853 research outputs found
Distributed Compressive CSIT Estimation and Feedback for FDD Multi-user Massive MIMO Systems
To fully utilize the spatial multiplexing gains or array gains of massive
MIMO, the channel state information must be obtained at the transmitter side
(CSIT). However, conventional CSIT estimation approaches are not suitable for
FDD massive MIMO systems because of the overwhelming training and feedback
overhead. In this paper, we consider multi-user massive MIMO systems and deploy
the compressive sensing (CS) technique to reduce the training as well as the
feedback overhead in the CSIT estimation. The multi-user massive MIMO systems
exhibits a hidden joint sparsity structure in the user channel matrices due to
the shared local scatterers in the physical propagation environment. As such,
instead of naively applying the conventional CS to the CSIT estimation, we
propose a distributed compressive CSIT estimation scheme so that the compressed
measurements are observed at the users locally, while the CSIT recovery is
performed at the base station jointly. A joint orthogonal matching pursuit
recovery algorithm is proposed to perform the CSIT recovery, with the
capability of exploiting the hidden joint sparsity in the user channel
matrices. We analyze the obtained CSIT quality in terms of the normalized mean
absolute error, and through the closed-form expressions, we obtain simple
insights into how the joint channel sparsity can be exploited to improve the
CSIT recovery performance.Comment: 16 double-column pages, accepted for publication in IEEE Transactions
on Signal Processin
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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