2,962 research outputs found
Information Theoretic Limits for Standard and One-Bit Compressed Sensing with Graph-Structured Sparsity
In this paper, we analyze the information theoretic lower bound on the
necessary number of samples needed for recovering a sparse signal under
different compressed sensing settings. We focus on the weighted graph model, a
model-based framework proposed by Hegde et al. (2015), for standard compressed
sensing as well as for one-bit compressed sensing. We study both the noisy and
noiseless regimes. Our analysis is general in the sense that it applies to any
algorithm used to recover the signal. We carefully construct restricted
ensembles for different settings and then apply Fano's inequality to establish
the lower bound on the necessary number of samples. Furthermore, we show that
our bound is tight for one-bit compressed sensing, while for standard
compressed sensing, our bound is tight up to a logarithmic factor of the number
of non-zero entries in the signal
One-Bit ExpanderSketch for One-Bit Compressed Sensing
Is it possible to obliviously construct a set of hyperplanes H such that you
can approximate a unit vector x when you are given the side on which the vector
lies with respect to every h in H? In the sparse recovery literature, where x
is approximately k-sparse, this problem is called one-bit compressed sensing
and has received a fair amount of attention the last decade. In this paper we
obtain the first scheme that achieves almost optimal measurements and sublinear
decoding time for one-bit compressed sensing in the non-uniform case. For a
large range of parameters, we improve the state of the art in both the number
of measurements and the decoding time
User Activity Detection in Massive Random Access: Compressed Sensing vs. Coded Slotted ALOHA
Machine-type communication services in mobile cel- lular systems are
currently evolving with an aim to efficiently address a massive-scale user
access to the system. One of the key problems in this respect is to efficiently
identify active users in order to allocate them resources for the subsequent
transmissions. In this paper, we examine two recently suggested approaches for
user activity detection: compressed-sensing (CS) and coded slotted ALOHA (CSA),
and provide their comparison in terms of performance vs resource utilization.
Our preliminary results show that CS-based approach is able to provide the
target user activity detection performance with less overall system resource
utilization. However, this comes at a price of lower energy- efficiency per
user, as compared to CSA-based approach.Comment: Accepted for presentation at IEEE SPAWC 201
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