Downlink Channel Reconstruction for Spatial Multiplexing in Massive MIMO Systems
arXiv:2102.05224 · doi:10.1109/TWC.2021.3072158
Abstract
To get channel state information (CSI) at a base station (BS), most of researches on massive multiple-input multiple-output (MIMO) systems consider time division duplexing (TDD) to get benefit from the uplink and downlink channel reciprocity. Even in TDD, however, the BS still needs to transmit downlink training signals, which are referred to as channel state information reference signals (CSI-RSs) in the 3GPP standard, to support spatial multiplexing in practice. This is because there are many cases that the number of transmit antennas is less than the number of receive antennas at a user equipment (UE) due to power consumption and circuit complexity issues. Because of this mismatch, uplink sounding reference signals (SRSs) from the UE are not enough for the BS to obtain full downlink MIMO CSI. Therefore, after receiving the downlink CSI-RSs, the UE needs to feed back quantized CSI to the BS using a pre-defined codebook to support spatial multiplexing. In this paper, possible approaches to reconstruct full downlink MIMO CSI at the BS are proposed by exploiting both the SRS and quantized downlink CSI considering practical antenna structures with reduced downlink CSI-RS overhead. Numerical results show that the spectral efficiencies by spatial multiplexing based on the proposed downlink MIMO CSI reconstruction techniques outperform the conventional methods solely based on the quantized CSI.
arXiv commentSubmitted to IEEE Transactions on Wireless Communications
References in corpus (16)
- Massive MIMO for Next Generation Wireless Systems
- Scaling up MIMO: Opportunities and Challenges with Very Large Arrays
- Channel Estimation and Hybrid Precoding for Millimeter Wave Cellular Systems
- Massive MIMO: Ten Myths and One Critical Question
- Massive MIMO Systems with Non-Ideal Hardware: Energy Efficiency, Estimation, and Capacity Limits
- Distributed Compressive CSIT Estimation and Feedback for FDD Multi-user Massive MIMO Systems
- Spatially Common Sparsity Based Adaptive Channel Estimation and Feedback for FDD Massive MIMO
- Downlink Training Techniques for FDD Massive MIMO Systems: Open-Loop and Closed-Loop Training with Memory
- Structured Compressive Sensing Based Spatio-Temporal Joint Channel Estimation for FDD Massive MIMO
- Overview of Full-Dimension MIMO in LTE-Advanced Pro
- Noncoherent Trellis Coded Quantization: A Practical Limited Feedback Technique for Massive MIMO Systems
- Channel Feedback Based on AoD-Adaptive Subspace Codebook in FDD Massive MIMO Systems
- Optimal Channel Estimation for Reciprocity-Based Backscattering with a Full-Duplex MIMO Reader
- Joint CSIT Acquisition Based on Low-Rank Matrix Completion for FDD Massive MIMO Systems
- Two-Stage Analog Combining in Hybrid Beamforming Systems with Low-Resolution ADCs
- Efficient Channel AoD/AoA Estimation Using Widebeams for Millimeter Wave MIMO Systems