Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Measurement Bounds for Compressed Sensing in Sensor Networks with
  Missing Data   In this paper, we study the problem of sparse vector recovery at the fusion
center of a sensor network from linear sensor measurements when there is
missing data. In the presence of missing data, the random sampling approach
employed in compressed sensing is known to provide excellent reconstruction
accuracy. However, when there is missing data, the theoretical guarantees
associated with sparse recovery have not been well studied. Therefore, in this
paper, we derive an upper bound on the minimum number of measurements required
to ensure faithful recovery of a sparse signal when the generation of missing
data is modeled using a Bernoulli erasure channel. We analyze three different
network topologies, namely, star, (relay aided-)tree, and serial-star
topologies. Our analysis establishes how the minimum required number of
measurements for recovery scales with the network parameters, the properties of
the measurement matrix, and the recovery algorithm. Finally, through numerical
simulations, we show the variation of the minimum required number of
measurements with different system parameters and validate our theoretical
results.

24
The categories of this paper are: dg-ga
5
The categories of this paper are: gr-qc
8
The categories of this paper are: hep-ex
34
The categories of this paper are: chem-ph
27
The categories of this paper are: comp-gas
33
The categories of this paper are: plasm-ph
3
The categories of this paper are: physics
2
The categories of this paper are: math
31
The categories of this paper are: ao-sci
0
The categories of this paper are: hep-ph
25
The categories of this paper are: patt-sol
9
The categories of this paper are: nlin
32
The categories of this paper are: atom-ph
12
The categories of this paper are: nucl-th
36
The categories of this paper are: cmp-lg
29
The categories of this paper are: funct-an