  In massive MIMO (M-MIMO) systems, one of the key challenges in the
implementation is the large-scale matrix inversion operation, as widely used in
channel estimation, equalization, detection, and decoding procedures.
Traditionally, to handle this complexity issue, several low-complexity matrix
inversion approximation methods have been proposed, including the classic
Cholesky decomposition and the Neumann series expansion (NSE). However, the
conventional approaches failed to exploit neither the special structure of
channel matrices nor the critical issues in the hardware implementation, which
results in poorer throughput performance and longer processing delay. In this
paper, by targeting at the correlated M-MIMO systems, we propose a modified NSE
based on tridiagonal matrix inversion approximation (TMA) to accommodate the
complexity as well as the performance issue in the conventional hardware
implementation, and analyze the corresponding approximation errors. Meanwhile,
we investigate the VLSI implementation for the proposed detection algorithm
based on a Xilinx Virtex-7 XC7VX690T FPGA platform. It is shown that for
correlated massive MIMO systems, it can achieve near-MMSE performance and $630$
Mb/s throughput. Compared with other benchmark systems, the proposed pipelined
TMA detector can get high throughput-to-hardware ratio. Finally, we also
propose a fast iteration structure for further research.
