Design and Demonstration of a Scalable Massive MIMO Uplink at E-Band

LaCaille, Greg; Dunn, James; Puglielli, Antonio; Iotti, Lorenzo; Ramakrishnan, Sameet; Calderin, Lucas; Lin, Zhenghan; Naviasky, Emily; Nikolić, Borivoje; Niknejad, Ali M.; Alon, Elad

doi:10.1109/iccworkshops49005.2020.9145323

Cited by 5 publications

(1 citation statement)

References 8 publications

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“…To perform energy-efficiency signal processing, a two-stage beamforming algorithm has been proposed [9]. The first stage is the frequency-flat MRC beamformer to maximize the output signal-to-noise-ratio (SNR).…”

Section: A Two-stage Beamformingmentioning

confidence: 99%

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

Dai

Liew

Rasekh

et al. 2021

2021 IEEE Workshop on Signal Processing Systems (SiPS)

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This paper describes a scalable, highly portable, and energy-efficient generator for massive multiple-input multipleoutput (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%.

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Section: A Two-stage Beamformingmentioning

confidence: 99%

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

Dai

Liew

Rasekh

et al. 2021

2021 IEEE Workshop on Signal Processing Systems (SiPS)

Self Cite

View full text Add to dashboard Cite

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

Dai

Rasekh

Mirfarshbafan

et al. 2022

J Sign Process Syst

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This 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%.

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