421 research outputs found
Grant-Free Massive MTC-Enabled Massive MIMO: A Compressive Sensing Approach
A key challenge of massive MTC (mMTC), is the joint detection of device
activity and decoding of data. The sparse characteristics of mMTC makes
compressed sensing (CS) approaches a promising solution to the device detection
problem. However, utilizing CS-based approaches for device detection along with
channel estimation, and using the acquired estimates for coherent data
transmission is suboptimal, especially when the goal is to convey only a few
bits of data.
First, we focus on the coherent transmission and demonstrate that it is
possible to obtain more accurate channel state information by combining
conventional estimators with CS-based techniques. Moreover, we illustrate that
even simple power control techniques can enhance the device detection
performance in mMTC setups.
Second, we devise a new non-coherent transmission scheme for mMTC and
specifically for grant-free random access. We design an algorithm that jointly
detects device activity along with embedded information bits. The approach
leverages elements from the approximate message passing (AMP) algorithm, and
exploits the structured sparsity introduced by the non-coherent transmission
scheme. Our analysis reveals that the proposed approach has superior
performance compared to application of the original AMP approach.Comment: Submitted to IEEE Transactions on Communication
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
Modeling and Performance of Uplink Cache-Enabled Massive MIMO Heterogeneous Networks
A significant burden on wireless networks is brought by the uploading of user-generated contents to the Internet by means of applications such as social media. To cope with this mobile data tsunami, we develop a novel multiple-input multiple-output (MIMO) network architecture with randomly located base stations (BSs) a large number of antennas employing cache-enabled uplink transmission. In particular, we formulate a scenario, where the users upload their content to their strongest BSs, which are Poisson point process distributed. In addition, the BSs, exploiting the benefits of massive MIMO, upload their contents to the core network by means of a finite-rate backhaul. After proposing the caching policies, where we propose the modified von Mises distribution as the popularity distribution function, we derive the outage probability and the average delivery rate by taking advantage of tools from the deterministic equivalent and stochastic geometry analyses. Numerical results investigate the realistic performance gains of the proposed heterogeneous cache-enabled uplink on the network in terms of cardinal operating parameters. For example, insights regarding the BSs storage size are exposed. Moreover, the impacts of the key parameters such as the file popularity distribution and the target bitrate are investigated. Specifically, the outage probability decreases if the storage size is increased, while the average delivery rate increases. In addition, the concentration parameter, defining the number of files stored at the intermediate nodes (popularity), affects the proposed metrics directly. Furthermore, a higher target rate results in higher outage because fewer users obey this constraint. Also, we demonstrate that a denser network decreases the outage and increases the delivery rate. Hence, the introduction of caching at the uplink of the system design ameliorates the network performance.Peer reviewe
Sparse Reconstruction-based Detection of Spatial Dimension Holes in Cognitive Radio Networks
In this paper, we investigate a spectrum sensing algorithm for detecting
spatial dimension holes in Multiple Inputs Multiple Outputs (MIMO)
transmissions for OFDM systems using Compressive Sensing (CS) tools. This
extends the energy detector to allow for detecting transmission opportunities
even if the band is already energy filled. We show that the task described
above is not performed efficiently by regular MIMO decoders (such as MMSE
decoder) due to possible sparsity in the transmit signal. Since CS
reconstruction tools take into account the sparsity order of the signal, they
are more efficient in detecting the activity of the users. Building on
successful activity detection by the CS detector, we show that the use of a
CS-aided MMSE decoders yields better performance rather than using either
CS-based or MMSE decoders separately. Simulations are conducted to verify the
gains from using CS detector for Primary user activity detection and the
performance gain in using CS-aided MMSE decoders for decoding the PU
information for future relaying.Comment: accepted for PIMRC 201
Ubiquitous Cell-Free Massive MIMO Communications
Since the first cellular networks were trialled in the 1970s, we have
witnessed an incredible wireless revolution. From 1G to 4G, the massive traffic
growth has been managed by a combination of wider bandwidths, refined radio
interfaces, and network densification, namely increasing the number of antennas
per site. Due its cost-efficiency, the latter has contributed the most. Massive
MIMO (multiple-input multiple-output) is a key 5G technology that uses massive
antenna arrays to provide a very high beamforming gain and spatially
multiplexing of users, and hence, increases the spectral and energy efficiency.
It constitutes a centralized solution to densify a network, and its performance
is limited by the inter-cell interference inherent in its cell-centric design.
Conversely, ubiquitous cell-free Massive MIMO refers to a distributed Massive
MIMO system implementing coherent user-centric transmission to overcome the
inter-cell interference limitation in cellular networks and provide additional
macro-diversity. These features, combined with the system scalability inherent
in the Massive MIMO design, distinguishes ubiquitous cell-free Massive MIMO
from prior coordinated distributed wireless systems. In this article, we
investigate the enormous potential of this promising technology while
addressing practical deployment issues to deal with the increased
back/front-hauling overhead deriving from the signal co-processing.Comment: Published in EURASIP Journal on Wireless Communications and
Networking on August 5, 201
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
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