2,457 research outputs found
Characterization of Coded Random Access with Compressive Sensing based Multi-User Detection
The emergence of Machine-to-Machine (M2M) communication requires new Medium
Access Control (MAC) schemes and physical (PHY) layer concepts to support a
massive number of access requests. The concept of coded random access,
introduced recently, greatly outperforms other random access methods and is
inherently capable to take advantage of the capture effect from the PHY layer.
Furthermore, at the PHY layer, compressive sensing based multi-user detection
(CS-MUD) is a novel technique that exploits sparsity in multi-user detection to
achieve a joint activity and data detection. In this paper, we combine coded
random access with CS-MUD on the PHY layer and show very promising results for
the resulting protocol.Comment: Submitted to Globecom 201
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
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
Compressive Random Access Using A Common Overloaded Control Channel
We introduce a "one shot" random access procedure where users can send a
message without a priori synchronizing with the network. In this procedure a
common overloaded control channel is used to jointly detect sparse user
activity and sparse channel profiles. The detected information is subsequently
used to demodulate the data in dedicated frequency slots. We analyze the system
theoretically and provide a link between achievable rates and standard
compressing sensing estimates in terms of explicit expressions and scaling
laws. Finally, we support our findings with simulations in an LTE-A-like
setting allowing "one shot" sparse random access of 100 users in 1ms.Comment: 6 pages, 3 figures, published at Globecom 201
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