A Resolution-Adaptive 8 mm2 9.98 Gb/s 39.7 pJ/b 32-Antenna All-Digital Spatial Equalizer for mmWave Massive MU-MIMO in 65nm CMOS

All-digital millimeter-wave (mmWave) massive multi-user multiple-input multiple-output (MU-MIMO) receivers enable extreme data rates but require high power consumption. In order to reduce power consumption, this paper presents the first resolution-adaptive all-digital receiver ASIC that is able to adjust the resolution of the data-converters and baseband-processing engine to the instantaneous communication scenario. The scalable 32-antenna, 65 nm CMOS receiver occupies a total area of 8 mm2 and integrates analog-to-digital converters (ADCs) with programmable gain and resolution, beamspace channel estimation, and a resolution-adaptive processing-in-memory spatial equalizer. With 6-bit ADC samples and a 4-bit spatial equalizer, our ASIC achieves a throughput of 9.98 Gb/s while being at least 2× more energy-efficient than state-of-the-art designs.

[1]  Lars Thiele,et al.  QuaDRiGa: A 3-D Multi-Cell Channel Model With Time Evolution for Enabling Virtual Field Trials , 2014, IEEE Transactions on Antennas and Propagation.

[2]  Erik G. Larsson,et al.  Massive MIMO for next generation wireless systems , 2013, IEEE Communications Magazine.

[3]  Christoph Studer,et al.  Beamspace Channel Estimation for Massive MIMO mmWave Systems: Algorithm and VLSI Design , 2019, IEEE Transactions on Circuits and Systems I: Regular Papers.

[4]  Theodore S. Rappaport,et al.  Millimeter Wave Wireless Communications , 2014 .

[5]  Sridhar Ramesh,et al.  Performance, Power, and Area Design Trade-Offs in Millimeter-Wave Transmitter Beamforming Architectures , 2018, IEEE Circuits and Systems Magazine.

[6]  Robert W. Heath,et al.  Channel Estimation and Hybrid Precoding for Millimeter Wave Cellular Systems , 2014, IEEE Journal of Selected Topics in Signal Processing.

[7]  Wei Tang,et al.  A 0.58mm2 2.76Gb/s 79.8pJ/b 256-QAM massive MIMO message-passing detector , 2016, 2016 IEEE Symposium on VLSI Circuits (VLSI-Circuits).

[8]  Tom Goldstein,et al.  Finite-Alphabet MMSE Equalization for All-Digital Massive MU-MIMO mmWave Communication , 2020, IEEE Journal on Selected Areas in Communications.

[9]  Shouyi Yin,et al.  Energy- and Area-Efficient Recursive-Conjugate-Gradient-Based MMSE Detector for Massive MIMO Systems , 2020, IEEE Transactions on Signal Processing.

[10]  Christoph Studer,et al.  SMUL-FFT: A Streaming Multiplierless Fast Fourier Transform , 2021, IEEE Transactions on Circuits and Systems II: Express Briefs.

[11]  Sundeep Rangan,et al.  Power Consumption Analysis for Mobile MmWave and Sub-THz Receivers , 2020, 2020 2nd 6G Wireless Summit (6G SUMMIT).

[12]  Oscar Castaneda,et al.  A 354 Mb/s 0.37 mm2 151 mW 32-User 256-QAM Near-MAP Soft-Input Soft-Output Massive MU-MIMO Data Detector in 28nm CMOS , 2019, IEEE Solid-State Circuits Letters.

[13]  Josef A. Nossek,et al.  Achievable Rate and Energy Efficiency of Hybrid and Digital Beamforming Receivers With Low Resolution ADC , 2016, IEEE Journal on Selected Areas in Communications.

[14]  Tom Goldstein,et al.  High-Bandwidth Spatial Equalization for mmWave Massive MU-MIMO With Processing-in-Memory , 2020, IEEE Transactions on Circuits and Systems II: Express Briefs.

[15]  Sven Jacobsson,et al.  Throughput Analysis of Massive MIMO Uplink With Low-Resolution ADCs , 2016, IEEE Transactions on Wireless Communications.

[16]  Christoph Studer,et al.  PPAC: A Versatile In-Memory Accelerator for Matrix-Vector-Product-Like Operations , 2019, 2019 IEEE 30th International Conference on Application-specific Systems, Architectures and Processors (ASAP).

[17]  Wei Tang,et al.  A 1.8Gb/s 70.6pJ/b 128×16 link-adaptive near-optimal massive MIMO detector in 28nm UTBB-FDSOI , 2018, 2018 IEEE International Solid - State Circuits Conference - (ISSCC).

[18]  Upamanyu Madhow,et al.  Towards All-digital mmWave Massive MIMO: Designing around Nonlinearities , 2018, 2018 52nd Asilomar Conference on Signals, Systems, and Computers.