983 research outputs found
User Activity Detection and Channel Estimation for Grant-Free Random Access in LEO Satellite-Enabled Internet-of-Things
With recent advances on the dense low-earth orbit (LEO) constellation, LEO
satellite network has become one promising solution to providing global
coverage for Internet-of-Things (IoT) services. Confronted with the sporadic
transmission from randomly activated IoT devices, we consider the random access
(RA) mechanism, and propose a grant-free RA (GF-RA) scheme to reduce the access
delay to the mobile LEO satellites. A Bernoulli-Rician message passing with
expectation maximization (BR-MP-EM) algorithm is proposed for this
terrestrial-satellite GF-RA system to address the user activity detection (UAD)
and channel estimation (CE) problem. This BR-MP-EM algorithm is divided into
two stages. In the inner iterations, the Bernoulli messages and Rician messages
are updated for the joint UAD and CE problem. Based on the output of the inner
iterations, the expectation maximization (EM) method is employed in the outer
iterations to update the hyper-parameters related to the channel impairments.
Finally, simulation results show the UAD and CE accuracy of the proposed
BR-MP-EM algorithm, as well as the robustness against the channel impairments.Comment: 14 pages, 9 figures, accepted by Internet of Things Journa
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
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