779 research outputs found
Performance analysis of RIS-assisted cell-free massive MIMO systems with transceiver hardware impairments
Integrating reconfigurable intelligent surface (RIS) into cell-free massive multiple-input multiple-output (MIMO) is a promising approach to enhance the coverage quality, spectral efficiency (SE), and energy efficiency. In this paper, an RIS-assisted cell-free massive MIMO downlink system suffering from the transceiver hardware impairments (T-HWIs) is investigated. To improve the accuracy of the direct estimation (DE) scheme, a modified ON/OFF estimation (MOE) with moderate pilot overhead is proposed. Relying on the knowledge of imperfect channel state information, we derive closed-form expressions of the lower-bound achievable SE with T-HWIs under both DE and MOE schemes. The closed-form results facilitate the investigation of how RIS improves the downlink SE under various system settings and allow us to explore the trade-off strategies between using more hardware-impaired APs and low-cost RISs in terms of the downlink SE and power consumption. Numerical results validate the theoretical analysis and show that the proposed MOE scheme outperforms the DE scheme in terms of the downlink SE. Moreover, the benefits of introducing RIS into hardware-impaired cell-free massive MIMO systems are also illustrated
Massive MIMO Systems with Non-Ideal Hardware: Energy Efficiency, Estimation, and Capacity Limits
The use of large-scale antenna arrays can bring substantial improvements in
energy and/or spectral efficiency to wireless systems due to the greatly
improved spatial resolution and array gain. Recent works in the field of
massive multiple-input multiple-output (MIMO) show that the user channels
decorrelate when the number of antennas at the base stations (BSs) increases,
thus strong signal gains are achievable with little inter-user interference.
Since these results rely on asymptotics, it is important to investigate whether
the conventional system models are reasonable in this asymptotic regime. This
paper considers a new system model that incorporates general transceiver
hardware impairments at both the BSs (equipped with large antenna arrays) and
the single-antenna user equipments (UEs). As opposed to the conventional case
of ideal hardware, we show that hardware impairments create finite ceilings on
the channel estimation accuracy and on the downlink/uplink capacity of each UE.
Surprisingly, the capacity is mainly limited by the hardware at the UE, while
the impact of impairments in the large-scale arrays vanishes asymptotically and
inter-user interference (in particular, pilot contamination) becomes
negligible. Furthermore, we prove that the huge degrees of freedom offered by
massive MIMO can be used to reduce the transmit power and/or to tolerate larger
hardware impairments, which allows for the use of inexpensive and
energy-efficient antenna elements.Comment: To appear in IEEE Transactions on Information Theory, 28 pages, 15
figures. The results can be reproduced using the following Matlab code:
https://github.com/emilbjornson/massive-MIMO-hardware-impairment
Can Hardware Distortion Correlation be Neglected When Analyzing Uplink SE in Massive MIMO?
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. The 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
the distortion correlation are first uncovered. Then, we focus on third-order
non-linearities and prove analytically and numerically that the correlation can
be neglected in the SE analysis when there are many users. In i.i.d. Rayleigh
fading with equal signal-to-noise ratios, this occurs when having five users.Comment: 5 pages, 3 figures, IEEE International Workshop on Signal Processing
Advances in Wireless Communications (SPAWC), 201
Performance Analysis of Cell-Free Massive MIMO Systems: A Stochastic Geometry Approach
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Cell-free (CF) massive multiple-input-multiple-output (MIMO) has emerged as an alternative deployment for conventional cellular massive MIMO networks. As revealed by its name, this topology considers no cells, while a large number of multi-antenna access points (APs) serves simultaneously a smaller number of users over the same time/frequency resources through time-division duplex (TDD) operation. Prior works relied on the strong assumption (quite idealized) that the APs are uniformly distributed, and actually, this randomness was considered during the simulation and not in the analysis. However, in practice, ongoing and future networks become denser and increasingly irregular. Having this in mind, we consider that the AP locations are modeled by means of a Poisson point process (PPP) which is a more realistic model for the spatial randomness than a grid or uniform deployment. In particular, by virtue of stochastic geometry tools, we derive both the downlink coverage probability and achievable rate. Notably, this is the only work providing the coverage probability and shedding light on this aspect of CF massive MIMO systems. Focusing on the extraction of interesting insights, we consider small-cells (SCs) as a benchmark for comparison. Among the findings, CF massive MIMO systems achieve both higher coverage and rate with comparison to SCs due to the properties of favorable propagation, channel hardening, and interference suppression. Especially, we showed for both architectures that increasing the AP density results in a higher coverage which saturates after a certain value and increasing the number of users decreases the achievable rate but CF massive MIMO systems take advantage of the aforementioned properties, and thus, outperform SCs. In general, the performance gap between CF massive MIMO systems and SCs is enhanced by increasing the AP density. Another interesting observation concerns that a higher path-loss exponent decreases the rate while the users closer to the APs affect more the performance in terms of the rate.Peer reviewe
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
Massive MIMO for Next Generation Wireless Systems
Multi-user Multiple-Input Multiple-Output (MIMO) offers big advantages over
conventional point-to-point MIMO: it works with cheap single-antenna terminals,
a rich scattering environment is not required, and resource allocation is
simplified because every active terminal utilizes all of the time-frequency
bins. However, multi-user MIMO, as originally envisioned with roughly equal
numbers of service-antennas and terminals and frequency division duplex
operation, is not a scalable technology. Massive MIMO (also known as
"Large-Scale Antenna Systems", "Very Large MIMO", "Hyper MIMO", "Full-Dimension
MIMO" & "ARGOS") makes a clean break with current practice through the use of a
large excess of service-antennas over active terminals and time division duplex
operation. Extra antennas help by focusing energy into ever-smaller regions of
space to bring huge improvements in throughput and radiated energy efficiency.
Other benefits of massive MIMO include the extensive use of inexpensive
low-power components, reduced latency, simplification of the media access
control (MAC) layer, and robustness to intentional jamming. The anticipated
throughput depend on the propagation environment providing asymptotically
orthogonal channels to the terminals, but so far experiments have not disclosed
any limitations in this regard. While massive MIMO renders many traditional
research problems irrelevant, it uncovers entirely new problems that urgently
need attention: the challenge of making many low-cost low-precision components
that work effectively together, acquisition and synchronization for
newly-joined terminals, the exploitation of extra degrees of freedom provided
by the excess of service-antennas, reducing internal power consumption to
achieve total energy efficiency reductions, and finding new deployment
scenarios. This paper presents an overview of the massive MIMO concept and
contemporary research.Comment: Final manuscript, to appear in IEEE Communications Magazin
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