CSI Feedback Overhead Reduction for 5G Massive MIMO Systems

Enhancing the throughput of multi-user (MU) massive multiple-input multiple-output (MIMO) networks is one of the biggest promises that the fifth generation (5G) networks are expected to deliver. In the Third Generation Partnership Project (3GPP) New Radio (NR) standardization efforts, downlink precoding designs that balance performance and uplink feedback overhead are being investigated. Most recently, a high-resolution precoder (Type-II codebook) was specified for downlink NR Release (Rel.) 15 wherein the channel state information (CSI) feedback is compressed in the spatial domain via exploiting a Discrete Fourier Transform (DFT)-based codebook structure. An extension of the Type-II codebook for NR Rel. 16 which also exploits frequency correlation to reduce CSI feedback overhead is currently under study. In this paper, an overview of some of the recent developments for Rel. 16 Type-II codebook is provided. In addition, a practical approach is proposed that uses multi-stage quantization of codebook parameters with variable quantization resolution, where the resolution is proportional to the coefficients' amplitude values. This approach helps provide better utilization of the CSI feedback, compared with the case with the same quantization resolution for all coefficients. System-level simulation results are provided which show that the proposed approach significantly reduces the CSI feedback overhead without notable impact on performance.

[1]  Apostolos Papathanassiou,et al.  MU-MIMO and CSI Feedback Performance of NR/LTE , 2019, 2019 53rd Annual Conference on Information Sciences and Systems (CISS).

[2]  Yang Li,et al.  Full dimension MIMO for LTE-Advanced and 5G , 2015, 2015 Information Theory and Applications Workshop (ITA).

[3]  Filippo Tosato,et al.  Overhead Reduction of NR type II CSI for NR Release 16 , 2019, WSA.

[4]  Luis Suarez,et al.  DFT Based Beam-Time Delay Sparse Channel Representation for Channel State Information (CSI) Compression in 5G FDD Massive MIMO Systems , 2018, 2018 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom).

[5]  Eugene Visotsky,et al.  Explicit CSI Feedback Design for 5G New Radio phase II , 2018, WSA.

[6]  Shaohui Sun,et al.  CSI Feedback Based on Spatial and Frequency Domains Compression for 5G Multi-User Massive MIMO Systems , 2019, 2019 IEEE/CIC International Conference on Communications in China (ICCC).

[7]  Thorsten Wild,et al.  Comparison of Explicit CSI Feedback Schemes for 5G New Radio , 2019, 2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring).

[8]  Junil Choi,et al.  Advanced Quantizer Designs for FDD-Based FD-MIMO Systems Using Uniform Planar Arrays , 2017, IEEE Transactions on Signal Processing.

[9]  Li Guo,et al.  Modular and High-Resolution Channel State Information and Beam Management for 5G New Radio , 2018, IEEE Communications Magazine.

[10]  Il-Min Kim,et al.  Deep Autoencoder Based CSI Feedback With Feedback Errors and Feedback Delay in FDD Massive MIMO Systems , 2019, IEEE Wireless Communications Letters.

[11]  Honglei Miao,et al.  Amplitude Quantization for Type-2 Codebook Based CSI Feedback in New Radio System , 2018, 2018 European Conference on Networks and Communications (EuCNC).