S
Sahabul Alam
Researcher at Carleton University
Publications - 20
Citations - 474
Sahabul Alam is an academic researcher from Carleton University. The author has contributed to research in topics: Noise & Bit error rate. The author has an hindex of 8, co-authored 19 publications receiving 174 citations. Previous affiliations of Sahabul Alam include École de technologie supérieure & Université du Québec.
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A Vision and Framework for the High Altitude Platform Station (HAPS) Networks of the Future
Gunes Karabulut Kurt,Mohammad G. Khoshkholgh,Safwan Alfattani,Ahmed Ibrahim,Tasneem Darwish,Sahabul Alam,Halim Yanikomeroglu,Abbas Yongacoglu +7 more
TL;DR: A vision and framework for the HAPS networks of the future supported by a comprehensive and state-of-the-art literature review is provided and the unrealized potential of HAPS systems is highlighted and elaborate on their unique ability to serve metropolitan areas.
Posted Content
A Vision and Framework for the High Altitude Platform Station (HAPS) Networks of the Future
Gunes Karabulut Kurt,Mohammad G. Khoshkholgh,Safwan Alfattani,Ahmed Ibrahim,Tasneem Darwish,Sahabul Alam,Halim Yanikomeroglu,Abbas Yongacoglu +7 more
TL;DR: In this paper, the authors provide a vision and framework for the HAPS networks of the future supported by a comprehensive and state-of-the-art literature survey, highlighting the undiscovered potential of HAPS systems, and elaborate on their unique ability to serve metropolitan areas.
Journal ArticleDOI
High Altitude Platform Station Based Super Macro Base Station Constellations
TL;DR: In this article, a super macro base station (HAPS-SMBS) is proposed to provide connectivity in a plethora of applications, such as disaster recovery, high capacity, low latency, and computing requirements for highly populated metropolitan areas.
Journal ArticleDOI
NOMA-Based IoT Networks: Impulsive Noise Effects and Mitigation
TL;DR: This work proposes a multistage nonlinear processing approach specifically designed for OFDM-based PDM-NOMA systems to obtain the optimum threshold of the corresponding users, and proposes a deep learning approach to estimate the impulsive noise parameters from the received OFDM symbol.
Journal ArticleDOI
Joint Code-Frequency Index Modulation for IoT and Multi-User Communications
TL;DR: The proposed architecture reduces the peak-to-average-power ratio (PAPR) of orthogonal frequency-division multiplexing (OFDM)-based schemes without relegating the data rate.