Optimizing Multicarrier Multiantenna Systems for LoS Channel Charting

• URL: http://arxiv.org/abs/2310.03762v2
• Date: Tue, 23 Jul 2024 08:39:54 GMT
• Title: Optimizing Multicarrier Multiantenna Systems for LoS Channel Charting
• Authors: Taha Yassine, Luc Le Magoarou, Matthieu Crussière, Stephane Paquelet,
• Abstract summary: Channel charting (CC) consists in learning a mapping between the space of raw channel observations and a low-dimensional space where close points correspond to channels of user equipments (UEs) close spatially. Among the different methods of learning this mapping, some rely on a distance measure between channel vectors.
• Score: 6.6297544881511055
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Channel charting (CC) consists in learning a mapping between the space of raw channel observations, made available from pilot-based channel estimation in multicarrier multiantenna system, and a low-dimensional space where close points correspond to channels of user equipments (UEs) close spatially. Among the different methods of learning this mapping, some rely on a distance measure between channel vectors. Such a distance should reliably reflect the local spatial neighborhoods of the UEs. The recently proposed phase-insensitive (PI) distance exhibits good properties in this regards, but suffers from ambiguities due to both its periodic and oscillatory aspects, making users far away from each other appear closer in some cases. In this paper, a thorough theoretical analysis of the said distance and its limitations is provided, giving insights on how they can be mitigated. Guidelines for designing systems capable of learning quality charts are consequently derived. Experimental validation is then conducted on synthetic and realistic data in different scenarios.

Related papers

• Deep Learning-Based Pilotless Spatial Multiplexing [8.68775490839808]
  We show that by training the transmitter and receiver jointly, the transmitter can learn such constellation shapes for the spatial streams. This is the first time ML-based spatial multiplexing without channel estimation pilots is demonstrated. The results show that the learned pilotless scheme can outperform a conventional pilot-based system by as much as 15-20% in terms of spectral efficiency.
  arXiv  Detail & Related papers  (2023-12-08T16:38:02Z)
• Channel Estimation for Underwater Visible Light Communication: A Sparse Learning Perspective [1.7830921962643287]
  This paper proposes a sparse learning based underwater visible light channel estimation (SL-UVCE) scheme. Specifically, a deep-unfolding neural network mimicking the classical iterative sparse recovery algorithm of approximate message passing (AMP) is employed. Compared with the existing non-CS-based and CS-based schemes, the proposed scheme shows better performance of accuracy in channel estimation.
  arXiv  Detail & Related papers  (2023-03-13T16:22:16Z)
• A Survey on Deep Learning based Channel Estimation in Doubly Dispersive Environments [7.310043452300738]
  Wireless communications systems are impacted by multi-path fading and Doppler shift in dynamic environments. Only a few pilots are used for channel estimation in conventional approaches to preserve high data rate transmission. Deep learning has been employed for doubly-dispersive channel estimation due to its low-complexity, robustness, and good generalization ability.
  arXiv  Detail & Related papers  (2022-06-05T12:44:50Z)
• iSDF: Real-Time Neural Signed Distance Fields for Robot Perception [64.80458128766254]
  iSDF is a continuous learning system for real-time signed distance field reconstruction. It produces more accurate reconstructions and better approximations of collision costs and gradients.
  arXiv  Detail & Related papers  (2022-04-05T15:48:39Z)
• Learning to Perform Downlink Channel Estimation in Massive MIMO Systems [72.76968022465469]
  We study downlink (DL) channel estimation in a Massive multiple-input multiple-output (MIMO) system. A common approach is to use the mean value as the estimate, motivated by channel hardening. We propose two novel estimation methods.
  arXiv  Detail & Related papers  (2021-09-06T13:42:32Z)
• Model-Driven Deep Learning Based Channel Estimation and Feedback for Millimeter-Wave Massive Hybrid MIMO Systems [61.78590389147475]
  This paper proposes a model-driven deep learning (MDDL)-based channel estimation and feedback scheme for millimeter-wave (mmWave) systems. To reduce the uplink pilot overhead for estimating the high-dimensional channels from a limited number of radio frequency (RF) chains, we propose to jointly train the phase shift network and the channel estimator as an auto-encoder. Numerical results show that the proposed MDDL-based channel estimation and feedback scheme outperforms the state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-04-22T13:34:53Z)
• Channelized Axial Attention for Semantic Segmentation [70.14921019774793]
  We propose the Channelized Axial Attention (CAA) to seamlessly integratechannel attention and axial attention with reduced computationalcomplexity. Our CAA not onlyrequires much less computation resources compared with otherdual attention models such as DANet, but also outperforms the state-of-the-art ResNet-101-based segmentation models on alltested datasets.
  arXiv  Detail & Related papers  (2021-01-19T03:08:03Z)
• Channel Gain Cartography via Mixture of Experts [18.126535422561766]
  Most approaches to build such spectrum maps are location-based, meaning that the input variable to the estimating function is a pair of spatial locations. An alternative location-free approach was recently proposed for spectrum power maps, where the input variable to the maps consists of features extracted from the positioning signals, instead of location estimates. In this work, apart from adapting the location-free features for the CG maps, a method that can combine both approaches is proposed.
  arXiv  Detail & Related papers  (2020-12-08T09:06:57Z)
• Channel-wise Knowledge Distillation for Dense Prediction [73.99057249472735]
  We propose to align features channel-wise between the student and teacher networks. We consistently achieve superior performance on three benchmarks with various network structures.
  arXiv  Detail & Related papers  (2020-11-26T12:00:38Z)
• Hierarchical Optimal Transport for Robust Multi-View Learning [97.21355697826345]
  Two assumptions may be questionable in practice, which limits the application of multi-view learning. We propose a hierarchical optimal transport (HOT) method to mitigate the dependency on these two assumptions. The HOT method is applicable to both unsupervised and semi-supervised learning, and experimental results show that it performs robustly on both synthetic and real-world tasks.
  arXiv  Detail & Related papers  (2020-06-04T22:24:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.