Fast Position-Aided MIMO Beam Training via Noisy Tensor Completion

Abstract

In this paper, a data-driven position-aided approach is proposed to reduce the training overhead in MIMO systems, by leveraging side information and on-the-field measurements. A data tensor is constructed by collecting beam-training measurements on a subset of positions and beams, and a hybrid noisy tensor completion (HNTC) algorithm is proposed to predict the received power across the coverage area, which exploits both the spatial smoothness and the low-rank property of MIMO channels. A recommendation algorithm based on the completed tensor, beam subset selection (BSS), is proposed to achieve fast and accurate beam-training. Besides, a grouping-based BSS algorithm is proposed to combat the detrimental effect of noisy positional information. Numerical results evaluated with the Quadriga channel simulator at 60 GHz millimeter-wave channels show that the proposed BSS recommendation algorithm in combination with HNTC achieve accurate received power predictions, enabling beam-alignment with small overhead: given power measurements on 40% of possible discretized positions, HNTC-based BSS attains a probability of correct alignment of 91%, with only 2% of trained beams, as opposed to a state-of-the-art position-aided beam-alignment scheme which achieves 54% correct alignment in the same configuration. Finally, an online HNTC method via warm-start is proposed, that alleviates the computational complexity by 50%, with no degradation in prediction accuracy.

arXiv comment13 pages, submitted to the IEEE Journal of Selected Topics in Signal Processing

References in corpus (15)

• Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
• An Overview of Signal Processing Techniques for Millimeter Wave MIMO Systems
• Channel Estimation and Hybrid Precoding for Millimeter Wave Cellular Systems
• Millimeter Wave Beamforming for Wireless Backhaul and Access in Small Cell Networks
• Downlink Training Techniques for FDD Massive MIMO Systems: Open-Loop and Closed-Loop Training with Memory
• Compressed Sensing Based Multi-User Millimeter Wave Systems: How Many Measurements Are Needed?
• Smooth PARAFAC Decomposition for Tensor Completion
• Compressive channel estimation and tracking for large arrays in mm wave picocells
• An Overview of Enhanced Massive MIMO with Array Signal Processing Techniques
• LIDAR Data for Deep Learning-Based mmWave Beam-Selection
• Estimation of low-rank tensors via convex optimization
• Adaptive Beam Tracking with the Unscented Kalman Filter for Millimeter Wave Communication
• Leveraging the Restricted Isometry Property: Improved Low-Rank Subspace Decomposition for Hybrid Millimeter-Wave Systems
• Efficient Sum-of-Sinusoids based Spatial Consistency for the 3GPP New-Radio Channel Model
• The Physics of Compressive Sensing and the Gradient-Based Recovery Algorithms

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