Downlink performance of hybrid precoding in massive MIMO systems subject to phase noise

Recent works have identified hybrid analog-digital processing as a cost-effective alternative to conventional full-digital processing in massive multiple-input multiple-output (MIMO) systems owing to the deployment of far less number of radio frequency (RF) chains. In this paper, we analyze the impact of oscillator phase noise on hybrid precoding in massive MIMO systems. Relying on the time division duplexing (TDD) operation, we design an efficient uplink (UL) channel estimation scheme suitable for the hybrid architecture by exploiting channel covariance matrices (CCMs) of sparse massive MIMO channels. Additionally, we employ a CCM aided RF precoder combined with regularized zero-forcing (RZF) precoding performed in the baseband. Subsequently, we derive the deterministic equivalent (DE) for the downlink (DL) signal-to-interference-plus-noise ratio (SINR), which visibly reveals the impact of phase noise on the performance of hybrid precoding in massive MIMO systems. Finally, we verify the analytical result and show the performance comparison of the proposed hybrid precoding scheme for different phase noise variances through Monte Carlo (MC) simulations.

[1]  Derrick Wing Kwan Ng,et al.  Multi-User Precoding and Channel Estimation for Hybrid Millimeter Wave Systems , 2017, IEEE Journal on Selected Areas in Communications.

[2]  Wei Yu,et al.  Hybrid Digital and Analog Beamforming Design for Large-Scale Antenna Arrays , 2016, IEEE Journal of Selected Topics in Signal Processing.

[3]  Xiaohu You,et al.  Uplink Spectral Efficiency Analysis of Distributed Massive MIMO With Channel Impairments , 2017, IEEE Access.

[4]  Leyuan Pan Efficient pilot-data transmission and channel estimation in next generation wireless communication systems , 2017 .

[5]  Xianbin Wang,et al.  Hybrid Analog-Digital Channel Estimation and Beamforming: Training-Throughput Tradeoff , 2015, IEEE Transactions on Communications.

[6]  Xiaohu You,et al.  An overview of transmission theory and techniques of large-scale antenna systems for 5G wireless communications , 2016, Science China Information Sciences.

[7]  Xiaodai Dong,et al.  Low-Complexity Hybrid Precoding in Massive Multiuser MIMO Systems , 2014, IEEE Wireless Communications Letters.

[8]  Thomas L. Marzetta,et al.  Pilot Contamination and Precoding in Multi-Cell TDD Systems , 2009, IEEE Transactions on Wireless Communications.

[9]  Robert W. Heath,et al.  Limited Feedback Hybrid Precoding for Multi-User Millimeter Wave Systems , 2014, IEEE Transactions on Wireless Communications.

[10]  Mérouane Debbah,et al.  Massive MIMO in the UL/DL of Cellular Networks: How Many Antennas Do We Need? , 2013, IEEE Journal on Selected Areas in Communications.

[11]  Sailes K. Sengijpta Fundamentals of Statistical Signal Processing: Estimation Theory , 1995 .

[12]  Anastasios K. Papazafeiropoulos Impact of General Channel Aging Conditions on the Downlink Performance of Massive MIMO , 2016, IEEE Transactions on Vehicular Technology.

[13]  Xiaohu You,et al.  Channel Estimation for Massive MIMO-OFDM Systems by Tracking the Joint Angle-Delay Subspace , 2016, IEEE Access.

[14]  Xiaohu You,et al.  TDD reciprocity calibration for multi-user massive MIMO systems with iterative coordinate descent , 2015, Science China Information Sciences.

[15]  Emil Björnson,et al.  Massive MIMO with Non-Ideal Arbitrary Arrays: Hardware Scaling Laws and Circuit-Aware Design , 2014, IEEE Transactions on Wireless Communications.

[16]  Xiaohu You,et al.  Impact of RF mismatches on the performance of massive MIMO systems with ZF precoding , 2016, Science China Information Sciences.

[17]  Robert W. Heath,et al.  Exploiting Spatial Channel Covariance for Hybrid Precoding in Massive MIMO Systems , 2017, IEEE Transactions on Signal Processing.

[18]  Theodore S. Rappaport,et al.  Low-Rank Spatial Channel Estimation for Millimeter Wave Cellular Systems , 2014, IEEE Transactions on Wireless Communications.