Impact of Spatial Filtering on Distortion From Low-Noise Amplifiers in Massive MIMO Base Stations

Abstract

In massive MIMO base stations, power consumption and cost of the low-noise amplifiers (LNAs) can be substantial because of the many antennas. We investigate the feasibility of inexpensive, power efficient LNAs, which inherently are less linear. A polynomial model is used to characterize the nonlinear LNAs and to derive the second-order statistics and spatial correlation of the distortion. We show that, with spatial matched filtering (maximum-ratio combining) at the receiver, some distortion terms combine coherently, and that the SINR of the symbol estimates therefore is limited by the linearity of the LNAs. Furthermore, it is studied how the power from a blocker in the adjacent frequency band leaks into the main band and creates distortion. The distortion term that scales cubically with the power received from the blocker has a spatial correlation that can be filtered out by spatial processing and only the coherent term that scales quadratically with the power remains. When the blocker is in free-space line-of-sight and the LNAs are identical, this quadratic term has the same spatial direction as the desired signal, and hence cannot be removed by linear receiver processing.

Massive MU-MIMO-OFDM Uplink with Hardware Impairments: Modeling and Analysis

“…The nonlinearities f lna (·) and f adc (·) in the behavioral model (14) renders a theoretical performance analysis challenging. We now derive an approximate, yet accurate, aggregate statistical model for the distortion caused by the hardware impairments reviewed in Sec.…”

“…In what follows, we use Bussgang's theorem to linearize the input-output relation of each nonlinear hardware component separately. We then combine the resulting linearized expressions to obtain a simple, yet accurate, approximation for the ADC output r[n] in (14) as a function of the LNA input in (7).…”

Channel Estimation in Massive MIMO Under Hardware Non-Linearities: Bayesian Methods Versus Deep Learning

“…The authors are with the Department of Electrical Engineering (ISY), Linkping University, 581 83 Linkping, Sweden (e-mail: ozlem.tugfe.demir@liu.se, emil.bjornson@liu.se) There are several works which model and analyze the impact of hardware non-linearities on massive MIMO using behavioral modeling [11], [12], [14], [17]- [21], [24]. Recently, [24] proposed several distortion-aware receivers for uplink signal detection in massive MIMO.…”

Hardware Distortion Correlation Has Negligible Impact on UL Massive MIMO Spectral Efficiency

“…The error-vector magnitude analysis in [22] shows that an approximate model where the BS distortion is uncorrelated across the antennas gives accurate results, while [23] provides a similar result when considering a model that also includes mutual coupling and noise at the transmitter side. Nevertheless, [24] recently claimed that the model with uncorrelated BS distortion "do not necessarily reflect the true behavior of the transmitted distorted signals", [25] stated that the model is "physically inaccurate", and [26] says that it "can yield incorrect conclusions in some important cases of interest when the number of antennas is large". These papers focus on the power spectral density and out-of-band interference, assuming setups with non-linearities at the BS, while the UEs are equipped with ideal hardware.…”

“…In contrast, the works [8], [18] promoting the uncorrelated BS distortion model also consider hardware distortion at the UEs and claim that it is the dominant factor in Massive MIMO. Hence, the question is if the statements from [24]- [26] are applicable when quantifying the uplink SE in typical Massive MIMO scenarios with hardware impairments at both the UEs and BSs. In other words:…”