A Scalable Generator for Massive MIMO Baseband Processing Systems with Beamspace Channel Estimation

Abstract

This paper describes a scalable, highly portable, and energy-efficient generator for massive multiple-input multiple-output (MIMO) baseband processing systems. This generator is written in Chisel and produces hardware instances for a scalable massive MIMO system employing distributed processing. The generator is parameterized in both the MIMO system and hardware datapath elements. Coupled with a Python-based system simulator, the generator can be adapted to implement other baseband processing algorithms. To demonstrate the adaptability, several generator instances with different parameter values are evaluated by FPGA emulation. In addition, a beamspace calibration and channel denoising algorithm are applied to further improve the channel estimation performance. With those algorithms, the error vector magnitude can be reduced by up 9.2%.