On the Uplink Achievable Rate of Massive MIMO System with Low-Resolution ADC and RF Impairments

Abstract

This paper considers channel estimation and uplink achievable rate of the coarsely quantized massive multiple-input multiple-output (MIMO) system with radio frequency (RF) impairments. We utilize additive quantization noise model (AQNM) and extended error vector magnitude (EEVM) model to analyze the impacts of low-resolution analog-to-digital converters (ADCs) and RF impairments respectively. We show that hardware impairments cause a nonzero floor on the channel estimation error, which contraries to the conventional case with ideal hardware. The maximal-ratio combining (MRC) technique is then used at the receiver, and an approximate tractable expression for the uplink achievable rate is derived. The simulation results illustrate the appreciable compensations between ADCs' resolution and RF impairments. The proposed studies support the feasibility of equipping economical coarse ADCs and economical imperfect RF components in practical massive MIMO systems.

Gridless Angular Domain Channel Estimation for mmWave Massive MIMO System with One-Bit Quantization via Approximate Message Passing

“…For the case of 1-bit quantization, the proposed method outperforms all other algorithms in distinguishing very close DoAs because all the DoAs are decoupled and estimated separately. Additionally, a nonzero estimation error floor arises due to 1-bit quantization and cannot be eliminated by increasing SNR, as also discussed in [15]. The CRBs of θ are provided as benchmarks, which are derived in [6].…”

Fast Beam Training Architecture for Hybrid mmWave Transceivers

“…The low-cost DBF module and the hybrid module will not work at the same time slot, hence, the power consumption will not be doubled. We adopt the EEVM model to capture the combined impact of all kinds of RF impairments [18], [19]. For illustration simplicity, let k denote the k-th RF chain of the receiver, χ(k) = κ(k)e jϕ(k) denotes the attenuation and phase shift effects on the k-th RF chain with |χ(k)| 1, and n RF (k) is the additive Gaussian noise.…”

“…Since the distance between the transmission and receiver antennas is quite close, the effective signal-to-noise ratio (SNR) can be high enough. Therefore, it is reasonable to assume that the Gaussian thermal noise is negligible and the channel between different antennas is the same, denoted as h in (18).…”

“…Several aspects of quantized MIMO systems have been studied, such as capacity analysis [13], energy efficiency analysis [14], data detection [15], and channel estimation [16]. However, only very few works in the literature have investigated hardware imperfections in the DBF or hybrid architectures [17], [18], including phase noise, power amplifier nonlinearities, and Inphase and Quadrature-phase (IQ) imbalance [19], [20], which can seriously undermine the system performance especially in mmWave frequencies. The work in [17] considered the aggregate impact of hardware impairments and modeled it as additive Gaussian noise.…”

“…The work in [17] considered the aggregate impact of hardware impairments and modeled it as additive Gaussian noise. The study in [18] characterized the superposition of different RF impairments by a more accurate extended error vector magnitude (EEVM) model. Capitalizing on [18], we will take low-resolution phase shifters, low-bit ADCs, and radio frequency (RF) impairments into consideration.…”