60 research outputs found
Massive MIMO is a Reality -- What is Next? Five Promising Research Directions for Antenna Arrays
Massive MIMO (multiple-input multiple-output) is no longer a "wild" or
"promising" concept for future cellular networks - in 2018 it became a reality.
Base stations (BSs) with 64 fully digital transceiver chains were commercially
deployed in several countries, the key ingredients of Massive MIMO have made it
into the 5G standard, the signal processing methods required to achieve
unprecedented spectral efficiency have been developed, and the limitation due
to pilot contamination has been resolved. Even the development of fully digital
Massive MIMO arrays for mmWave frequencies - once viewed prohibitively
complicated and costly - is well underway. In a few years, Massive MIMO with
fully digital transceivers will be a mainstream feature at both sub-6 GHz and
mmWave frequencies. In this paper, we explain how the first chapter of the
Massive MIMO research saga has come to an end, while the story has just begun.
The coming wide-scale deployment of BSs with massive antenna arrays opens the
door to a brand new world where spatial processing capabilities are
omnipresent. In addition to mobile broadband services, the antennas can be used
for other communication applications, such as low-power machine-type or
ultra-reliable communications, as well as non-communication applications such
as radar, sensing and positioning. We outline five new Massive MIMO related
research directions: Extremely large aperture arrays, Holographic Massive MIMO,
Six-dimensional positioning, Large-scale MIMO radar, and Intelligent Massive
MIMO.Comment: 20 pages, 9 figures, submitted to Digital Signal Processin
Understanding the Performance of Learning Precoding Policy with GNN and CNNs
Learning-based precoding has been shown able to be implemented in real-time,
jointly optimized with channel acquisition, and robust to imperfect channels.
Yet previous works rarely explain the design choices and learning performance,
and existing methods either suffer from high training complexity or depend on
problem-specific models. In this paper, we address these issues by analyzing
the properties of precoding policy and inductive biases of neural networks,
noticing that the learning performance can be decomposed into approximation and
estimation errors where the former is related to the smoothness of the policy
and both depend on the inductive biases of neural networks. To this end, we
introduce a graph neural network (GNN) to learn precoding policy and analyze
its connection with the commonly used convolutional neural networks (CNNs). By
taking a sum rate maximization precoding policy as an example, we explain why
the learned precoding policy performs well in the low signal-to-noise ratio
regime, in spatially uncorrelated channels, and when the number of users is
much fewer than the number of antennas, as well as why GNN is with higher
learning efficiency than CNNs. Extensive simulations validate our analyses and
evaluate the generalization ability of the GNN
A Spatial Sigma-Delta Approach to Mitigation of Power Amplifier Distortions in Massive MIMO Downlink
In massive multiple-input multiple-output (MIMO) downlink systems, the
physical implementation of the base stations (BSs) requires the use of cheap
and power-efficient power amplifiers (PAs) to avoid high hardware cost and high
power consumption. However, such PAs usually have limited linear amplification
ranges. Nonlinear distortions arising from operation beyond the linear
amplification ranges can significantly degrade system performance. Existing
approaches to handle the nonlinear distortions, such as digital predistortion
(DPD), typically require accurate knowledge, or acquisition, of the PA transfer
function. In this paper, we present a new concept for mitigation of the PA
distortions. Assuming a uniform linear array (ULA) at the BS, the idea is to
apply a Sigma-Delta () modulator to spatially shape the PA
distortions to the high-angle region. By having the system operating in the
low-angle region, the received signals are less affected by the PA distortions.
To demonstrate the potential of this spatial approach, we study
the application of our approach to the multi-user MIMO-orthogonal frequency
division modulation (OFDM) downlink scenario. A symbol-level precoding (SLP)
scheme and a zero-forcing (ZF) precoding scheme, with the new design
requirement by the spatial approach being taken into account,
are developed. Numerical simulations are performed to show the effectiveness of
the developed precoding schemes
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