1,347 research outputs found
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
Massive MIMO for Internet of Things (IoT) Connectivity
Massive MIMO is considered to be one of the key technologies in the emerging
5G systems, but also a concept applicable to other wireless systems. Exploiting
the large number of degrees of freedom (DoFs) of massive MIMO essential for
achieving high spectral efficiency, high data rates and extreme spatial
multiplexing of densely distributed users. On the one hand, the benefits of
applying massive MIMO for broadband communication are well known and there has
been a large body of research on designing communication schemes to support
high rates. On the other hand, using massive MIMO for Internet-of-Things (IoT)
is still a developing topic, as IoT connectivity has requirements and
constraints that are significantly different from the broadband connections. In
this paper we investigate the applicability of massive MIMO to IoT
connectivity. Specifically, we treat the two generic types of IoT connections
envisioned in 5G: massive machine-type communication (mMTC) and ultra-reliable
low-latency communication (URLLC). This paper fills this important gap by
identifying the opportunities and challenges in exploiting massive MIMO for IoT
connectivity. We provide insights into the trade-offs that emerge when massive
MIMO is applied to mMTC or URLLC and present a number of suitable communication
schemes. The discussion continues to the questions of network slicing of the
wireless resources and the use of massive MIMO to simultaneously support IoT
connections with very heterogeneous requirements. The main conclusion is that
massive MIMO can bring benefits to the scenarios with IoT connectivity, but it
requires tight integration of the physical-layer techniques with the protocol
design.Comment: Submitted for publicatio
Efficient DSP and Circuit Architectures for Massive MIMO: State-of-the-Art and Future Directions
Massive MIMO is a compelling wireless access concept that relies on the use
of an excess number of base-station antennas, relative to the number of active
terminals. This technology is a main component of 5G New Radio (NR) and
addresses all important requirements of future wireless standards: a great
capacity increase, the support of many simultaneous users, and improvement in
energy efficiency. Massive MIMO requires the simultaneous processing of signals
from many antenna chains, and computational operations on large matrices. The
complexity of the digital processing has been viewed as a fundamental obstacle
to the feasibility of Massive MIMO in the past. Recent advances on
system-algorithm-hardware co-design have led to extremely energy-efficient
implementations. These exploit opportunities in deeply-scaled silicon
technologies and perform partly distributed processing to cope with the
bottlenecks encountered in the interconnection of many signals. For example,
prototype ASIC implementations have demonstrated zero-forcing precoding in real
time at a 55 mW power consumption (20 MHz bandwidth, 128 antennas, multiplexing
of 8 terminals). Coarse and even error-prone digital processing in the antenna
paths permits a reduction of consumption with a factor of 2 to 5. This article
summarizes the fundamental technical contributions to efficient digital signal
processing for Massive MIMO. The opportunities and constraints on operating on
low-complexity RF and analog hardware chains are clarified. It illustrates how
terminals can benefit from improved energy efficiency. The status of technology
and real-life prototypes discussed. Open challenges and directions for future
research are suggested.Comment: submitted to IEEE transactions on signal processin
Quantifying Potential Energy Efficiency Gain in Green Cellular Wireless Networks
Conventional cellular wireless networks were designed with the purpose of
providing high throughput for the user and high capacity for the service
provider, without any provisions of energy efficiency. As a result, these
networks have an enormous Carbon footprint. In this paper, we describe the
sources of the inefficiencies in such networks. First we present results of the
studies on how much Carbon footprint such networks generate. We also discuss
how much more mobile traffic is expected to increase so that this Carbon
footprint will even increase tremendously more. We then discuss specific
sources of inefficiency and potential sources of improvement at the physical
layer as well as at higher layers of the communication protocol hierarchy. In
particular, considering that most of the energy inefficiency in cellular
wireless networks is at the base stations, we discuss multi-tier networks and
point to the potential of exploiting mobility patterns in order to use base
station energy judiciously. We then investigate potential methods to reduce
this inefficiency and quantify their individual contributions. By a
consideration of the combination of all potential gains, we conclude that an
improvement in energy consumption in cellular wireless networks by two orders
of magnitude, or even more, is possible.Comment: arXiv admin note: text overlap with arXiv:1210.843
On the Feasibility of Multi-Mode Antennas in UWB and IoT Applications below 10 GHz
While on the one hand 5G and B5G networks are challenged by ultra-high data
rates in wideband applications like 100+ Gbps wireless Internet access, on the
other hand they are expected to support reliable low-latency Internet of Things
(IoT) applications with ultra-high connectivity. These conflicting challenges
are addressed in a system proposal dealing with both extremes. In contrast to
most recent publications, focus is on the frequency domain below 10~GHz.
Towards this goal, multi-mode antenna technology is used and different
realizations, offering up to eight uncorrelated ports per radiator element, are
studied. Possible baseband architectures tailored to multi-mode antennas are
discussed, enabling different options regarding precoding and beamforming
Modelling Aspects of Planar Multi-Mode Antennas for Direction-of-Arrival Estimation
Multi-mode antennas are an alternative to classical antenna arrays, and hence
a promising emerging sensor technology for a vast variety of applications in
the areas of array signal processing and digital communications. An unsolved
problem is to describe the radiation pattern of multi-mode antennas in closed
analytic form based on calibration measurements or on electromagnetic field
(EMF) simulation data. As a solution, we investigate two modeling methods: One
is based on the array interpolation technique (AIT), the other one on wavefield
modeling (WM). Both methods are able to accurately interpolate quantized EMF
data of a given multi-mode antenna, in our case a planar four-port antenna
developed for the 6-8.5 GHz range. Since the modeling methods inherently depend
on parameter sets, we investigate the influence of the parameter choice on the
accuracy of both models. Furthermore, we evaluate the impact of modeling errors
for coherent maximum-likelihood direction-of-arrival (DoA) estimation given
different model parameters. Numerical results are presented for a single
polarization component. Simulations reveal that the estimation bias introduced
by model errors is subject to the chosen model parameters. Finally, we provide
optimized sets of AIT and WM parameters for the multi-mode antenna under
investigation. With these parameter sets, EMF data samples can be reproduced in
interpolated form with high angular resolution
Frequency Spreading Equalization in Multicarrier Massive MIMO
Application of filter bank multicarrier (FBMC) as an effective method for
signaling over massive MIMO channels has been recently proposed. This paper
further expands the application of FBMC to massive MIMO by applying frequency
spreading equalization (FSE) to these channels. FSE allows us to achieve a more
accurate equalization. Hence, higher number of bits per symbol can be
transmitted and the bandwidth of each subcarrier can be widened. Widening the
bandwidth of each subcarrier leads to (i) higher bandwidth efficiency; (ii)
lower complexity; (iii) lower sensitivity to carrier frequency offset (CFO);
(iv) reduced peak-to-average power ratio (PAPR); and (iv) reduced latency. All
these appealing advantages have a direct impact on the digital as well as
analog circuitry that is needed for the system implementation. In this paper,
we develop the mathematical formulation of the minimum mean square error (MMSE)
FSE for massive MIMO systems. This analysis guides us to decide on the number
of subcarriers that will be sufficient for practical channel models.Comment: Accepted in IEEE ICC 2015 - Workshop on 5G & Beyond - Enabling
Technologies and Application
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