  The compressed sensing paradigm allows to efficiently represent sparse
signals by means of their linear measurements. However, the problem of
transmitting these measurements to a receiver over a channel potentially prone
to packet losses has received little attention so far. In this paper, we
propose novel methods to generate multiple descriptions from compressed sensing
measurements to increase the robustness over unreliable channels. In
particular, we exploit the democracy property of compressive measurements to
generate descriptions in a simple manner by partitioning the measurement vector
and properly allocating bit-rate, outperforming classical methods like the
multiple description scalar quantizer. In addition, we propose a modified
version of the Basis Pursuit Denoising recovery procedure that is specifically
tailored to the proposed methods. Experimental results show significant
performance gains with respect to existing methods.
