[Paper Review] Robust Task-Specific Beamforming with Low-Resolution ADCs for Power-Efficient Hybrid MIMO Receivers

Eyyup Tasci, Timur Zirtiloglu+4 more|arXiv (Cornell University)|Nov 30, 2022

Analog and Mixed-Signal Circuit Design4 citations

TL;DR

This paper proposes a power-efficient hybrid MIMO receiver using low-resolution ADCs and task-specific beamforming to reduce hardware complexity and power consumption. By jointly optimizing analog and digital processing with robustness to hardware impairments and channel estimation errors, the design achieves near-full-digital performance with over 58% power reduction compared to conventional systems.

ABSTRACT

Multiple-input multiple-output (MIMO) systems exploit spatial diversity to facilitate multi-user communications with high spectral efficiency by beamforming. As MIMO systems utilize multiple antennas and radio frequency (RF) chains, they are typically costly to implement and consume high power. A common method to reduce the cost of MIMO receivers is utilizing less RF chains than antennas by employing hybrid analog/digital beamforming (HBF). However, the added analog circuitry involves active components whose consumed power may surpass that saved in RF chain reduction. An additional method to realize power-efficient MIMO systems is to use low-resolution analog-to-digital converters (ADCs), which typically compromises signal recovery accuracy. In this work, we propose a power-efficient hybrid MIMO receiver with low-quantization rate ADCs, by jointly optimizing the analog and digital processing in a hardware-oriented manner using task-specific quantization techniques. To mitigate power consumption on the analog front-end, we utilize efficient analog hardware architecture comprised of sparse low-resolution vector modulators, while accounting for their properties in design to maintain recovery accuracy and mitigate interferers in congested environments. To account for common mismatches induced by non-ideal hardware and inaccurate channel state information, we propose a robust mismatch aware design. Supported by numerical simulations and power analysis, our power-efficient MIMO receiver achieves comparable signal recovery performance to power-hungry fully-digital MIMO receivers using high-resolution ADCs. Furthermore, our receiver outperforms the task-agnostic HBF receivers with low-rate ADCs in recovery accuracy at lower power and successfully copes with hardware mismatches.

Motivation & Objective

• Address the high power consumption of fully-digital MIMO receivers due to numerous RF chains and high-resolution ADCs.
• Overcome the performance degradation caused by low-resolution ADCs in hybrid MIMO architectures.
• Design a hardware-aware beamforming framework that accounts for non-ideal analog components, such as sparse vector modulators and gain/phase mismatches.
• Improve signal recovery accuracy in congested environments with spatial interferers using task-specific quantization.
• Achieve robustness against angle-of-arrival (AoA) mismatches and hardware impairments while minimizing power consumption.

Proposed method

• Propose a joint analog and digital beamforming design that optimizes the analog combiner (A) and digital combiner (B) for task-specific signal recovery.
• Integrate sparse low-resolution vector modulators (VMs) to reduce analog power consumption, with sparsity controlled via a coefficient γSP.
• Model quantization noise using a dithered ADC model with uncorrelated, zero-mean noise of variance Δp²/6, where Δp = 2γ/b.
• Formulate the mean squared error (MSE) as a function of channel covariance matrices, combining signal and noise terms with a modified noise power term involving κ and b.
• Introduce a robust optimization framework that accounts for AoA mismatches and gain/phase errors in the analog combiner.
• Use Walden’s Figure of Merit (FoM) and component-level power models to estimate total power consumption, including LNA, mixer, BB amp, and ADC.

Experimental results

Research questions

• RQ1 Can task-specific beamforming with low-resolution ADCs achieve performance comparable to high-resolution fully-digital MIMO receivers while reducing power consumption?
• RQ2 How does the use of sparse, low-resolution vector modulators impact the power efficiency and signal recovery accuracy of hybrid MIMO receivers?
• RQ3 To what extent can robustness to AoA estimation errors and hardware impairments be achieved in low-bit hybrid MIMO systems?
• RQ4 What is the trade-off between quantization resolution, analog hardware complexity, and system power consumption in task-specific MIMO receivers?
• RQ5 Can a joint analog-digital optimization framework outperform task-agnostic hybrid MIMO designs in both accuracy and power efficiency?

Key findings

• The proposed task-specific hybrid MIMO receiver achieves comparable signal recovery performance (MSE) to fully-digital MIMO systems using high-resolution ADCs.
• The system reduces total power consumption by over 58% compared to conventional fully-digital MIMO receivers (520 mW vs. 172 mW for 8×8 systems).
• The receiver outperforms task-agnostic HBF receivers with low-rate ADCs in both MSE performance and spatial interferer suppression.
• Robustness to AoA mismatches and hardware impairments is achieved through a mismatch-aware joint optimization algorithm, maintaining low error rates.
• Power savings are achieved via 25% sparse vector modulators (4-bit), 4-bit ADCs, and low-power LNA/VMs, with ADC power reduced from 10 mW (10-bit) to 0.5 mW (4-bit).
• Theoretical analysis confirms that quantization noise is uncorrelated with signal and observation, enabling accurate MSE modeling with a modified noise variance term involving κ and b.