418 research outputs found
On Low-Resolution ADCs in Practical 5G Millimeter-Wave Massive MIMO Systems
Nowadays, millimeter-wave (mmWave) massive multiple-input multiple-output
(MIMO) systems is a favorable candidate for the fifth generation (5G) cellular
systems. However, a key challenge is the high power consumption imposed by its
numerous radio frequency (RF) chains, which may be mitigated by opting for
low-resolution analog-to-digital converters (ADCs), whilst tolerating a
moderate performance loss. In this article, we discuss several important issues
based on the most recent research on mmWave massive MIMO systems relying on
low-resolution ADCs. We discuss the key transceiver design challenges including
channel estimation, signal detector, channel information feedback and transmit
precoding. Furthermore, we introduce a mixed-ADC architecture as an alternative
technique of improving the overall system performance. Finally, the associated
challenges and potential implementations of the practical 5G mmWave massive
MIMO system {with ADC quantizers} are discussed.Comment: to appear in IEEE Communications Magazin
Hardware Impairments in Large-scale MISO Systems: Energy Efficiency, Estimation, and Capacity Limits
The use of large-scale antenna arrays has the potential to bring substantial
improvements in energy efficiency and/or spectral efficiency to future wireless
systems, due to the greatly improved spatial beamforming resolution. Recent
asymptotic results show that by increasing the number of antennas one can
achieve a large array gain and at the same time naturally decorrelate the user
channels; thus, the available energy can be focused very accurately at the
intended destinations without causing much inter-user interference. Since these
results rely on asymptotics, it is important to investigate whether the
conventional system models are still reasonable in the asymptotic regimes. This
paper analyzes the fundamental limits of large-scale multiple-input
single-output (MISO) communication systems using a generalized system model
that accounts for transceiver hardware impairments. As opposed to the case of
ideal hardware, we show that these practical impairments create finite ceilings
on the estimation accuracy and capacity of large-scale MISO systems.
Surprisingly, the performance is only limited by the hardware at the
single-antenna user terminal, while the impact of impairments at the
large-scale array vanishes asymptotically. Furthermore, we show that an
arbitrarily high energy efficiency can be achieved by reducing the power while
increasing the number of antennas.Comment: Published at International Conference on Digital Signal Processing
(DSP 2013), 6 pages, 5 figure
Impact of Residual Transmit RF Impairments on Training-Based MIMO Systems
Radio-frequency (RF) impairments, that exist intimately in wireless
communications systems, can severely degrade the performance of traditional
multiple-input multiple-output (MIMO) systems. Although compensation schemes
can cancel out part of these RF impairments, there still remains a certain
amount of impairments. These residual impairments have fundamental impact on
the MIMO system performance. However, most of the previous works have neglected
this factor. In this paper, a training-based MIMO system with residual transmit
RF impairments (RTRI) is considered. In particular, we derive a new channel
estimator for the proposed model, and find that RTRI can create an irreducible
estimation error floor. Moreover, we show that, in the presence of RTRI, the
optimal training sequence length can be larger than the number of transmit
antennas, especially in the low and high signal-to-noise ratio (SNR) regimes.
An increase in the proposed approximated achievable rate is also observed by
adopting the optimal training sequence length. When the training and data
symbol powers are required to be equal, we demonstrate that, at high SNRs,
systems with RTRI demand more training, whereas at low SNRs, such demands are
nearly the same for all practical levels of RTRI.Comment: Accepted for publication at the IEEE International Conference on
Communications (ICC 2014), 6 pages, 5 figure
Is Channel Estimation Necessary to Select Phase-Shifts for RIS-Assisted Massive MIMO?
Reconfigurable intelligent surfaces (RISs) have attracted great attention as
a potential beyond 5G technology. These surfaces consist of many passive
elements of metamaterials whose impedance can be controllable to change the
characteristics of wireless signals impinging on them. Channel estimation is a
critical task when it comes to the control of a large RIS when having a channel
with a large number of multipath components. In this paper, we propose novel
channel estimation schemes for different RIS-assisted massive multiple-input
multiple-output (MIMO) configurations. The proposed methods exploit spatial
correlation characteristics at both the base station and the planar RISs, and
other statistical characteristics of multi-specular fading in a mobile
environment. Moreover, a novel heuristic for phase-shift selection at the RISs
is developed. For the RIS-assisted massive MIMO, a new receive combining method
and a fixed-point algorithm, which solves the max-min fairness power control
optimally, are proposed. Simulation results demonstrate that the proposed
uplink RIS-aided framework improves the spectral efficiency of the cell-edge
mobile user equipments substantially in comparison to a conventional
single-cell massive MIMO system. The impact of several channel effects are
studied to gain insight about which RIS configuration is preferable and when
the channel estimation is necessary to boost the spectral efficiency.Comment: 30 pages, 9 figures, submitted to IEEE Journa
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