We present new ways of producing a channel chart employing modelbased approaches. We estimate the angle of arrival θ and the distance between the base station and the user equipment ρ 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 rotateandsum (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.
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This content will become publicly available on June 1, 2024
Channel Charting: ModelBased Approaches
We present new ways of producing a channel chart employing modelbased approaches. We estimate the angle of arrival θ and the distance between the base station and the user equipment ρ 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 rotateandsum (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.
more »
« less
 Award ID(s):
 2030029
 NSFPAR ID:
 10440974
 Date Published:
 Journal Name:
 Proc. 2023 IEEE International Conference on Communications (ICC)
 Page Range / eLocation ID:
 1066  1072
 Format(s):
 Medium: X
 Sponsoring Org:
 National Science Foundation
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