This article provides an efficient hardware generator for high-performance sets-of-real-numbers (SORNs) arithmetic. Complex datapaths are automatically built in VHDL, comprising the generation of arithmetic operations and functions as well as all necessary interconnects. Various SORN datatypes can be easily set up enabling high adaptivity to different applications. For further performance improvements additional optimization and implementation techniques are considered as well. For evaluation, a SORN-based symbol detector for a multiantenna wireless communication scenario is generated that solves a system of linear equations. As SORN-based signal processing allows a quick but rough calculation of the system equations outcome, it can be exploited to reject possible input vectors that do not satisfy the general constraints of the signal detection task. Hence, this generally leads to a heavily reduced set of remaining solutions. Multiple hardware architectures with different SORN datatypes are generated and compared. Further, an analysis is performed considering the signal-to-noise ratio (SNR). Finally, logic synthesis is applied to selected designs and compared to references from the literature highlighting this article to be highly hardware-efficient and suitable for application-specific signal processing.

Massive MIMO systems have become more popular in wireless communications due to their improved spectral efficiency compared to existing small-scale MIMO systems. However, current estimation methodes take too long for larger numbers of antennas. In this paper, a near-optimal iterative linear signal detection for massive MIMO is introduced exploiting the random projection method to approximate the channel matrix in a significantly lower dimensional space. This is then used as a preconditioner in the conjugate gradient least squares algorithm to enhance the convergence rate. For evaluation, different scenarios of spatial correlation in a massive MIMO system are considered. In contrast to other low-complexity signal detectors, our approach achieves excellent results in terms of robustness and determined latency.