26,712 research outputs found
Power Allocation and Parameter Estimation for Multipath-based 5G Positioning
We consider a single-anchor multiple-input multiple-output (MIMO) orthogonal
frequency-division multiplexing (OFDM) system with imperfectly synchronized
transmitter (Tx) and receiver (Rx) clocks, where the Rx estimates its position
based on the received reference signals. The Tx, having (imperfect) prior
knowledge about the Rx location and the surrounding geometry, transmits the
reference signals based on a set of fixed beams. In this work, we develop
strategies for the power allocation among the beams aiming to minimize the
expected Cram\'er-Rao lower bound (CRLB) for Rx positioning. Additional
constraints on the design are included to ensure that the line-of-sight (LOS)
path is detected with high probability. Furthermore, the effect of clock
asynchronism on the resulting allocation strategies is also studied. We also
propose a gridless compressed sensing-based position estimation algorithm,
which exploits the information on the clock offset provided by
non-line-of-sight paths, and show that it is asymptotically efficient.Comment: 30 pages, 6 figures, submitted to IEEE Transactions on Wireless
Communication
Compressed Sensing Based Direct Conversion Receiver With Interference Reducing Sampling
This paper describes a direct conversion receiver applying compressed sensing
with the objective to relax the analog filtering requirements seen in the
traditional architecture. The analog filter is cumbersome in an \gls{IC} design
and relaxing its requirements is an advantage in terms of die area, performance
and robustness of the receiver. The objective is met by a selection of sampling
pattern matched to the prior knowledge of the frequency placement of the
desired and interfering signals. A simple numerical example demonstrates the
principle. The work is part of an ongoing research effort and the different
project phases are explained.Comment: 3 pages, 5 figures, submitted to IEEE International Conference On
Sensing Communication and Networking 2014 (poster
Info-Greedy sequential adaptive compressed sensing
We present an information-theoretic framework for sequential adaptive
compressed sensing, Info-Greedy Sensing, where measurements are chosen to
maximize the extracted information conditioned on the previous measurements. We
show that the widely used bisection approach is Info-Greedy for a family of
-sparse signals by connecting compressed sensing and blackbox complexity of
sequential query algorithms, and present Info-Greedy algorithms for Gaussian
and Gaussian Mixture Model (GMM) signals, as well as ways to design sparse
Info-Greedy measurements. Numerical examples demonstrate the good performance
of the proposed algorithms using simulated and real data: Info-Greedy Sensing
shows significant improvement over random projection for signals with sparse
and low-rank covariance matrices, and adaptivity brings robustness when there
is a mismatch between the assumed and the true distributions.Comment: Preliminary results presented at Allerton Conference 2014. To appear
in IEEE Journal Selected Topics on Signal Processin
Low-complexity Multiclass Encryption by Compressed Sensing
The idea that compressed sensing may be used to encrypt information from
unauthorised receivers has already been envisioned, but never explored in depth
since its security may seem compromised by the linearity of its encoding
process. In this paper we apply this simple encoding to define a general
private-key encryption scheme in which a transmitter distributes the same
encoded measurements to receivers of different classes, which are provided
partially corrupted encoding matrices and are thus allowed to decode the
acquired signal at provably different levels of recovery quality.
The security properties of this scheme are thoroughly analysed: firstly, the
properties of our multiclass encryption are theoretically investigated by
deriving performance bounds on the recovery quality attained by lower-class
receivers with respect to high-class ones. Then we perform a statistical
analysis of the measurements to show that, although not perfectly secure,
compressed sensing grants some level of security that comes at almost-zero cost
and thus may benefit resource-limited applications.
In addition to this we report some exemplary applications of multiclass
encryption by compressed sensing of speech signals, electrocardiographic tracks
and images, in which quality degradation is quantified as the impossibility of
some feature extraction algorithms to obtain sensitive information from
suitably degraded signal recoveries.Comment: IEEE Transactions on Signal Processing, accepted for publication.
Article in pres
On Known-Plaintext Attacks to a Compressed Sensing-based Encryption: A Quantitative Analysis
Despite the linearity of its encoding, compressed sensing may be used to
provide a limited form of data protection when random encoding matrices are
used to produce sets of low-dimensional measurements (ciphertexts). In this
paper we quantify by theoretical means the resistance of the least complex form
of this kind of encoding against known-plaintext attacks. For both standard
compressed sensing with antipodal random matrices and recent multiclass
encryption schemes based on it, we show how the number of candidate encoding
matrices that match a typical plaintext-ciphertext pair is so large that the
search for the true encoding matrix inconclusive. Such results on the practical
ineffectiveness of known-plaintext attacks underlie the fact that even
closely-related signal recovery under encoding matrix uncertainty is doomed to
fail.
Practical attacks are then exemplified by applying compressed sensing with
antipodal random matrices as a multiclass encryption scheme to signals such as
images and electrocardiographic tracks, showing that the extracted information
on the true encoding matrix from a plaintext-ciphertext pair leads to no
significant signal recovery quality increase. This theoretical and empirical
evidence clarifies that, although not perfectly secure, both standard
compressed sensing and multiclass encryption schemes feature a noteworthy level
of security against known-plaintext attacks, therefore increasing its appeal as
a negligible-cost encryption method for resource-limited sensing applications.Comment: IEEE Transactions on Information Forensics and Security, accepted for
publication. Article in pres
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