Sparse-DFT and WHT Precoding with Iterative Detection for Highly Frequency-Selective Channels

Roberto Bomfin, Marwa Chafii

Research output: Contribution to journalArticlepeer-review

Abstract

Various precoders have been recently studied by the wireless community to combat the channel fading effects. Two prominent precoders are implemented with the discrete Fourier transform (DFT) and Walsh-Hadamard transform (WHT). The WHT precoder is implemented with less complexity since it does not need complex multiplications. Also, spreading can be applied sparsely to decrease the transceiver complexity, leading to sparse DFT (SDFT) and sparse Walsh-Hadamard (SWH). Another relevant topic is the design of iterative receivers that deal with inter-symbol-interference (ISI). In particular, many detectors based on expectation propagation (EP) have been proposed recently for channels with high levels of ISI. An alternative is the maximum a-posterior (MAP) detector, although it leads to unfeasible high complexity in many cases. In this paper, we provide a relatively low-complexity computation of the MAP detector for the SWH. We also propose two feasible methods based on the Log-MAP and Max-Log-MAP. Additionally, the DFT, SDFT, and SWH precoders are compared using an EP-based receiver with one-tap FD equalization. Lastly, SWH-Max-Log-MAP is compared to the (S)DFT with EP-based receiver in terms of performance and complexity. The results show that the proposed SWH-Max-Log-MAP has a better performance and complexity trade-off for QPSK and 16-QAM under highly selective channels, but has unfeasible complexity for higher QAM orders.

Original languageEnglish (US)
Pages (from-to)4083-4096
Number of pages14
JournalIEEE Transactions on Wireless Communications
Volume23
Issue number5
DOIs
StatePublished - May 1 2024

Keywords

  • DFT-spread-OFDM
  • Single carrier
  • Walsh Hadamard
  • expectation propagation
  • maximum a-posteriori estimation

ASJC Scopus subject areas

  • Applied Mathematics
  • Electrical and Electronic Engineering
  • Computer Science Applications

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