3,616 research outputs found
Random Access Protocols for Massive MIMO
5G wireless networks are expected to support new services with stringent
requirements on data rates, latency and reliability. One novel feature is the
ability to serve a dense crowd of devices, calling for radically new ways of
accessing the network. This is the case in machine-type communications, but
also in urban environments and hotspots. In those use cases, the high number of
devices and the relatively short channel coherence interval do not allow
per-device allocation of orthogonal pilot sequences. This article motivates the
need for random access by the devices to pilot sequences used for channel
estimation, and shows that Massive MIMO is a main enabler to achieve fast
access with high data rates, and delay-tolerant access with different data rate
levels. Three pilot access protocols along with data transmission protocols are
described, fulfilling different requirements of 5G services
Random Pilot and Data Access in Massive MIMO for Machine-type Communications
A massive MIMO system, represented by a base station with hundreds of
antennas, is capable of spatially multiplexing many devices and thus naturally
suited to serve dense crowds of wireless devices in emerging applications, such
as machine-type communications. Crowd scenarios pose new challenges in the
pilot-based acquisition of channel state information and call for pilot access
protocols that match the intermittent pattern of device activity. A joint pilot
assignment and data transmission protocol based on random access is proposed in
this paper for the uplink of a massive MIMO system. The protocol relies on the
averaging across multiple transmission slots of the pilot collision events that
result from the random access process. We derive new uplink sum rate
expressions that take pilot collisions, intermittent device activity, and
interference into account. Simplified bounds are obtained and used to optimize
the device activation probability and pilot length. A performance analysis
indicates how performance scales as a function of the number of antennas and
the transmission slot duration
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
A Random Access Protocol for Pilot Allocation in Crowded Massive MIMO Systems
The Massive MIMO (multiple-input multiple-output) technology has great
potential to manage the rapid growth of wireless data traffic. Massive MIMO
achieves tremendous spectral efficiency by spatial multiplexing of many tens of
user equipments (UEs). These gains are only achieved in practice if many more
UEs can connect efficiently to the network than today. As the number of UEs
increases, while each UE intermittently accesses the network, the random access
functionality becomes essential to share the limited number of pilots among the
UEs. In this paper, we revisit the random access problem in the Massive MIMO
context and develop a reengineered protocol, termed strongest-user collision
resolution (SUCRe). An accessing UE asks for a dedicated pilot by sending an
uncoordinated random access pilot, with a risk that other UEs send the same
pilot. The favorable propagation of Massive MIMO channels is utilized to enable
distributed collision detection at each UE, thereby determining the strength of
the contenders' signals and deciding to repeat the pilot if the UE judges that
its signal at the receiver is the strongest. The SUCRe protocol resolves the
vast majority of all pilot collisions in crowded urban scenarios and continues
to admit UEs efficiently in overloaded networks.Comment: To appear in IEEE Transactions on Wireless Communications, 16 pages,
10 figures. This is reproducible research with simulation code available at
https://github.com/emilbjornson/sucre-protoco
A Novel Uplink Data Transmission Scheme For Small Packets In Massive MIMO System
Intelligent terminals often produce a large number of data packets of small
lengths. For these packets, it is inefficient to follow the conventional medium
access control (MAC) protocols because they lead to poor utilization of service
resources. We propose a novel multiple access scheme that targets massive
multiple-input multiple-output (MIMO) systems based on compressive sensing
(CS). We employ block precoding in the time domain to enable the simultaneous
transmissions of many users, which could be even more than the number of
receive antennas at the base station. We develop a block-sparse system model
and adopt the block orthogonal matching pursuit (BOMP) algorithm to recover the
transmitted signals. Conditions for data recovery guarantees are identified and
numerical results demonstrate that our scheme is efficient for uplink small
packet transmission.Comment: IEEE/CIC ICCC 2014 Symposium on Signal Processing for Communication
On Modeling Heterogeneous Wireless Networks Using Non-Poisson Point Processes
Future wireless networks are required to support 1000 times higher data rate,
than the current LTE standard. In order to meet the ever increasing demand, it
is inevitable that, future wireless networks will have to develop seamless
interconnection between multiple technologies. A manifestation of this idea is
the collaboration among different types of network tiers such as macro and
small cells, leading to the so-called heterogeneous networks (HetNets).
Researchers have used stochastic geometry to analyze such networks and
understand their real potential. Unsurprisingly, it has been revealed that
interference has a detrimental effect on performance, especially if not modeled
properly. Interference can be correlated in space and/or time, which has been
overlooked in the past. For instance, it is normally assumed that the nodes are
located completely independent of each other and follow a homogeneous Poisson
point process (PPP), which is not necessarily true in real networks since the
node locations are spatially dependent. In addition, the interference
correlation created by correlated stochastic processes has mostly been ignored.
To this end, we take a different approach in modeling the interference where we
use non-PPP, as well as we study the impact of spatial and temporal correlation
on the performance of HetNets. To illustrate the impact of correlation on
performance, we consider three case studies from real-life scenarios.
Specifically, we use massive multiple-input multiple-output (MIMO) to
understand the impact of spatial correlation; we use the random medium access
protocol to examine the temporal correlation; and we use cooperative relay
networks to illustrate the spatial-temporal correlation. We present several
numerical examples through which we demonstrate the impact of various
correlation types on the performance of HetNets.Comment: Submitted to IEEE Communications Magazin
Massive MIMO for Crowd Scenarios: A Solution Based on Random Access
This paper presents a new approach to intra-cell pilot contamination in
crowded massive MIMO scenarios. The approach relies on two essential properties
of a massive MIMO system, namely near-orthogonality between user channels and
near-stability of channel powers. Signal processing techniques that take
advantage of these properties allow us to view a set of contaminated pilot
signals as a graph code on which iterative belief propagation can be performed.
This makes it possible to decontaminate pilot signals and increase the
throughput of the system. The proposed solution exhibits high performance with
large improvements over the conventional method. The improvements come at the
price of an increased error rate, although this effect is shown to decrease
significantly for increasing number of antennas at the base station
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