3,003 research outputs found
Sparse Signal Processing Concepts for Efficient 5G System Design
As it becomes increasingly apparent that 4G will not be able to meet the
emerging demands of future mobile communication systems, the question what
could make up a 5G system, what are the crucial challenges and what are the key
drivers is part of intensive, ongoing discussions. Partly due to the advent of
compressive sensing, methods that can optimally exploit sparsity in signals
have received tremendous attention in recent years. In this paper we will
describe a variety of scenarios in which signal sparsity arises naturally in 5G
wireless systems. Signal sparsity and the associated rich collection of tools
and algorithms will thus be a viable source for innovation in 5G wireless
system design. We will discribe applications of this sparse signal processing
paradigm in MIMO random access, cloud radio access networks, compressive
channel-source network coding, and embedded security. We will also emphasize
important open problem that may arise in 5G system design, for which sparsity
will potentially play a key role in their solution.Comment: 18 pages, 5 figures, accepted for publication in IEEE Acces
Multiband Spectrum Access: Great Promises for Future Cognitive Radio Networks
Cognitive radio has been widely considered as one of the prominent solutions
to tackle the spectrum scarcity. While the majority of existing research has
focused on single-band cognitive radio, multiband cognitive radio represents
great promises towards implementing efficient cognitive networks compared to
single-based networks. Multiband cognitive radio networks (MB-CRNs) are
expected to significantly enhance the network's throughput and provide better
channel maintenance by reducing handoff frequency. Nevertheless, the wideband
front-end and the multiband spectrum access impose a number of challenges yet
to overcome. This paper provides an in-depth analysis on the recent
advancements in multiband spectrum sensing techniques, their limitations, and
possible future directions to improve them. We study cooperative communications
for MB-CRNs to tackle a fundamental limit on diversity and sampling. We also
investigate several limits and tradeoffs of various design parameters for
MB-CRNs. In addition, we explore the key MB-CRNs performance metrics that
differ from the conventional metrics used for single-band based networks.Comment: 22 pages, 13 figures; published in the Proceedings of the IEEE
Journal, Special Issue on Future Radio Spectrum Access, March 201
Rate-Splitting Multiple Access for 6G Networks: Ten Promising Scenarios and Applications
In the upcoming 6G era, multiple access (MA) will play an essential role in
achieving high throughput performances required in a wide range of wireless
applications. Since MA and interference management are closely related issues,
the conventional MA techniques are limited in that they cannot provide
near-optimal performance in universal interference regimes. Recently,
rate-splitting multiple access (RSMA) has been gaining much attention. RSMA
splits an individual message into two parts: a common part, decodable by every
user, and a private part, decodable only by the intended user. Each user first
decodes the common message and then decodes its private message by applying
successive interference cancellation (SIC). By doing so, RSMA not only embraces
the existing MA techniques as special cases but also provides significant
performance gains by efficiently mitigating inter-user interference in a broad
range of interference regimes. In this article, we first present the
theoretical foundation of RSMA. Subsequently, we put forth four key benefits of
RSMA: spectral efficiency, robustness, scalability, and flexibility. Upon this,
we describe how RSMA can enable ten promising scenarios and applications along
with future research directions to pave the way for 6G.Comment: 17 pages, 6 figures, submitted to IEEE Network Magazin
Collaborative Spectrum Sensing from Sparse Observations in Cognitive Radio Networks
Spectrum sensing, which aims at detecting spectrum holes, is the precondition
for the implementation of cognitive radio (CR). Collaborative spectrum sensing
among the cognitive radio nodes is expected to improve the ability of checking
complete spectrum usage. Due to hardware limitations, each cognitive radio node
can only sense a relatively narrow band of radio spectrum. Consequently, the
available channel sensing information is far from being sufficient for
precisely recognizing the wide range of unoccupied channels. Aiming at breaking
this bottleneck, we propose to apply matrix completion and joint sparsity
recovery to reduce sensing and transmitting requirements and improve sensing
results. Specifically, equipped with a frequency selective filter, each
cognitive radio node senses linear combinations of multiple channel information
and reports them to the fusion center, where occupied channels are then decoded
from the reports by using novel matrix completion and joint sparsity recovery
algorithms. As a result, the number of reports sent from the CRs to the fusion
center is significantly reduced. We propose two decoding approaches, one based
on matrix completion and the other based on joint sparsity recovery, both of
which allow exact recovery from incomplete reports. The numerical results
validate the effectiveness and robustness of our approaches. In particular, in
small-scale networks, the matrix completion approach achieves exact channel
detection with a number of samples no more than 50% of the number of channels
in the network, while joint sparsity recovery achieves similar performance in
large-scale networks.Comment: 12 pages, 11 figure
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