Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Time Synchronization of Turbo-Coded Square-QAM-Modulated Transmissions:
  Code-Aided ML Estimator and Closed-Form Cram\'er-Rao Lower Bounds   This paper introduces a new maximum likelihood (ML) solution for the
code-aided (CA) timing recovery problem in square-QAM transmissions and
derives, for the very first time, its CA Cram\'er-Rao lower bounds (CRLBs) in
closed-form expressions. By exploiting the full symmetry of square-QAM
constellations and further scrutinizing the Gray-coding mechanism, we express
the likelihood function (LF) of the system explicitly in terms of the code
bits' \textit{a priori} log-likelihood ratios (LLRs). The timing recovery task
is then embedded in the turbo iteration loop wherein increasingly accurate
estimates for such LLRs are computed from the output of the soft-input
soft-output (SISO) decoders and exploited at a per-turbo-iteration basis in
order to refine the ML time delay estimate. The latter is then used to better
re-synchronize the system, through feedback to the matched filter (MF), so as
to obtain more reliable symbol-rate samples for the next turbo iteration. In
order to properly benchmark the new CA ML estimator, we also derive for the
very first time the closed-form expressions for the exact CRLBs of the
underlying turbo synchronization problem. Computer simulations will show that
the new closed-form CRLBs coincide exactly with their empirical counterparts
evaluated previously using exhaustive Monte-Carlo simulations. They will also
show unambiguously the potential performance gains in time synchronization that
can be achieved owing to the decoder assistance. Moreover, the new CA ML
estimator almost reaches the underlying CA CRLBs, even for small SNRs, thereby
confirming its statistical efficiency in practice. It also enjoys significant
improvements in computational complexity as compared to the most powerful
existing ML solution, namely the combined sum-product and
expectation-maximization (SP-EM) algorithm.

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The categories of this paper are: cond-mat
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The categories of this paper are: comp-gas
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The categories of this paper are: hep-th
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The categories of this paper are: physics
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The categories of this paper are: hep-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: eess
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The categories of this paper are: gr-qc
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The categories of this paper are: nucl-ex
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The categories of this paper are: dg-ga
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The categories of this paper are: supr-con
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The categories of this paper are: hep-ex
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The categories of this paper are: hep-lat
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The categories of this paper are: cmp-lg
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The categories of this paper are: acc-phys