631 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
Blind Beamforming for Intelligent Reflecting Surface in Fading Channels without CSI
This paper discusses how to optimize the phase shifts of intelligent
reflecting surface (IRS) to combat channel fading without any channel state
information (CSI), namely blind beamforming. Differing from most previous works
based on a two-stage paradigm of first estimating channels and then optimizing
phase shifts, our approach is completely data-driven, only requiring a dataset
of the received signal power at the user terminal. Thus, our method does not
incur extra overhead costs for channel estimation, and does not entail
collaboration from service provider, either. The main idea is to choose phase
shifts at random and use the corresponding conditional sample mean of the
received signal power to extract the main features of the wireless environment.
This blind beamforming approach guarantees an boost of signal-to-noise
ratio (SNR), where is the number of reflective elements (REs) of IRS,
regardless of whether the direct channel is line-of-sight (LoS) or not.
Moreover, blind beamforming is extended to a double-IRS system with provable
performance. Finally, prototype tests show that the proposed blind beamforming
method can be readily incorporated into the existing communication systems in
the real world; simulation tests further show that it works for a variety of
fading channel models.Comment: 14 pages, 14 figure
Statistical CSI-based Beamforming for RIS-Aided Multiuser MISO Systems using Deep Reinforcement Learning
The paper presents a joint beamforming algorithm using statistical channel
state information (S-CSI) for reconfigurable intelligent surfaces (RIS) for
multiuser MISO wireless communications. We used S-CSI, which is a long-term
average of the cascaded channel as opposed to instantaneous CSI utilized in
most existing works. Through this method, the overhead of channel estimation is
dramatically reduced. We propose a proximal policy optimization (PPO) algorithm
which is a well-known actor-critic based reinforcement learning (RL) algorithm
to solve the optimization problem. To test the efficacy of this algorithm,
simulation results are presented along with evaluations of key system
parameters, including the Rician factor and RIS location, on the achievable sum
rate of the users
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