Impact of Power Amplifier Nonlinearities in Multi-User Massive MIMO Downlink

Zou, Yuexian; Raeesi, Orod; Antilla, Lauri; Hakkarainen, Aki; Vieira, João; Tufvesson, Fredrik; Cui, Qimei; Valkama, Mikko

doi:10.1109/glocomw.2015.7414011

Cited by 46 publications

(52 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This cannot happen unless K ≥ M . This confirms previous downlink [20], [21], [24], [25] and uplink [26] results, derived with different system models. The equivalent model of the receiver distortion derived in this section is summarized in Fig.…”

Section: Lemma 1 Consider the Jointly Circular-symmetric Complexsupporting

confidence: 91%

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

Björnson

Sanguinetti

Hoydis

2019

IEEE Trans. Commun.

View full text Add to dashboard Cite

This paper analyzes how the distortion created by hardware impairments in a multiple-antenna base station affects the uplink spectral efficiency (SE), with focus on Massive MIMO. This distortion is correlated across the antennas, but has been often approximated as uncorrelated to facilitate (tractable) SE analysis. To determine when this approximation is accurate, basic properties of distortion correlation are first uncovered. Then, we separately analyze the distortion correlation caused by thirdorder non-linearities and by quantization. Finally, we study the SE numerically and show that the distortion correlation can be safely neglected in Massive MIMO when there are sufficiently many users. Under i.i.d. Rayleigh fading and equal signal-tonoise ratios (SNRs), this occurs for more than five transmitting users. Other channel models and SNR variations have only minor impact on the accuracy. We also demonstrate the importance of taking the distortion characteristics into account in the receive combining.Index Terms-Massive MIMO, uplink spectral efficiency, hardware impairments, hardware distortion correlation, optimal receive combining.

show abstract

Section: Lemma 1 Consider the Jointly Circular-symmetric Complexsupporting

confidence: 91%

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

Björnson

Sanguinetti

Hoydis

2019

IEEE Trans. Commun.

View full text Add to dashboard Cite

show abstract

“…Hence the focus now is on the terms (5) and (6), and on the question whether they add up constructively or not in particular spatial directions. To proceed with the analysis, we consider a setup with two antennas and a receiver located in the far field of the array.…”

Section: Two-tone Waveform Through a Third-order Non-linearitymentioning

confidence: 99%

Out-of-Band Radiation From Antenna Arrays Clarified

Larsson

Perre

2018

IEEE Wireless Commun. Lett.

View full text Add to dashboard Cite

Abstract-Non-linearities in radio-frequency (RF) transceiver hardware, particularly in power amplifiers, cause distortion inband and out-of-band. Contrary to claims made in recent literature, in a multiple-antenna system this distortion is correlated across the antennas in the array. A significant implication of this fact is that out-of-band emissions caused by non-linearities are beamformed, in some cases into the same direction as the useful signal.

show abstract

“…We have expressed the received signal at the BS in the form y = C yς ς + µ and derived the elements of effective channel matrix C yς . In the following sections, we will analyze the SE of distortion-aware receivers using the derived effective channels, and then use (20) for channel estimation.…”

Section: Effective Channels For Third-order Non-linearitiesmentioning

confidence: 99%

“…We consider LMMSE estimation of the effective channel matrix C yς whose expression is given in (20) for the data signals with the 90 • circular shift property. The LMMSE estimate of the (m, k)th element of C yς given y p m is given by…”

Section: B Distortion-aware Lmmse Estimatormentioning

confidence: 99%

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

Demir

Björnson

2020

IEEE Open J. Commun. Soc.

View full text Add to dashboard Cite

This paper considers the joint impact of non-linear hardware impairments at the base station (BS) and user equipments (UEs) on the uplink performance of single-cell massive MIMO (multiple-input multiple-output) in practical Rician fading environments. First, Bussgang decomposition-based effective channels and distortion characteristics are analytically derived and the spectral efficiency (SE) achieved by several receivers are explored for third-order non-linearities. Next, two deep feedforward neural networks are designed and trained to estimate the effective channels and the distortion variance at each BS antenna, which are used in signal detection. We compare the performance of the proposed methods with state-of-the-art distortion-aware and -unaware Bayesian linear minimum mean-squared error (LMMSE) estimators. The proposed deep learning approach improves the estimation quality by exploiting impairment characteristics, while LMMSE methods treat distortion as noise. Using the data generated by the derived effective channels for general order of non-linearities at both the BS and UEs, it is shown that the deep learning-based estimator provides better estimates of the effective channels also for non-linearities more than order three.

show abstract

Impact of Power Amplifier Nonlinearities in Multi-User Massive MIMO Downlink

Cited by 46 publications

References 13 publications

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

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

Out-of-Band Radiation From Antenna Arrays Clarified

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

Contact Info

Product

Resources

About