2 research outputs found
Secure Massive IoT Using Hierarchical Fast Blind Deconvolution
The Internet of Things and specifically the Tactile Internet give rise to
significant challenges for notions of security. In this work, we introduce a
novel concept for secure massive access. The core of our approach is a fast and
low-complexity blind deconvolution algorithm exploring a bi-linear and
hierarchical compressed sensing framework. We show that blind deconvolution has
two appealing features: 1) There is no need to coordinate the pilot signals, so
even in the case of collisions in user activity, the information messages can
be resolved. 2) Since all the individual channels are recovered in parallel,
and by assumed channel reciprocity, the measured channel entropy serves as a
common secret and is used as an encryption key for each user. We will outline
the basic concepts underlying the approach and describe the blind deconvolution
algorithm in detail. Eventually, simulations demonstrate the ability of the
algorithm to recover both channel and message. They also exhibit the inherent
trade-offs of the scheme between economical recovery and secret capacity.Comment: submitted to WCNC2018 (accepted
Simultaneous Sparse Recovery and Blind Demodulation
The task of finding a sparse signal decomposition in an overcomplete
dictionary is made more complicated when the signal undergoes an unknown
modulation (or convolution in the complementary Fourier domain). Such
simultaneous sparse recovery and blind demodulation problems appear in many
applications including medical imaging, super resolution, self-calibration,
etc. In this paper, we consider a more general sparse recovery and blind
demodulation problem in which each atom comprising the signal undergoes a
distinct modulation process. Under the assumption that the modulating waveforms
live in a known common subspace, we employ the lifting technique and recast
this problem as the recovery of a column-wise sparse matrix from structured
linear measurements. In this framework, we accomplish sparse recovery and blind
demodulation simultaneously by minimizing the induced atomic norm, which in
this problem corresponds to the block norm minimization. For perfect
recovery in the noiseless case, we derive near optimal sample complexity bounds
for Gaussian and random Fourier overcomplete dictionaries. We also provide
bounds on recovering the column-wise sparse matrix in the noisy case. Numerical
simulations illustrate and support our theoretical results.Comment: 16 pages, 10 figure