10,387 research outputs found
Enhanced Compressive Wideband Frequency Spectrum Sensing for Dynamic Spectrum Access
Wideband spectrum sensing detects the unused spectrum holes for dynamic
spectrum access (DSA). Too high sampling rate is the main problem. Compressive
sensing (CS) can reconstruct sparse signal with much fewer randomized samples
than Nyquist sampling with high probability. Since survey shows that the
monitored signal is sparse in frequency domain, CS can deal with the sampling
burden. Random samples can be obtained by the analog-to-information converter.
Signal recovery can be formulated as an L0 norm minimization and a linear
measurement fitting constraint. In DSA, the static spectrum allocation of
primary radios means the bounds between different types of primary radios are
known in advance. To incorporate this a priori information, we divide the whole
spectrum into subsections according to the spectrum allocation policy. In the
new optimization model, the minimization of the L2 norm of each subsection is
used to encourage the cluster distribution locally, while the L0 norm of the L2
norms is minimized to give sparse distribution globally. Because the L0/L2
optimization is not convex, an iteratively re-weighted L1/L2 optimization is
proposed to approximate it. Simulations demonstrate the proposed method
outperforms others in accuracy, denoising ability, etc.Comment: 23 pages, 6 figures, 4 table. arXiv admin note: substantial text
overlap with arXiv:1005.180
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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