Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DOA Parameter Estimation with 1-bit Quantization - Bounds, Methods and
  the Exponential Replacement   While 1-bit analog-to-digital conversion (ADC) allows to significantly reduce
the analog complexity of wireless receive systems, using the exact likelihood
function of the hard-limiting system model in order to obtain efficient
algorithms in the digital domain can make 1-bit signal processing challenging.
If the signal model before the quantizer consists of correlated Gaussian random
variables, the tail probability for a multivariate Gaussian distribution with N
dimensions (general orthant probability) is required in order to formulate the
likelihood function of the quantizer output. As a closed-form expression for
the general orthant probability is an open mathematical problem, formulation of
efficient processing methods for correlated and quantized data and an
analytical performance assessment have, despite their high practical relevance,
only found limited attention in the literature on quantized estimation theory.
Here we review the approach of replacing the original system model by an
equivalent distribution within the exponential family. For 1-bit signal
processing, this allows to circumvent calculation of the general orthant
probability and gives access to a conservative approximation of the receive
likelihood. For the application of blind direction-of-arrival (DOA) parameter
estimation with an array of K sensors, each performing 1-bit quantization, we
demonstrate how the exponential replacement enables to formulate a pessimistic
version of the Cram\'er-Rao lower bound (CRLB) and to derive an asymptotically
achieving conservative maximum-likelihood estimator (CMLE). The 1-bit DOA
performance analysis based on the pessimistic CRLB points out that a
low-complexity radio front-end design with 1-bit ADC is in particular suitable
for blind wireless DOA estimation with a large number of array elements
operating in the medium SNR regime.

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The categories of this paper are: bayes-an
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The categories of this paper are: comp-gas
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The categories of this paper are: astro-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: econ
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The categories of this paper are: mtrl-th
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The categories of this paper are: hep-lat
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The categories of this paper are: quant-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: plasm-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: q-fin
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The categories of this paper are: ao-sci
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The categories of this paper are: stat
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The categories of this paper are: hep-ph
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The categories of this paper are: gr-qc