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

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