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On Low-Resolution ADCs in Practical 5G Millimeter-Wave Massive MIMO Systems

Jiayi Zhang, Lnglong Dai, Xu Li, Ying Liu, Lajos Hanzo

arXiv:1803.07384v1cs.IT

TL;DR

Practical mmWave massive MIMO systems face potentially unaffordable ADC power consumption as antenna and RF-chain counts grow. This paper surveys low-resolution-ADC system performance and associated transceiver techniques, including sparse channel estimation and related physical-layer processing. It reports that such techniques can enhance system performance, while the conclusion also points to applicability at similar THz frequencies.

  • Problem

    The paper addresses the high ADC power consumption of practical mmWave massive MIMO systems, where 256 RF chains and 512 ADCs can require 256 W.

  • Method

    The paper surveys quantized mmWave massive MIMO challenges and physical-layer techniques, including sparse channel estimation and other signal-processing methods.

  • Results

    The discussed physical-layer signal-processing techniques are capable of enhancing the performance of mmWave massive MIMO systems using low-resolution ADCs.

  • Takeaways & Limitations

    The paper identifies low-resolution-ADC mmWave massive MIMO as a practical system-design area and notes that the discussed techniques may also apply to THz massive MIMO systems.

Abstract

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Nowadays, millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems is a favorable candidate for the fifth generation (5G) cellular systems. However, a key challenge is the high power consumption imposed by its numerous radio frequency (RF) chains, which may be mitigated by opting for low-resolution analog-to-digital converters (ADCs), whilst tolerating a moderate performance loss. In this article, we discuss several important issues based on the most recent research on mmWave massive MIMO systems relying on low-resolution ADCs. We discuss the key transceiver design challenges including channel estimation, signal detector, channel information feedback and transmit precoding. Furthermore, we introduce a mixed-ADC architecture as an alternative technique of improving the overall system performance. Finally, the associated challenges and potential implementations of the practical 5G mmWave massive MIMO system {with ADC quantizers} are discussed.

I. INTRODUCTION

MmWave massive MIMO offers high data rates and beamforming gains but faces severe ADC-driven power consumption in practical 5G implementations. Low-resolution ADCs reduce power and hardware cost while introducing nonlinear distortion and new signal-processing challenges.

  • Massive MIMO beamforming compensates for mmWave propagation attenuation through directional transmissions using high-dimensional antenna arrays.
  • MmWave massive MIMO combines large bandwidths and high-dimensional antenna arrays to provide very high-speed cellular data rates.
  • 256 RF chains and 512 high-resolution ADCs can consume 256 W, making practical mmWave massive MIMO potentially unaffordable.
  • Low-resolution 1-3-bit ADCs reduce power consumption and hardware cost, with each RF chain using two such ADCs instead of high-resolution converters.
  • Low-resolution quantization imposes rough nonlinear distortion, rendering high-resolution signal-processing algorithms potentially suboptimal.
  • The paper surveys challenges and research directions spanning performance analysis, sparse channel estimation, signal detection, CSI feedback, transmit precoding, and distortion mitigation.

II. PERFORMANCE CONSIDERATIONS

This section examines performance in quantized mmWave massive MIMO systems, including ADC-resolution effects, signaling distributions, and capacity-oriented modeling. It reports that 3-bit ADCs approach infinite-resolution spectral efficiency, while antenna scaling and signaling design remain important considerations.

  • Realistic low-resolution mmWave massive MIMO capacity is approached by discrete input distributions, whose design requires channel state information at the transmitter.
  • The optimal signaling alphabet remains unknown for the practical mmWave massive MIMO channel.
  • The AQNM characterizes quantization noise and supports a lower capacity bound under Gaussian-distributed signaling alphabets.
  • 3-bit ADC spectral efficiency is close to the idealized infinite-resolution case of b = ∞ in Rician-fading massive MIMO systems.

V. Transmit Precoding

Low-resolution ADCs can reduce spectral and energy efficiency, but large arrays and mixed-ADC designs can preserve performance while lowering hardware demands. The section highlights unresolved capacity, quantizer, signaling, and mmWave-specific design questions.

  • 3-bit ADC spectral efficiency is close to that of high-resolution ADCs, with only modest quantization loss.
  • More antennas can compensate for the spectral-efficiency loss imposed by low-resolution ADCs.
  • Mixed-ADC receivers deploy M0 high-resolution and M1 low-resolution ADCs, achieving little performance loss and compelling overall performance.
  • The exact capacity of quantized massive MIMO remains unknown because input distributions and suitable quantizer thresholds are unresolved.
  • Low-resolution ADCs are particularly suitable at low SNRs, whereas high-resolution ADCs can improve performance in the high-SNR regime.
  • Energy efficiency depends on the tradeoff between ADC resolution, power consumption, and receiver SNR; fewer quantization bits can substantially improve efficiency at low SNRs.

III. CHANNEL ESTIMATION

Channel estimation in quantized mmWave massive MIMO must address nonlinear ADC effects, long training requirements, and sparse channel structure. Existing approaches include EM, GAMP, ML, and joint channel-and-data methods, each with different performance and complexity tradeoffs.

  • EM estimation has high complexity because each iteration requires a matrix inverse and many iterations for convergence.
  • GAMP exploits angular-domain sparsity by converting vector estimation into scalar problems and outperforms EM at low and medium SNRs.
  • The ML estimator uses convex optimization to estimate both channel norm and direction, achieving lower MSE than EM at high SNRs but limited performance at low SNRs.
  • Quantized channel estimation requires acceptable CSI despite excessively long training sequences and substantial pilot overhead.
  • JCD and DDCE use reliably detected payload data as pilots, requiring relatively short training sequences while achieving performance comparable to ideal unquantized estimation.
  • Future estimators should combine mixed-ADC high-resolution measurements, compressed sensing, and temporal or spatial mmWave sparsity to reduce bias and complexity.

IV. SIGNAL DETECTION

Low-resolution ADCs degrade classical receiver performance, motivating detectors tailored to quantization and mmWave structure. Proposed nML and message-passing approaches improve detection, but practical complexity and channel generality remain concerns.

  • MRC and ZF detectors suffer substantial performance degradation at high SNRs with low-resolution ADCs.
  • Message-passing detection offers superior performance to other linear detectors with low computational complexity, but was developed for spatial modulation and omits mmWave CIR sparsity.
  • The nML detector outperforms linear detectors in both MSE and BER for one-bit ADC systems.
  • nML supports arbitrary constellations and remains robust to channel-estimation errors compared with message-passing detection.
  • The nML detector requires around 20-40 iterations to converge, limiting suitability for practical mmWave massive MIMO systems.
  • Future work includes extending ML detection to frequency-selective and sparse mmWave channels and reducing complexity through convex-optimization-based MMSE detection.

V. OTHER KEY ASPECTS OF LOW-RESOLUTION ADCS

Deployment of low-resolution ADC mmWave massive MIMO systems raises additional challenges in channel-information feedback, transmit precoding, and mixed-ADC architectures.

  • The remaining deployment issues concern channel-information feedback, transmit precoding, and mixed-ADC receiver architectures.

A. Channel Feedback

Channel feedback in quantized mmWave massive MIMO must represent phase, angle-of-arrival, and channel norm despite low-resolution ADC constraints. The paper identifies feedback-codebook and joint estimation-feedback designs as important directions.

  • A. Channel Feedback: Low-resolution ADCs inaccurately represent channel phase and norm, while phase-invariant beamforming codebooks are unsuitable.The stated causes are coarse phase representation and sub-optimal ADC threshold setting.
  • A. Channel Feedback: Feedback codebooks should explicitly incorporate quantized mmWave channel phase, allocating bits to angle-of-arrival information and residual phase.The proposed direction retains explicit phase information rather than relying on phase-invariant codebooks.
  • A. Channel Feedback: Feedback delay and channel errors are key considerations in finite-rate feedback systems.These factors constrain the reliability of channel information available at the transmitter.
  • A. Channel Feedback: Sparse-channel impulse-response feedback and joint channel estimation-feedback schemes are identified as promising future work.In joint designs, quantized ADC outputs can determine the codebook index.

B. Transmit Precoding

Transmit precoding for quantized mmWave massive MIMO is scenario-dependent and can improve realistic quantized-MIMO performance, but existing approaches have limited applicability in highly correlated channels.

  • B. Transmit Precoding: Transmit precoding can provide substantial performance improvement in realistic quantized MIMO systems.The paper presents precoding as a way to address quantized-system performance loss.
  • B. Transmit Precoding: Realistic quantized-MIMO precoding quantizes channel-matrix phase information while fixing each matrix element’s amplitude.This design constrains the transmitted channel representation to phase quantization with fixed amplitudes.
  • B. Transmit Precoding: Channel-inversion precoding can approach capacity when the MIMO channel matrix has low condition number and full row-rank.The result is conditional on both matrix properties.
  • B. Transmit Precoding: The channel-inversion assumption has finite applicability in highly correlated mmWave small-cell channels.This limits direct reliance on channel inversion for such channel conditions.
  • B. Transmit Precoding: Efficient transmit-precoding schemes remain needed for mmWave massive MIMO systems with ADC quantizers, especially in multiuser scenarios.The paper frames this as an open development challenge.

C. Mixed-ADC Resolution

Mixed-ADC architectures combine a small number of high-resolution ADCs with low-resolution ADCs to improve channel estimation and detection while reducing quantization-related losses. Their optimal allocation and wideband analysis remain open problems.

  • C. Mixed-ADC Resolution: Using a few high-resolution ADCs is expected to mitigate capacity loss, channel-estimation overhead, and symbol-detection error floors.The architecture assigns high- and low-resolution ADCs to different receiver roles.
  • C. Mixed-ADC Resolution: High-resolution ADC assistance reduces channel-estimation quantization noise and can reduce pilot overhead.It enables accurate estimation of all antenna channels through round-robin connections.
  • C. Mixed-ADC Resolution: A mixed-ADC receiver uses Nh high-resolution ADCs for selected antennas and Nr −Nh low-resolution ADCs for the remaining antennas during detection.During channel estimation, the high-resolution ADCs connect to different antenna groups across time slots.
  • C. Mixed-ADC Resolution: The optimal ADC assignment for specific SNRs remains open because low-resolution ADCs become less beneficial at high SNRs.The paper asks how many high-resolution ADCs should be used at low, medium, and high SNRs for energy efficiency.
  • C. Mixed-ADC Resolution: Performance analysis and signal-detector design for mixed-ADC systems over wideband frequency-selective mmWave fading channels require further research.This is identified as a practical implementation challenge.

VI. CONCLUSIONS

Low-resolution ADCs reduce hardware cost and power consumption in mmWave massive MIMO, but their quantization creates analysis and transceiver-design challenges. The paper surveys these challenges and signal-processing techniques, with relevance extending to similar THz channel characteristics.

  • VI. CONCLUSIONS: Low-resolution ADCs offer reduced hardware cost and power consumption for 5G mmWave massive MIMO systems.These benefits motivate low-resolution architectures despite associated quantization effects.
  • VI. CONCLUSIONS: Direct extension of information-theoretical analysis is unavailable for low-resolution ADCs.This marks a scope boundary for applying conventional analytical results.
  • VI. CONCLUSIONS: The article details realization challenges for mmWave massive MIMO systems relying on low-resolution ADCs and surveys quantized system-model challenges.Its scope covers practical implementation issues rather than a single transceiver component.
  • VI. CONCLUSIONS: The paper discusses physical-layer signal-processing techniques capable of enhancing low-resolution mmWave massive-MIMO performance.The conclusion frames these techniques as performance-oriented approaches within the surveyed system class.
  • VI. CONCLUSIONS: Because THz and mmWave frequencies have similar channel characteristics, the discussed methods are relevant to THz massive MIMO.The stated connection is based on channel-characteristic similarity.
  • VI. CONCLUSIONS: Low-resolution ADCs are also capable of circumventing some practical THz massive-MIMO issues.The conclusion states this broader applicability without specifying a particular issue.
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