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Amr Aly

dblp:343/5530 · DBLP profile ↗
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2ranked-venue papers
2as first author
2since 2021 · last 2024
0000-0003-2164-8845ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 2 · 2 first-author · 2 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer networks
1 paper
Physical-layer communications · 61% Wireless sensing and localization · 30% Network measurement and analytics · 9%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Wireless sensing and localization › localization algorithms
channel charting
0.812024
Model-Based Approaches to Channel Charting · IEEE Trans. Commun. 2024
Physical-layer communications › signal processing for communications › array signal processing
direction-of-arrival estimation
0.812024
Model-Based Approaches to Channel Charting · IEEE Trans. Commun. 2024
Physical-layer communications › signal processing for communications › array signal processing › direction-of-arrival estimation
MUSIC
0.812024
Model-Based Approaches to Channel Charting · IEEE Trans. Commun. 2024
Network measurement and analytics
distance estimation
0.212024
Model-Based Approaches to Channel Charting · IEEE Trans. Commun. 2024

Methods — techniques the papers use, named apart from their topics

principal component analysis · 0.8linear regression · 0.8autoencoder · 0.8
YearPublicationVenuePosition
2024 Model-Based Approaches to Channel Charting
abstract
We present new ways of producing a channel chart employing model-based approaches. We estimate the angle of arrival$\theta $and the distance$\rho $between the base station and the user equipment by employing our algorithms, inverse of the root sum squares of channel coefficients (ISQ) algorithm, linear regression (LR) algorithm, and MUSIC/MUSIC (MM) algorithm. We compare these methods with the channel charting algorithms principal component analysis (PCA), Sammon’s method (SM), and autoencoder (AE). We show that ISQ, LR, and MM surpass PCA, SM, and AE in performance. We also compare our algorithm MM with an algorithm from the literature that uses the MUSIC algorithm jointly on$\theta $and$\rho $. We call this algorithm the JM algorithm. JM performs very slightly better than MM but at a substantial increase in complexity. Finally, we introduce the rotate-and-sum (RS) algorithm which has about the same performance as the MM and JM algorithms. Unlike MUSIC, RS does not employ eigenvalue and eigenvector analysis. Thus, it is more suitable for direct register transfer logic (RTL) implementation.
Amr Aly, Ender Ayanoglu
IEEE Trans. Commun.1
2023 Channel Charting: Model-Based Approaches
abstract
We present new ways of producing a channel chart employing model-based approaches. We estimate the angle of arrival θ and the distance between the base station and the user equipment$p$by employing our algorithms, inverse of the root sum squares of channel coefficients (ISQ) algorithm, linear regression (LR) algorithm, and the MUSIC/MUSIC (MM) algorithm. We compare these methods with the channel charting algorithms principal component analysis (PCA), Sammon's method (SM), and autoencoder (AE) from [1]. We show that ISQ, LR, and MM outperform all three in performance. ISQ and LR have similar performance with ISQ having less complexity than LR. The performance of MM is better than ISQ and LR but it is more complex. Finally, we introduce the rotate-and-sum (RS) algorithm which has about the same performance as the MM algorithm but is less complex due to the avoidance of the eigenvector and eigenvalue analysis and a potential register transfer logic (RTL) implementation.
Amr Aly, Ender Ayanoglu
ICC1