Channel Charting Assisted Beam Tracking

Abstract

We propose a novel beam-tracking algorithm based on channel charting (CC) which maintains the communication link between a base station (BS) and a mobile user equipment (UE) in a millimeter wave (mmWave) mobile communications system. Our method first uses large-scale channel state information information at the BS in order to learn a CC. The points in the channel chart are then annotated with the signal-to-noise ratio (SNR) of best beams. One can then leverage this CC-to-SNR mapping in order to track strong beams between UEs and BS efficiently and robustly at very low beam-search overhead. Simulation results in a mmWave scenario show that the performance of the CC-assisted beam tracking method approaches that of an exhaustive beam-search approach while requiring significantly lower beam-search overhead than conventional tracking methods.

Optimality of the Discrete Fourier Transform for Beamspace Massive MU-MIMO Communication

“…Hence, we note that when the channel chart represents the global geometry well, then an affine transform learned from a handful of labeled samples can result in a good positioning performance. 6 However, the shortcomings of B2 will be apparent in our next scenario. While the channel chart of P1 demonstrates that the bilateration and LoS bounding-box losses alone can help create a channel chart in real-world coordinates, P1 is outperformed by all the other methods in all performance metrics.…”

“…Since a smaller difference in power is more likely to be caused by small-scale fading (and not by distance), we would be less confident in deducing which AP might be closer. Hence, observing that the AP-side receive power is not perfectly inversely proportional to distance motivates the margin parameter M p in (6). By setting M p > 0, we can avoid some false AP pairs, i.e., pairs (a c , a f ) ∈ P (n) for which ∥x (n) − x(ac) ∥ > ∥x (n) − x(a f ) ∥.…”

“…In addition, we include experimental results with measured channel vectors from [2]. management [5], [6], channel capacity prediction [7], and many more; see [8] for an overview.…”

Channel Charting in Real-World Coordinates

“…III-A, this approach is weakly-supervised. In addition, this method also demonstrates the use of large λ values in (5) as…”

“…The key idea is to apply dimensionality reduction to a large database of CSI features that represent large-scale properties of the wireless channel; this leads to a low-dimensional latent space-the socalled channel chart-that is tied to UE position. This UE pseudo-position information can be used to assist a wide range of applications in wireless communication systems, such as pilot allocation [3], beam management [4], [5], channel capacity prediction [6], and many more [7].…”