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On Performance Loss of DOA Measurement Using Massive MIMO Receiver with Mixed-ADCs

arXiv 2104.02447 ↗First posted 6 Apr 2021cs.IT eess.SP math.ITcited by 1

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Abstract (arXiv)

High hardware cost and high power consumption of massive multiple-input and multiple output (MIMO) are two challenges for the future wireless communications including beyond fifth generation (B5G) and sixth generation (6G). Adopting the low-resolution analog-to-digital converter (ADC) is viewed as a promising solution. Additionally, the direction of arrival (DOA) estimation is an indispensable technology for beam alignment and tracking in massive MIMO systems. Thus, in this paper, the performance of DOA estimation with mixed-ADC structure is firstly investigated. The Cramer-Rao lower bound (CRLB) for this architecture is derived based on the additive quantization noise model. Eventually, a performance loss factor and the associated energy efficiency factor is defined for analysis in detail. Simulation results show that the mixed-ADC architecture can strike a good balance among performance loss, circuit cost and energy efficiency. More importantly, just a few bits (up to 4 bits) of low-resolution ADCs can achieve a satisfactory performance for DOA measurement.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). First posted on arXiv 6 Apr 2021.

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  1. 2021DOA Estimation for Hybrid Massive MIMO Systems using Mixed-ADCs: Performance Loss and Energy Efficiency
  2. 2024CRB Analysis for Mixed-ADC Based DOA Estimation
  3. 2024Hybrid Receiver Design for Massive MIMO-OFDM with Low-Resolution ADCs and Oversampling ↗
  4. 2024Joint Beamforming Design and Bit Allocation in Massive MIMO with Resolution-Adaptive ADCs ↗
  5. 2021An Optimal Low-Complexity Energy-Efficient ADC Bit Allocation for Massive MIMO ↗
  6. 2024Digital and Hybrid Precoding Designs in Massive MIMO with Low-Resolution ADCs ↗
  7. 2022MIMO Systems with One-bit ADCs: Capacity Gains using Nonlinear Analog Operations ↗
  8. 2022Non-Linear Analog Processing in MIMO Systems with Coarse Quantization ↗