Multipoint Channel Charting for Wireless Networks

Abstract

Multipoint channel charting is a machine learning framework in which multiple massive MIMO (mMIMO) basestations (BSs) collaboratively learn a multi-cell radio map that characterizes the network environment and the users' spatial distribution pattern. The method utilizes large amounts of highdimensional channel state information (CSI) that is passively collected from spatiotemporal samples by the multiple distributed BSs. At each BS, a high-resolution multi-path channel parameter estimation algorithm extracts features hidden in the acquired CSI. Each BS then constructs a local dissimilarity matrix based on the extracted features for its collected samples, and feeds it to a centralized entity which performs feature fusion and manifold learning to construct a multi-cell channel chart. The objective is to chart the radio geometry of a cellular system in such a way that the spatial distance between two users is correlated with their CSI feature distance. The proposed multipoint charting method is capable of unravelling the topology of a Manhattangrid system, and the neighbor relations between CSI features from different spatial locations are captured almost perfectly

Siamese Neural Networks for Wireless Positioning and Channel Charting

“…Channel charting (CC), as proposed in [16], is unsupervised and uses dimensionality reduction to perform relative positioning solely from CSI measurements, without the need of ground-truth position information. Recent extensions of CC include multi-point CC [24] for systems with simultaneous connectivity to multiple BSs and semisupervised CC with autoencoders [22], which enables the inclusion of partially-annotated datasets. The Siamese network proposed in this paper unifies supervised CSI-based neural-networkbased positioning with unsupervised CC in a single network architecture.…”

Multipoint channel charting-based radio resource management for V2V communications

“…To handle this issue, multiple antennas are used at both the Tx and Rx terminals [3]. Note that UEs of type V2I can have more than one antenna; however, it is shown that one element at the UE can be used to construct an accurate MPCC [21].…”

“…CC is based on the assumption that statistical properties of MIMO channel vary relatively slowly across space, on a length-scale related to the macroscopic distances between scatterers in the channel, not on the small fading length-scale of wavelengths. In this regard, the CSI covariance matrix can be used to capture large-scale effects of the wireless channel based on the assumption that there is a continuous mapping from the spatial location p k of UE k to the covariance CSI R b,k [20,21]:…”

“…CC starts by processing the CSI covariance matrix R b,k into suitable channel features f b,k that capture large-scale properties of the wireless channel. CC then proceeds by using the set of collected features {f b,k } [20,21]). In this paper, we select the feature vector f b,k based on multipath components [21]:…”

“…However, cell edge UEs may not be accurately located in the chart due to their low signal-to-noise ratio (SNR) at the cell edge. In [21], a multipoint CC (MPCC) framework is proposed to support advanced multicell RRM and to accurately map cell edge UEs. First, each BS generates its own dissimilarity matrix between the users it can decode; then, the dissimilarity matrices are fused and used to construct the MPCC.…”