A 1.2 mm<sup>2</sup> 416 mW 1.44 Mmat/s 64×16 Matrix Preprocessing ASIC for Massive MIMO in 22FDX

Massive multiuser (MU) multiple-input multiple-output (MIMO) enables concurrent transmission of multiple users to a multi-antenna basestation (BS). To detect the users’ data using linear equalization, the BS must perform preprocessing, which requires, among other tasks, the inversion of a matrix whose dimension equals the number of user data streams. Explicit inversion of large matrices is notoriously difficult to implement due to high complexity, stringent data dependencies that lead to high latency, and high numerical precision requirements. We propose a novel preprocessing architecture based on the block-LDL matrix factorization, which improves parallelism and, hence, reduces latency. We demonstrate the effectiveness of our architecture through (i) massive MU-MIMO system simulations with mmWave channel vectors and (ii) measurements of a 22FDX ASIC, which is, to our knowledge, the first fabricated preprocessing engine for massive MU-MIMO with 64 BS antennas and 16 single-antenna users. Our ASIC reaches a clock frequency of 870 MHz while consuming 416 mW. At its peak throughput, the ASIC preprocesses 1.44 M $64 \times 16$ matrices per second at a latency of only $0.7 \mu \mathrm{~s}$.

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Published
2024-09-09
DOI
https://doi.org/10.1109/esserc62670.2024.10719436
Primary Topic
Photonic and Optical Devices
Type
article
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article

A 1.2 mm2 416 mW 1.44 Mmat/s 64×16 Matrix Preprocessing ASIC for Massive MIMO in 22FDX

Christoph Studer, Darja Nonaca
Photonic and Optical Devices
article

A 1.2 mm2 416 mW 1.44 Mmat/s 64×16 Matrix Preprocessing ASIC for Massive MIMO in 22FDX

Christoph Studer, Darja Nonaca
article en

Abstract

Massive multiuser (MU) multiple-input multiple-output (MIMO) enables concurrent transmission of multiple users to a multi-antenna basestation (BS). To detect the users’ data using linear equalization, the BS must perform preprocessing, which requires, among other tasks, the inversion of a matrix whose dimension equals the number of user data streams. Explicit inversion of large matrices is notoriously difficult to implement due to high complexity, stringent data dependencies that lead to high latency, and high numerical precision requirements. We propose a novel preprocessing architecture based on the block-LDL matrix factorization, which improves parallelism and, hence, reduces latency. We demonstrate the effectiveness of our architecture through (i) massive MU-MIMO system simulations with mmWave channel vectors and (ii) measurements of a 22FDX ASIC, which is, to our knowledge, the first fabricated preprocessing engine for massive MU-MIMO with 64 BS antennas and 16 single-antenna users. Our ASIC reaches a clock frequency of 870 MHz while consuming 416 mW. At its peak throughput, the ASIC preprocesses 1.44 M $64 \times 16$ matrices per second at a latency of only $0.7 \mu \mathrm{~s}$.

ETH Zurich (CH)
Affordable and clean energy
Openalex Percentile: Top 84%
Photonic and Optical Devices
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A 1.2 mm<sup>2</sup> 416 mW 1.44 Mmat/s 64×16 Matrix Preprocessing ASIC for Massive MIMO in 22FDX — Christoph Studer, Darja Nonaca · (2024) | TGRS Research Map | TGRS