A Deep Learning and Geospatial Data-Based Channel Estimation Technique for Hybrid Massive MIMO Systems

by   Xiaoyi Zhu, et al.

This paper presents a novel channel estimation technique for the multi-user massive multiple-input multiple-output (MU-mMIMO) systems using angular-based hybrid precoding (AB-HP). The proposed channel estimation technique generates group-wise channel state information (CSI) of user terminal (UT) zones in the service area by deep neural networks (DNN) and fuzzy c-Means (FCM) clustering. The slow time-varying CSI between the base station (BS) and feasible UT locations in the service area is calculated from the geospatial data by offline ray tracing and a DNN-based path estimation model associated with the 1-dimensional convolutional neural network (1D-CNN) and regression tree ensembles. Then, the UT-level CSI of all feasible locations is grouped into clusters by a proposed FCM clustering. Finally, the service area is divided into a number of non-overlapping UT zones. Each UT zone is characterized by a corresponding set of clusters named as UT-group CSI, which is utilized in the analog RF beamformer design of AB-HP to reduce the required large online CSI overhead in the MU-mMIMO systems. Then, the reduced-size online CSI is employed in the baseband (BB) precoder of AB-HP. Simulations are conducted in the indoor scenario at 28 GHz and tested in an AB-HP MU-mMIMO system with a uniform rectangular array (URA) having 16x16=256 antennas and 22 RF chains. Illustrative results indicate that 91.4 proposed offline channel estimation technique as compared to the conventional online channel sounding. The proposed DNN-based path estimation technique produces same amount of UT-level CSI with runtime reduced by 65.8 to the computationally expensive ray tracing.


page 2

page 4

page 5

page 6

page 11

page 12

page 14

page 17


Deep Learning based Multi-User Power Allocation and Hybrid Precoding in Massive MIMO Systems

This paper proposes a deep learning based power allocation (DL-PA) and h...

A Two-Step Learning and Interpolation Method for Location-Based Channel Database

Timely and accurate knowledge of channel state information (CSI) is nece...

A Deep Learning Framework for Hybrid Beamforming Without Instantaneous CSI Feedback

Hybrid beamformer design plays very crucial role in the next generation ...

Robust User Scheduling with COST 2100 Channel Model for Massive MIMO Networks

This paper considers a Massive multiple-input multiple-output (MIMO) net...

Knowledge-Aided Deep Learning for Beamspace Channel Estimation in Millimeter-Wave Massive MIMO Systems

Millimeter-wave massive multiple-input multiple-output (MIMO) can use a ...

A Multi-Dimensional Matrix Pencil-Based Channel Prediction Method for Massive MIMO with Mobility

This paper addresses the mobility problem in massive multiple-input mult...

Towards Practical Indoor Positioning Based on Massive MIMO Systems

We showcase the practicability of an indoor positioning system (IPS) sol...

Please sign up or login with your details

Forgot password? Click here to reset