3D Channel Estimation in Large-Scale MIMO Systems

• Main Authors: Chen, Rei-Ru, 陳佩汝
• Other Authors: Su, Yu- T.
• Format: Others
• Language: en_US
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/48364045598068235553

碩士 === 國立交通大學 === 電信工程研究所 === 103 === We consider a single-cell time-division duplexing (TDD) multi-user (MU) multiple input multiple-output (MIMO) system with a base station (BS) equipped with a large number of antennas serving many single-antenna mobile users. Assuming a linear receiving array at BS, we decompose the spatial channel (correlation) matrix through two-dimensional unitary transforms such as discrete cosine transform (DCT) or discrete Fourier transform (DFT). The channel estimation and related mean angle-of-arrival (AoA) and angle spread (AS) information id extracted two different perspectives; both offer useful insights into the problem at hand while render accurate estimates. From a model-based viewpoint, the transform attempts to describe the channel matrix by a nonparametric regression model or equivalently, projecting it into a predetermined unitary coordinate. We analyze the behavior of the corresponding regressioncoefficients to determine the desired mean AoA and AS values. This approach gives a minimum rank channel representation and explains why a joint mean AoA and channel estimate requires less modeling parameters thus gives improved performance when the AS is not large. An alternate perspective that treats the channel vector (matrix) as the received waveform so the responsibility of the receiver is locating the AoA(s) and the associated AS (beamwidth). Applying a 2D transform on the channel vector (matrix) is equivalent to using a multibeam antenna (beamforming matrix) to search for the directions and spreads of the incoming wavefront which arrives at the receive array in spatial clusters.The spatial search viewpoint raises issues concerning the number of beams, the search range, resolution and the search method, resulting in a variety of estimation options. The 2D channel and AoA/AS estimation problem is extend to the 3D case and the single cell assumption is removed and extended to a multi-cell scenario. As signals from neighboring cells tend to arrive at a BS in different spatial directions, they are likely to be separable in angle domain by our estimate which is capable of identifying the received waveform in both azimuth and altitude (elevation) directions, thereby eliminating most inter-cell interference, pilot contamination included. We analyze the mean squared error (MSE) performance of the channel estimate in both single-cell and multi-cell (with pilot contamination) environments. Numerical results show that we are able to provide quite accurate estimates and suppress most co-channel interference resulted from neighboring pilots, if exists.