Enabling low-power massive MIMO with ternary ADCs for AIoT sensing

TL;DR

This paper proposes using ternary ADCs in massive MIMO systems to enable low-power AIoT sensing, demonstrating that joint-pilot-and-data schemes can effectively mitigate quantization effects and improve channel estimation accuracy.

Contribution

It introduces a cost-effective T-ADC approach combined with a joint-pilot-and-data scheme for channel sensing in massive MIMO AIoT systems, with new estimators for different channel types.

Findings

JPD scheme mitigates quantization effects without extra pilot overhead

Proposed EM and variational inference EM estimators improve channel estimation accuracy

Simulations confirm the feasibility of T-ADCs for low-power AIoT sensing

Abstract

The proliferation of networked devices and the surging demand for ubiquitous intelligence have given rise to the artificial intelligence of things (AIoT). However, the utilization of high-resolution analog-to-digital converters (ADCs) and numerous radio frequency chains significantly raises power consumption. This paper explores a cost-effective solution using ternary ADCs (T-ADCs) in massive multiple-input-multiple-output (MIMO) systems for low-power AIoT and specifically addresses channel sensing challenges. The channel is first estimated through a pilot-aided scheme and refined using a joint-pilot-and-data (JPD) approach. To assess the performance limits of this two-threshold ADC system, the analysis includes its hardware-ideal counterpart, the parallel one-bit ADCs (PO-ADCs) and a realistic scenario where noise variance is unknown at the receiver is considered. Analytical findings indicate that the JPD scheme effectively mitigates performance degradation in channel estimation due to coarse quantization effects under mild conditions, without necessitating additional pilot overhead. For deterministic and random channels, we propose modified expectation maximization (EM) and variational inference EM estimators, respectively. Extensive simulations validate the theoretical results and demonstrate the effectiveness of the proposed estimators in terms of mean square error and symbol error rate, which showcases the feasibility of implementing T-ADCs and the associated JPD scheme for greener AIoT smart sensing.