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arXiv · 2210.10193

$\lambda$-MIMO: Massive MIMO via Modulo Sampling

Ziang Liu·Ayush Bhandari·Bruno Clerckx
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Abstract

Massive multiple-input multiple-output (M-MIMO) architecture is the workhorse of modern communication systems. Currently, two fundamental bottlenecks, namely, power consumption and receiver saturation, limit the full potential achievement of this technology. These bottlenecks are intricately linked with the analog-to-digital converter (ADC) used in each radio frequency (RF) chain. The power consumption in MIMO systems grows exponentially with the ADC's bit budget while ADC saturation causes permanent loss of information. This motivates the need for a solution that can simultaneously tackle the above-mentioned bottlenecks while offering advantages over existing alternatives such as low-resolution ADCs. Taking a radically different approach to this problem, we propose $\lambda$-MIMO architecture which uses modulo ADCs ($M_\lambda$-ADC) instead of a conventional ADC. Our work is inspired by the Unlimited Sampling Framework. $M_\lambda$-ADC in the RF chain folds high dynamic range signals into low dynamic range modulo samples, thus alleviating the ADC saturation problem. At the same time, digitization of modulo signal results in high resolution quantization. In the novel $\lambda$-MIMO context, we discuss baseband signal reconstruction, detection and uplink achievable sum-rate performance. The key takeaways of our work include, (a) leveraging higher signal-to-quantization noise ratio (SQNR), (b) detection and average uplink sum-rate performances comparable to a conventional, infinite-resolution ADC when using a $1$-$2$ bit $M_\lambda$-ADC. This enables higher order modulation schemes e.g. $1024$ QAM that seemed previously impossible, (c) superior trade-off between energy efficiency and bit budget, thus resulting in higher power efficiency. Numerical simulations and modulo ADC based hardware experiments corroborate our theory and reinforce the clear benefits of $\lambda$-MIMO approach.

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BibTeXRIS

Ziang Liu, Ayush Bhandari, Bruno Clerckx. 2022-10-18. $\lambda$-MIMO: Massive MIMO via Modulo Sampling. https://arxiv.org/abs/2210.10193

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