Upgrading an analog recovery loop for optimized decoding jointly to an increased data rate

Giuseppe Visalli ()
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Giuseppe Visalli: Independent Researcher, Padua

Telecommunication Systems: Modelling, Analysis, Design and Management, 2021, vol. 78, issue 2, No 8, 239-252

Abstract: Abstract The maximum likelihood detection theory improves the error rate of a sub-optimal but cheaper, coded symbol recovery loop using oversampling proposed as an alternate solution for the decoding problem without the log-likelihood ratio computation. The former implementation delivers the output data in one-symbol delay, and the required transistor count makes this approach attractive for ultra-low-energy wireless applications. The proposed hardware upgrade includes an analog to digital converter and fixed-point accumulation logic to compute the soft values, replacing a trigger used as a hard detector. This work investigates the soft decoding in the presence of binary and non-binary source symbols. Simulation results show that the soft approach improves the signal-to-noise ratio by 3 dB and 2.5 dB when the encoding rates are 1/3 and 2/3.

Keywords: Closed-loop systems; Costas Loop; Gaussian noise; Maximum likelihood decoding; Modulation; Viterbi algorithm (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s11235-021-00807-9

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