An Adaptable and Scalable Generator of Distributed Massive MIMO Baseband Processing Systems

Abstract

AbstractThis paper presents an algorithm-adaptable, scalable, and platform-portable generator for massive multiple-input multiple-output (MIMO) baseband processing systems. This generator is written in Chisel hardware construction language, and produces instances that implement distributed massive MIMO base station (BS) processing, including channel estimation and beamforming. The generator can be reused for different MIMO systems and hardware datapath designs by changing the parameters. The generator is paired with a Python-based system simulator, which incorporated together can emulate a system testing various baseband signal processing algorithms. The field programmable gate array (FPGA) emulation is performed with generated instances using various parameter values. To demonstrate the algorithmic adaptability, a Golay-sequence-based channel estimation method, a beamspace calibration method, and a channel denoising algorithm are evaluated across a range of channel models. The performance of the generator, necessity of the algorithmic adaptability, and ease of hardware generation are evaluated and discussed. The emulated register-transfer level (RTL) implementation with different system parameters shows that with beamspace methods, the demodulation error vector magnitude is improved by up to 29.8%.