Massive MIMO Systems with Low-Resolution ADCs

Massive multiple-input multiple-output (MIMO) systems leverage arrays of hundreds of antennas to deliver unprecedented spectral efficiency and resilience to fading in modern wireless networks. However, equipping each antenna with high-precision analogue-to-digital converters (ADCs) incurs substantial hardware cost and power consumption, particularly in millimetre-wave and wideband deployments. Employing low-resolution ADCs—often only one to a few bits per sample—offers a compelling route to reduce energy use and complexity, though at the expense of coarse quantisation noise. Recent advances have focused on maintaining reliable channel estimation and data detection under severe quantisation, devising architectures such as mixed-ADC arrays and spatial sigma-delta converters to shape or mitigate quantisation distortion. Algorithmic innovations, ranging from Bussgang-based linear models and compressive sensing to machine-learning-driven detectors, have enabled near-optimal operation in the presence of coarse ADCs. The global drive towards energy-efficient 5G and beyond, the proliferation of Internet-of-Things devices and the push for green communications continue to spur research into balancing hardware constraints with stringent performance demands in massive MIMO systems.

Research from Nature Portfolio

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Research from all publishers

Comprehensive surveys of low-resolution ADCs in wireless communication have synthesised the state of the art, detailing system-level performance trade-offs, receiver-side signal processing, and application scenarios. These works highlight how coarse quantisation can be integrated with minimal capacity loss by leveraging advanced signal-recovery techniques and adaptive quantiser designs. A spatial sigma-delta ADC architecture has been shown to direct quantisation noise away from intended user directions in massive MIMO arrays, supporting linear minimum mean-square-error channel estimation and enhanced sum-rate performance compared to uniform low-bit quantisers. Analytical frameworks for uplink systems using one-bit ADCs have delivered closed-form expressions for channel estimation error and detection statistics under Rayleigh fading, revealing fundamental signal-to-noise trade-offs and guidelines for selecting pilot lengths and operating SNRs. Collectively, these studies advance practical deployments by quantifying the impact of coarse quantisation on system capacity, energy efficiency and robustness.

Massive MIMO Systems with Low-Resolution ADCs publication trend

The graph below shows the total number of articles in massive mimo systems with low-resolution adcs across all publications each year (not limited to Nature Index journals).

Technical terms

Massive MIMO: A wireless technology employing very large antenna arrays at the transceiver to improve spectral efficiency and link reliability.

Analogue-to-Digital Converter (ADC): A device that samples an analogue signal and converts it into a digital representation, where resolution is measured in bits.

Quantisation: The process of mapping a continuous range of signal amplitudes to a finite set of levels, introducing quantisation noise when levels are coarse.

Bussgang decomposition: A mathematical technique to linearise a non-linear quantiser by representing its output as a scaled input plus uncorrelated distortion noise.

Channel estimation: The procedure by which a receiver infers the characteristics of a communication channel to enable coherent detection and decoding.

References

1. Channel Estimation for mmWave Massive MIMO Systems With Mixed-ADC Architecture. IEEE Open Journal of the Communications Society (2023).
2. Low-Resolution ADCs for Wireless Communication: A Comprehensive Survey. IEEE Access (2019).
3. Massive MIMO Channel Estimation With Low-Resolution Spatial Sigma-Delta ADCs. IEEE Access (2021).
4. Machine Learning Detectors for MU-MIMO Systems With One-Bit ADCs. IEEE Access (2020).
5. Channel Estimation and Data Detection Analysis of Massive MIMO With 1-Bit ADCs. IEEE Transactions on Wireless Communications (2021).