11,744 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
Compressed Sensing based Dynamic PSD Map Construction in Cognitive Radio Networks
In the context of spectrum sensing in cognitive radio networks, collaborative spectrum sensing has been proposed as a way to overcome multipath and shadowing, and hence increasing the reliability of the sensing. Due to the high amount of information to be transmitted, a dynamic compressive sensing approach is proposed to map the PSD estimate to a sparse domain which is then transmitted to the fusion center. In this regard, CRs send a compressed version of their estimated PSD to the fusion center, whose job is to reconstruct the PSD estimates of the CRs, fuse them, and make a global decision on the availability of the spectrum in space and frequency domains at a given time. The proposed compressive sensing based method considers the dynamic nature of the PSD map, and uses this dynamicity in order to decrease the amount of data needed to be transmitted between CR sensors’ and the fusion center. By using the proposed method, an acceptable PSD map for cognitive radio purposes can be achieved by only 20 % of full data transmission between sensors and master node. Also, simulation results show the robustness of the proposed method against the channel variations, diverse compression ratios and processing times in comparison with static methods
Ensemble Analysis of Adaptive Compressed Genome Sequencing Strategies
Acquiring genomes at single-cell resolution has many applications such as in
the study of microbiota. However, deep sequencing and assembly of all of
millions of cells in a sample is prohibitively costly. A property that can come
to rescue is that deep sequencing of every cell should not be necessary to
capture all distinct genomes, as the majority of cells are biological
replicates. Biologically important samples are often sparse in that sense. In
this paper, we propose an adaptive compressed method, also known as distilled
sensing, to capture all distinct genomes in a sparse microbial community with
reduced sequencing effort. As opposed to group testing in which the number of
distinct events is often constant and sparsity is equivalent to rarity of an
event, sparsity in our case means scarcity of distinct events in comparison to
the data size. Previously, we introduced the problem and proposed a distilled
sensing solution based on the breadth first search strategy. We simulated the
whole process which constrained our ability to study the behavior of the
algorithm for the entire ensemble due to its computational intensity. In this
paper, we modify our previous breadth first search strategy and introduce the
depth first search strategy. Instead of simulating the entire process, which is
intractable for a large number of experiments, we provide a dynamic programming
algorithm to analyze the behavior of the method for the entire ensemble. The
ensemble analysis algorithm recursively calculates the probability of capturing
every distinct genome and also the expected total sequenced nucleotides for a
given population profile. Our results suggest that the expected total sequenced
nucleotides grows proportional to of the number of cells and
proportional linearly with the number of distinct genomes
Random Access in C-RAN for User Activity Detection with Limited-Capacity Fronthaul
Cloud-Radio Access Network (C-RAN) is characterized by a hierarchical
structure in which the baseband processing functionalities of remote radio
heads (RRHs) are implemented by means of cloud computing at a Central Unit
(CU). A key limitation of C-RANs is given by the capacity constraints of the
fronthaul links connecting RRHs to the CU. In this letter, the impact of this
architectural constraint is investigated for the fundamental functions of
random access and active User Equipment (UE) identification in the presence of
a potentially massive number of UEs. In particular, the standard C-RAN approach
based on quantize-and-forward and centralized detection is compared to a scheme
based on an alternative CU-RRH functional split that enables local detection.
Both techniques leverage Bayesian sparse detection. Numerical results
illustrate the relative merits of the two schemes as a function of the system
parameters.Comment: 6 pages, 3 figures, under revision in IEEE Signal Processing Letter
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