3,830 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
Receive Spatial Modulation for Massive MIMO Systems
In this paper, we consider the downlink of a massive
multiple-input-multiple-output (MIMO) single user transmission system operating
in the millimeter wave outdoor narrowband channel environment. We propose a
novel receive spatial modulation architecture aimed to reduce the power
consumption at the user terminal, while attaining a significant throughput. The
energy consumption reduction is obtained through the use of analog devices
(amplitude detector), which reduces the number of radio frequency chains and
analog-to-digital-converters (ADCs). The base station transmits spatial and
modulation symbols per channel use. We show that the optimal spatial symbol
detector is a threshold detector that can be implemented by using one bit ADC.
We derive closed form expressions for the detection threshold at different
signal-to-noise-ratio (SNR) regions showing that a simple threshold can be
obtained at high SNR and its performance approaches the exact threshold. We
derive expressions for the average bit error probability in the presence and
absence of the threshold estimation error showing that a small number of pilot
symbols is needed. A performance comparison is done between the proposed system
and fully digital MIMO showing that a suitable constellation selection can
reduce the performance gap
Deep Learning for Uplink CSI-based Downlink Precoding in FDD massive MIMO Evaluated on Indoor Measurements
When operating massive multiple-input multiple-output (MIMO) systems with
uplink (UL) and downlink (DL) channels at different frequencies (frequency
division duplex (FDD) operation), acquisition of channel state information
(CSI) for downlink precoding is a major challenge. Since, barring transceiver
impairments, both UL and DL CSI are determined by the physical environment
surrounding transmitter and receiver, it stands to reason that, for a static
environment, a mapping from UL CSI to DL CSI may exist. First, we propose to
use various neural network (NN)-based approaches that learn this mapping and
provide baselines using classical signal processing. Second, we introduce a
scheme to evaluate the performance and quality of generalization of all
approaches, distinguishing between known and previously unseen physical
locations. Third, we evaluate all approaches on a real-world indoor dataset
collected with a 32-antenna channel sounder
Millimeter Wave Cellular Networks: A MAC Layer Perspective
The millimeter wave (mmWave) frequency band is seen as a key enabler of
multi-gigabit wireless access in future cellular networks. In order to overcome
the propagation challenges, mmWave systems use a large number of antenna
elements both at the base station and at the user equipment, which lead to high
directivity gains, fully-directional communications, and possible noise-limited
operations. The fundamental differences between mmWave networks and traditional
ones challenge the classical design constraints, objectives, and available
degrees of freedom. This paper addresses the implications that highly
directional communication has on the design of an efficient medium access
control (MAC) layer. The paper discusses key MAC layer issues, such as
synchronization, random access, handover, channelization, interference
management, scheduling, and association. The paper provides an integrated view
on MAC layer issues for cellular networks, identifies new challenges and
tradeoffs, and provides novel insights and solution approaches.Comment: 21 pages, 9 figures, 2 tables, to appear in IEEE Transactions on
Communication
Energy-Efficient Downlink Power Control in mmWave Cell-Free and User-Centric Massive MIMO
This paper considers cell-free and user-centric approaches for coverage
improvement in wireless cellular systems operating at millimeter wave
frequencies, and proposes downlink power control algorithms aimed at maximizing
the global energy efficiency. To tackle the non-convexity of the problems, an
interaction between sequential and alternating optimization is considered. The
use of hybrid analog/digital beamformers is also taken into account. The
numerical results show the benefits obtained from the power control algorithm,
as well as that the user-centric approach generally outperforms the cell-free
one.Comment: 4 pages; to be presented at the IEEE 5G Worls Forum Conference, Santa
Clara, July 2018. arXiv admin note: text overlap with arXiv:1710.0781
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