves upon the existing approaches by approximately 98\% for bursty traffic variations. Our algorithm has fast convergence and achieves low E2E delay violations for lower priority slices.

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arXiv2021

Adaptive Rate NOMA for Cellular IoT Networks

Internet-of-Things (IoT) technology is envisioned to enable a variety of real-time applications by interconnecting billions of sensors/devices deployed to observe some random physical processes. These IoT devices rely on low-power wide-area wireless connectivity for transmitting, mostly fixed- but small-size, status updates of their associated random processes. The cellular networks are seen as a natural candidate for providing reliable wireless connectivity to IoT devices. However, the conventional orthogonal multiple access (OMA) to these massive number of devices is expected to degrade the spectral efficiency. As a promising alternative to OMA, the cellular base stations (BSs) can employ non-orthogonal multiple access (NOMA) for the uplink transmissions of mobile users and IoT devices. In particular, the uplink NOMA can be configured such that the mobile user can adapt transmission rate based on its channel condition while the IoT device transmits at a fixed rate. For this setting, we analyze the ergodic capacity of mobile users and the mean local delay of IoT devices using stochastic geometry. Our analysis demonstrates that the above NOMA configuration can provide better ergodic capacity for mobile users compare to OMA when IoT devices' delay constraint is strict. Furthermore, we also show that NOMA can support a larger packet size for IoT devices than OMA under the same delay constraint.

cs.IT↗

arXiv2021

Learning Aided Auctioning based Spectrum Access System in a Wireless Optical Network

This paper focusses on Service Level Agreement (SLA) based end-to-end Quality of Service (QoS) maintenance across a wireless optical integrated network. We use long term evolution (LTE) based spectrum access system (SAS) in the wireless network and the optical network is comprised of an Ethernet Passive Optical Network (EPON). The proposal targets a learning-based intelligent SAS where opportunistic allocation of any available bandwidth is done after meeting the SLA requirements. Such an opportunistic allocation is particularly beneficial for nomadic users with varying QoS requirements. The opportunistic allocation is carried out with the help of Vickrey-Clarke-Groves (VCG) auction. The proposal allows the users of the integrated network to decide the payment they want to make in order to opportunistically avail bandwidth. Learning automata is used for the users to intelligently converge to the optimal payment value based on the network load. The payment made by the users is later used by the optical network units of the EPON to prepare the bids for the auction. The proposal has been verified through extensive simulations.

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