Search Results for author: Animesh Yadav

Found 9 papers, 0 papers with code

Impact of Objective Function on Spectral Efficiency in Integrated HAPS-Terrestrial Networks

no code implementations14 Mar 2024 Afsoon Alidadi Shamsabadi, Animesh Yadav, Halim Yanikomeroglu

Integrating non-terrestrial networks (NTNs), in particular high altitude platform stations (HAPS), with terrestrial networks, referred to as vHetNets, emerges as a promising future wireless network architecture for providing ubiquitous connectivity.

Fairness

Strategic Client Selection to Address Non-IIDness in HAPS-enabled FL Networks

no code implementations10 Jan 2024 Amin Farajzadeh, Animesh Yadav, Halim Yanikomeroglu

The deployment of federated learning (FL) within vertical heterogeneous networks, such as those enabled by high-altitude platform station (HAPS), offers the opportunity to engage a wide array of clients, each endowed with distinct communication and computational capabilities.

Federated Learning

Enhancing Next-Generation Urban Connectivity: Is the Integrated HAPS-Terrestrial Network a Solution?

no code implementations17 Jul 2023 Afsoon Alidadi Shamsabadi, Animesh Yadav, Halim Yanikomeroglu

Located in the stratospheric layer of Earth's atmosphere, high altitude platform station (HAPS) is a promising network infrastructure, which can bring significant advantages to sixth-generation (6G) and beyond wireless communications systems by forming vertical heterogeneous networks (vHetNets).

Fairness

Multi-Tier Hierarchical Federated Learning-assisted NTN for Intelligent IoT Services

no code implementations9 May 2023 Amin Farajzadeh, Animesh Yadav, Halim Yanikomeroglu

In the ever-expanding landscape of the IoT, managing the intricate network of interconnected devices presents a fundamental challenge.

Federated Learning Management

FLSTRA: Federated Learning in Stratosphere

no code implementations1 Feb 2023 Amin Farajzadeh, Animesh Yadav, Omid Abbasi, Wael Jaafar, Halim Yanikomeroglu

We propose a federated learning (FL) in stratosphere (FLSTRA) system, where a high altitude platform station (HAPS) facilitates a large number of terrestrial clients to collaboratively learn a global model without sharing the training data.

Federated Learning

Resource-Efficient HAPS-RIS Enabled Beyond-Cell Communications

no code implementations23 Jul 2022 Safwan Alfattani, Animesh Yadav, Halim Yanikomeroglu, Abbas Yongacoglu

Particularly, unsupported UEs will be connected to a dedicated control station (CS) through RIS-mounted HAPS.

Full-Duplex Non-Orthogonal Multiple Access Cooperative Overlay Spectrum-Sharing Networks with SWIPT

no code implementations19 Nov 2020 Quang Nhat Le, Animesh Yadav, Nam-Phong Nguyen, Octavia A. Dobre, Ruiqin Zhao

Numerical results show that FD, SWIPT, and NOMA techniques greatly boost the performance of cooperative spectrum-sharing network in terms of outage probability, system throughput, and sum rate compared to that of half-duplex NOMA and the conventional orthogonal multiple access-time division multiple access networks.

STS

A Novel Spectrally-Efficient Uplink Hybrid-Domain NOMA System

no code implementations17 Jul 2020 Chen Quan, Animesh Yadav, Baocheng Geng, Pramod K. Varshney, H. Vincent Poor

This paper proposes a novel hybrid-domain (HD) non-orthogonal multiple access (NOMA) approach to support a larger number of uplink users than the recently proposed code-domain NOMA approach, i. e., sparse code multiple access (SCMA).

Clustering

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