3,030 research outputs found
Compressive Channel Estimation and Multi-user Detection in C-RAN
This paper considers the channel estimation (CE) and multi-user detection
(MUD) problems in cloud radio access network (C-RAN). Assuming that active
users are sparse in the network, we solve CE and MUD problems with compressed
sensing (CS) technology to greatly reduce the long identification pilot
overhead. A mixed L{2,1}-regularization functional for extended sparse
group-sparsity recovery is proposed to exploit the inherently sparse property
existing both in user activities and remote radio heads (RRHs) that active
users are attached to. Empirical and theoretical guidelines are provided to
help choosing tuning parameters which have critical effect on the performance
of the penalty functional. To speed up the processing procedure, based on
alternating direction method of multipliers and variable splitting strategy, an
efficient algorithm is formulated which is guaranteed to be convergent.
Numerical results are provided to illustrate the effectiveness of the proposed
functional and efficient algorithm.Comment: 6 pages, 3 figure
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
Performance Limits of Compressive Sensing Channel Estimation in Dense Cloud RAN
Towards reducing the training signaling overhead in large scale and dense
cloud radio access networks (CRAN), various approaches have been proposed based
on the channel sparsification assumption, namely, only a small subset of the
deployed remote radio heads (RRHs) are of significance to any user in the
system. Motivated by the potential of compressive sensing (CS) techniques in
this setting, this paper provides a rigorous description of the performance
limits of many practical CS algorithms by considering the performance of the,
so called, oracle estimator, which knows a priori which RRHs are of
significance but not their corresponding channel values. By using tools from
stochastic geometry, a closed form analytical expression of the oracle
estimator performance is obtained, averaged over distribution of RRH positions
and channel statistics. Apart from a bound on practical CS algorithms, the
analysis provides important design insights, e.g., on how the training sequence
length affects performance, and identifies the operational conditions where the
channel sparsification assumption is valid. It is shown that the latter is true
only in operational conditions with sufficiently large path loss exponents.Comment: 6 pages, two-column format; ICC 201
Compressive Massive Access for Internet of Things: Cloud Computing or Fog Computing?
This paper considers the support of grant-free massive access and solves the
challenge of active user detection and channel estimation in the case of a
massive number of users. By exploiting the sparsity of user activities, the
concerned problems are formulated as a compressive sensing problem, whose
solution is acquired by approximate message passing (AMP) algorithm.
Considering the cooperation of multiple access points, for the deployment of
AMP algorithm, we compare two processing paradigms, cloud computing and fog
computing, in terms of their effectiveness in guaranteeing ultra reliable
low-latency access. For cloud computing, the access points are connected in a
cloud radio access network (C-RAN) manner, and the signals received at all
access points are concentrated and jointly processed in the cloud baseband
unit. While for fog computing, based on fog radio access network (F-RAN), the
estimation of user activity and corresponding channels for the whole network is
split, and the related processing tasks are performed at the access points and
fog processing units in proximity to users. Compared to the cloud computing
paradigm based on traditional C-RAN, simulation results demonstrate the
superiority of the proposed fog computing deployment based on F-RAN.Comment: 7 pages, 7 figures, accepted by IEEE International Conference on
Communications (ICC) 2020, Dublin, Irelan
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