Majorization–Minimization Based Direct Localization Using One-Bit Channel Measurements

Research output: Contribution to journalArticleScientificpeer-review

Abstract

Direct localization or direct position determination (DPD) can outperform the more traditional angle and delay estimation based approaches, yet being less used in practice due to the requirement of aggregating raw data or measurements to a single processing point. To reduce the network burden, this paper considers one-bit quantized channel response data, and proposes a majorization–minimization (MM) based one-bit DPD (MO-DPD) algorithm to localize an orthogonal frequency division multiplexing (OFDM) signal source. First, the one-bit DPD is formulated as a maximum likelihood (ML) estimation problem, which is then iteratively solved using the MM approach. The proposed MO-DPD avoids iteratively estimating any nuisance parameters, leading to high computational efficiency. The numerical results show that the MO-DPD outperforms the baseline one-bit ML solver in terms of computational load, while efficiently converging to one-bit Cramér-Rao lower bound (CRLB) over wide range of signal-to-noise ratios (SNRs). Furthermore, we show that no more than three iterations are required to achieve high accuracy.

Original languageEnglish
JournalIEEE Wireless Communications Letters
DOIs
Publication statusE-pub ahead of print - 29 Feb 2024
Publication typeA1 Journal article-refereed

Keywords

  • Direct position determination
  • Location awareness
  • majorization–minimization
  • maximum likelihood estimation
  • Maximum likelihood estimation
  • OFDM
  • one-bit quantization
  • orthogonal frequency division multiplexing
  • Signal to noise ratio
  • Task analysis
  • Telecommunication traffic
  • Vectors
  • wireless localization

Publication forum classification

  • Publication forum level 2

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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