Works (13)

Updated: September 20th, 2023 05:00

2023 article

Channel Rank Improvement in Urban Drone Corridors Using Passive Intelligent Reflectors

2023 IEEE AEROSPACE CONFERENCE.

By: E. Ozturk n, C. Anjinappa n, F. Erden n, M. Chowdhury, I. Guvenc n, H. Dai n, A. Bhuyan*

TL;DR: The results at 6 GHz, 28 GHz, and 60 GHz show that the proposed approach increases the average rates for all frequency bands for a given drone corridor when compared with the environment with no IRSs present, and IRS-aided channels perform close to each other at sub-6 and mmWave bands. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: September 18, 2023

2022 journal article

Comparative Analysis of Radar-Cross-Section-Based UAV Recognition Techniques

IEEE SENSORS JOURNAL, 22(18), 17932–17949.

By: M. Ezuma n, C. Anjinappa n, V. Semkin* & I. Guvenc n

author keywords: Deep learning (DL); machine learning (ML); radar cross section (RCS); statistical learning (SL); target identification and recognition; unmanned aerial vehicles (UAVs)
TL;DR: The study shows that, while the average accuracy of all the algorithms increases with the signal-to-noise ratio (SNR), the ML algorithm achieved better accuracy than the SL and DL algorithms. (via Semantic Scholar)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: August 4, 2022

2022 journal article

Coverage Probability Analysis of Passive Reflectors in Indoor Environments

IEEE COMMUNICATIONS LETTERS, 26(10), 2287–2291.

By: S. Maeng n, C. Anjinappa n & I. Guvenc n

author keywords: Scattering; Signal to noise ratio; Probability density function; Metals; Mathematical models; Antenna arrays; Transmitting antennas; B5G; indoor coverage; mmWave; non-line-of-sight (NLOS); sub-THz; transparent reflectors
TL;DR: This letter considers an indoor open-door scenario, where the LoS link is blocked by the walls for receivers inside the room, and the Coverage can only be achieved via an NLoS link through a passive reflector, and analytically derive closed-form equations of the reflection visibility probability and the coverage probability. (via Semantic Scholar)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: July 27, 2022

2022 article

Indoor Propagation Measurements with Transparent Reflectors at 28/39/120/144 GHz

2022 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS WORKSHOPS (ICC WORKSHOPS), pp. 1118–1123.

By: C. Anjinappa n, A. Ganesh n, O. Ozdemir n, K. Ridenour n, W. Khawaja n, I. Guvenc n, H. Nomoto*, Y. Ide*

author keywords: 6G; B5G; Indoor coverage; mmWave; non-line-of-sight (NLOS); Sub-THz; Transparent reflectors
UN Sustainable Development Goal Categories
11. Sustainable Cities and Communities (Web of Science; OpenAlex)
Sources: Web Of Science, NC State University Libraries
Added: September 26, 2022

2022 journal article

Radar Cross Section Based Statistical Recognition of UAVs at Microwave Frequencies

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, 58(1), 27–46.

By: M. Ezuma n, C. Anjinappa n, M. Funderburk n & I. Guvenc n

author keywords: Radar cross-sections; Radar; Radar imaging; Target recognition; Signal to noise ratio; Drones; Propellers; Akaike information criterion (AIC); automatic target recognition (ATR); Bayesian information criterion (BIC); classification; compact-range chamber; detection; radar cross-section (RCS); unmanned aerial vehicle (UAV)
TL;DR: From the model selection analysis, it is observed that the lognormal, generalized extreme value, and gamma distributions are most suitable for modeling the RCS of the commercial UAVs while the Gaussian distribution performed relatively well and the best UAV radar statistics forms the class conditional probability densities for the proposed UAV statistical recognition system. (via Semantic Scholar)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: February 10, 2022

2022 article

Ray Tracing Analysis of Sub-6 GHz and mmWave Indoor Coverage with Reflecting Surfaces

2022 IEEE RADIO AND WIRELESS SYMPOSIUM (RWS), pp. 160–163.

By: E. Ozturk n, F. Erden n, K. Du n, C. Anjinappa n, O. Ozdemir n & I. Guvenc n

author keywords: 28 GHz; 60 GHz; indoor propagation; mmWave; ray tracing; reflecting surfaces
TL;DR: The results showed that ray tracing solutions are consistent with theoretical calculations, and using reflective walls significantly improves average received power and connectivity at mmWave bands, e.g., for the given floor plan, coverage increases from 86% to 97.5% at 60 GHz band. (via Semantic Scholar)
UN Sustainable Development Goal Categories
11. Sustainable Cities and Communities (OpenAlex)
Sources: Web Of Science, NC State University Libraries
Added: June 13, 2022

2021 article

A Taxonomy and Survey on Experimentation Scenarios for Aerial Advanced Wireless Testbed Platforms

2021 IEEE AEROSPACE CONFERENCE (AEROCONF 2021).

By: M. Chowdhury n, C. Anjinappa n, I. Guvenc n, M. Sichitiu n, O. Ozdemir n, U. Bhattacherjee n, R. Dutta n, V. Marojevic*, B. Floyd n

Contributors: M. Chowdhury n, C. Anjinappa n, I. Guvenc n, M. Sichitiu n, O. Ozdemir n, U. Bhattacherjee n, R. Dutta n, V. Marojevic*, B. Floyd n

TL;DR: A brief overview of large-scale advanced wireless experimentation platforms broadly available to the wireless research community, including also the Aerial Experimentation and Research Platform for Advanced Wireless (AERPAW). (via Semantic Scholar)
UN Sustainable Development Goal Categories
Sources: Web Of Science, NC State University Libraries, ORCID
Added: September 13, 2021

2021 journal article

Base Station and Passive Reflectors Placement for Urban mmWave Networks

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 70(4), 3525–3539.

By: C. Anjinappa n, F. Erden n & I. Guvenc n

author keywords: Three-dimensional displays; Planning; IP networks; Quality of service; Buildings; Optimization; Bibliographies; 5G; coverage enhancement; GNB location optimization; maximum coverage; metallic reflectors; MmWave
TL;DR: Simulation results show that considering the first-order reflections in planning the mmWave network helps reduce the number of PMRs required to cover the NLoS area, which in turn reduces the deployment cost. (via Semantic Scholar)
UN Sustainable Development Goal Categories
11. Sustainable Cities and Communities (OpenAlex)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: May 6, 2021

2021 journal article

Coverage Hole Detection for mmWave Networks: An Unsupervised Learning Approach

IEEE COMMUNICATIONS LETTERS, 25(11), 3580–3584.

By: C. Anjinappa n & I. Guvenc n

author keywords: Visualization; Manifolds; Unsupervised learning; Linear antenna arrays; Channel estimation; Technological innovation; Radio links; 5G; coverage hole; dimensionality reduction; mmWave; radio hole; t-SNE; UMAP; unsupervised learning
TL;DR: Results on the DeepMIMO dataset scenario demonstrate that the proposed method can learn the structure within the data samples and provide visual holes in the low-dimensional embedding while preserving the CH boundaries. (via Semantic Scholar)
UN Sustainable Development Goal Categories
11. Sustainable Cities and Communities (OpenAlex)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: November 11, 2021

2021 journal article

Slow Beam Steering and NOMA for Indoor Multi-User Visible Light Communications

IEEE TRANSACTIONS ON MOBILE COMPUTING, 20(4), 1627–1641.

By: Y. Eroglu n, C. Anjinappa n, I. Guvenc n & N. Pala*

author keywords: NOMA; Light emitting diodes; Beam steering; Laser beams; Lenses; Resource management; Optimization; Beam steering; Li-Fi; micro-electro-mechanical systems (MEMS); NOMA; optical wireless communications (OWC)
TL;DR: This paper finds the steering angles that simultaneously serve multiple users within the frame duration and maximize the data rates by solving a non-convex optimization problem using a grid-based search and majorization-minimization procedure. (via Semantic Scholar)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: March 5, 2021

2020 article

A Framework for Developing Algorithms for Estimating Propagation Parameters from Measurements

2020 IEEE GLOBECOM WORKSHOPS (GC WKSHPS).

By: A. Sayeed, P. Vouras*, C. Gentile*, A. Weiss n, J. Quimby*, Z. Cheng*, B. Modad*, Y. Zhang* ...

TL;DR: The results indicate that the simple greedy-LS algorithm has the ability to extract MPCs over a large dynamic range, and suggest several avenues for further performance improvement through extensions of the greedy- LS algorithm as well as by incorporating features of other algorithms, such as SAGE and RIMAX. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: July 12, 2021

2020 journal article

Detection and Classification of UAVs Using RF Fingerprints in the Presence of Wi-Fi and Bluetooth Interference

IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY, 1, 60–76.

By: M. Ezuma n, F. Erden n, C. Anjinappa n, O. Ozdemir n & I. Guvenc n

author keywords: Interference; machine learning; Markov models; RF fingerprinting; unmanned aerial vehicles (UAVs); UAV detection and classification
TL;DR: This paper investigates the problem of detection and classification of unmanned aerial vehicles (UAVs) in the presence of wireless interference signals using a passive radio frequency (RF) surveillance system and investigates the performance of the NCA and five different ML classifiers for 15 different types of UAV controllers. (via Semantic Scholar)
UN Sustainable Development Goal Categories
7. Affordable and Clean Energy (OpenAlex)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: January 2, 2020

2019 journal article

Joint Frame Synchronization and Channel Estimation: Sparse Recovery Approach and USRP Implementation

IEEE ACCESS, 7, 39041–39053.

By: O. Ozdemir*, C. Anjinappa*, R. Hamila*, N. Al-Dhahir* & I. Guvenc*

author keywords: Equalization; frame synchronization; large delay spread; MSE; OMP; software defined radio (SDR); sparse channel estimation; sparse recovery; USRP; vehicular communications
TL;DR: This paper proposes several JFSCE methods using popular sparse signal recovery algorithms which exploit the sparsity of the combined channel vector and the sparse channel vector estimate is used to design a sparse equalizer. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: April 22, 2019

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