Search for: All records

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 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 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. 

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 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 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. 

We present a new way of producing a channel chart for cellular wireless communications in polar coordinates. We estimate the angle of arrival θ and the distance between the base station and the user equipment ρ using the MUSIC algorithm and inverse of the root sum squares of channel coefficients (ISQ) or linear regression (LR). We compare this method with the channel charting algorithms principal component analysis (PCA), Samson’s method (SM), and autoencoder (AE). We show that ISQ and LR outperform all three in both performance and complexity. The performance of LR and ISQ are close, with ISQ having less complexity.