2 research outputs found
A Distributed Processing Architecture for Modular and Scalable Massive MIMO Base Stations
In this work, a scalable and modular architecture for massive MIMO base
stations with distributed processing is proposed. New antennas can readily be
added by adding a new node as each node handles all the additional involved
processing. The architecture supports conjugate beamforming, zero-forcing, and
MMSE, where for the two latter cases a central matrix inversion is required.
The impact of the time required for this matrix inversion is carefully analyzed
along with a generic frame format. As part of the contribution, careful
computational, memory, and communication analyses are presented. It is shown
that all computations can be mapped to a single computational structure and
that a processing node consisting of a single such processing element can
handle a broad range of bandwidths and number of terminals
On the Low-Complexity, Hardware-Friendly Tridiagonal Matrix Inversion for Correlated Massive MIMO Systems
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
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