Fixed-point arithmetic detectors for massive MIMO-OFDM systems

Abstract

In this paper, the performance of massive multiple input multiple output (MIMO) systems is investigated using reduced detection implementations for MIMO detectors. The motivation for this paper is the need for a reduced complexity detector to be implemented as an optimum massive MIMO detector with low precision. We used different decomposition schemes to build the linear detector based on the (IEEE 754) standard in addition to user-defined precision for selected detectors. Simulations are used to demonstrate the behaviour of several matrix inversion schemes under reduced bit resolution. The numerical results demonstrate improved performance when using QRD and pivoted LDLT decomposition schemes at reduced precision

Low‐complexity linear precoding for low‐PAPR massive MU‐MIMO‐OFDM downlink systems

“…RZF data precoding and OPNS PCSs precoding can achieve good performance, but they require large-scale matrix inversions that entail high computational complexity and potential stability issues in fixed point implementations. 23 In addition, we consider gradient-iterative method (cast from our previous algorithm called MU-PP-GDm 7 ), which needs many iterations to achieve the desired performance, leading then to a high computational complexity. In order to overcome this issue, we investigate the optimization of M-POLY-based data and PCSs precoders to perform jointly the MU precoding and the PAPR reduction.…”

“…Furthermore, we provide a detailed computational complexity analysis for the different studied linear precoders, including RZF, orthogonal projection onto null space (OPNS), gradient‐iterative method, and M‐POLY for both data and PCSs precoding. We investigate the complexity‐performance tradeoff of different data and PCSs precoders. RZF data precoding and OPNS PCSs precoding can achieve good performance, but they require large‐scale matrix inversions that entail high computational complexity and potential stability issues in fixed point implementations 23 . In addition, we consider gradient‐iterative method (cast from our previous algorithm called MU‐PP‐GDm 7 ), which needs many iterations to achieve the desired performance, leading then to a high computational complexity.…”