1,414 research outputs found
On the Impact of Hardware Impairments on Massive MIMO
Massive multi-user (MU) multiple-input multiple-output (MIMO) systems are one
possible key technology for next generation wireless communication systems.
Claims have been made that massive MU-MIMO will increase both the radiated
energy efficiency as well as the sum-rate capacity by orders of magnitude,
because of the high transmit directivity. However, due to the very large number
of transceivers needed at each base-station (BS), a successful implementation
of massive MU-MIMO will be contingent on of the availability of very cheap,
compact and power-efficient radio and digital-processing hardware. This may in
turn impair the quality of the modulated radio frequency (RF) signal due to an
increased amount of power-amplifier distortion, phase-noise, and quantization
noise.
In this paper, we examine the effects of hardware impairments on a massive
MU-MIMO single-cell system by means of theory and simulation. The simulations
are performed using simplified, well-established statistical hardware
impairment models as well as more sophisticated and realistic models based upon
measurements and electromagnetic antenna array simulations.Comment: 7 pages, 9 figures, Accepted for presentation at Globe-Com workshop
on Massive MIM
Impact of Transceiver Impairments on the Capacity of Dual-Hop Relay Massive MIMO Systems
Despite the deleterious effect of hardware impairments on communication
systems, most prior works have not investigated their impact on widely used
relay systems. Most importantly, the application of inexpensive transceivers,
being prone to hardware impairments, is the most cost-efficient way for the
implementation of massive multiple-input multiple-output (MIMO) systems.
Consequently, the direction of this paper is towards the investigation of the
impact of hardware impairments on MIMO relay networks with large number of
antennas. Specifically, we obtain the general expression for the ergodic
capacity of dual-hop (DH) amplify-and-forward (AF) relay systems. Next, given
the advantages of the free probability (FP) theory with comparison to other
known techniques in the area of large random matrix theory, we pursue a large
limit analysis in terms of number of antennas and users by shedding light to
the behavior of relay systems inflicted by hardware impairments.Comment: 6 pages, 4 figures, accepted in IEEE Global Communications Conference
(GLOBECOM 2015) - Workshop on Massive MIMO: From theory to practice, 201
Analysis of Massive MIMO With Hardware Impairments and Different Channel Models
Massive Multiple-Input Multiple-Output (MIMO) is foreseen to be one of the
main technology components in next generation cellular communications (5G). In
this paper, fundamental limits on the performance of downlink massive MIMO
systems are investigated by means of simulations and analytical analysis.
Signal-to-noise-and-interference ratio (SINR) and sum rate for a single-cell
scenario multi-user MIMO are analyzed for different array sizes, channel
models, and precoding schemes. The impact of hardware impairments on
performance is also investigated. Simple approximations are derived that show
explicitly how the number of antennas, number of served users, transmit power,
and magnitude of hardware impairments affect performance.Comment: 5 pages, 5 figure
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
Massive MU-MIMO-OFDM Uplink with Hardware Impairments: Modeling and Analysis
We study the impact of hardware impairments at the base station (BS) of an
orthogonal frequency-division multiplexing (OFDM)-based massive multiuser (MU)
multiple-input multiple-output (MIMO) uplink system. We leverage Bussgang's
theorem to develop accurate models for the distortions caused by nonlinear
low-noise amplifiers, local oscillators with phase noise, and oversampling
finite-resolution analog-to-digital converters. By combining the individual
effects of these hardware models, we obtain a composite model for the BS-side
distortion caused by nonideal hardware that takes into account its inherent
correlation in time, frequency, and across antennas. We use this composite
model to analyze the impact of BS-side hardware impairments on the performance
of realistic massive MU-MIMO-OFDM uplink systems
Distributed Massive MIMO in Cellular Networks: Impact of Imperfect Hardware and Number of Oscillators
Distributed massive multiple-input multiple-output (MIMO) combines the array
gain of coherent MIMO processing with the proximity gains of distributed
antenna setups. In this paper, we analyze how transceiver hardware impairments
affect the downlink with maximum ratio transmission. We derive closed-form
spectral efficiencies expressions and study their asymptotic behavior as the
number of the antennas increases. We prove a scaling law on the hardware
quality, which reveals that massive MIMO is resilient to additive distortions,
while multiplicative phase noise is a limiting factor. It is also better to
have separate oscillators at each antenna than one per BS.Comment: First published in the Proceedings of the 23rd European Signal
Processing Conference (EUSIPCO-2015) in 2015, published by EURASIP. 5 pages,
3, figure
Towards a Realistic Assessment of Multiple Antenna HCNs: Residual Additive Transceiver Hardware Impairments and Channel Aging
Given the critical dependence of broadcast channels by the accuracy of
channel state information at the transmitter (CSIT), we develop a general
downlink model with zero-forcing (ZF) precoding, applied in realistic
heterogeneous cellular systems with multiple antenna base stations (BSs).
Specifically, we take into consideration imperfect CSIT due to pilot
contamination, channel aging due to users relative movement, and unavoidable
residual additive transceiver hardware impairments (RATHIs). Assuming that the
BSs are Poisson distributed, the main contributions focus on the derivations of
the upper bound of the coverage probability and the achievable user rate for
this general model. We show that both the coverage probability and the user
rate are dependent on the imperfect CSIT and RATHIs. More concretely, we
quantify the resultant performance loss of the network due to these effects. We
depict that the uplink RATHIs have equal impact, but the downlink transmit BS
distortion has a greater impact than the receive hardware impairment of the
user. Thus, the transmit BS hardware should be of better quality than user's
receive hardware. Furthermore, we characterise both the coverage probability
and user rate in terms of the time variation of the channel. It is shown that
both of them decrease with increasing user mobility, but after a specific value
of the normalised Doppler shift, they increase again. Actually, the time
variation, following the Jakes autocorrelation function, mirrors this effect on
coverage probability and user rate. Finally, we consider space division
multiple access (SDMA), single user beamforming (SU-BF), and baseline
single-input single-output (SISO) transmission. A comparison among these
schemes reveals that the coverage by means of SU-BF outperforms SDMA in terms
of coverage.Comment: accepted in IEEE TV
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