SWAN: Swarm-Based Low-Complexity Scheme for PAPR Reduction

Luis F. Abanto-Leon, Gek Hong Allyson Sim, Matthias Hollick, Amnart Boonkajay, Fumiyuki Adachi

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)

Abstract

Cyclically shifted partial transmit sequences (CS-PTS) has conventionally been used in SISO systems for PAPR reduction of OFDM signals. Compared to other techniques, CS-PTS attains superior performance. Nevertheless, due to the exhaustive search requirement, it demands excessive computational complexity. In this paper, we adapt CS-PTS to operate in a MIMO framework, where singular value decomposition (SVD) precoding is employed. We also propose SWAN, a novel optimization method based on swarm intelligence to circumvent the exhaustive search. SWAN not only provides a significant reduction in computational complexity, but it also attains a fair balance between optimality and complexity. Through simulations, we show that SWAN achieves near-optimal performance at a much lower complexity than other competing approaches.

Original languageEnglish
Article number9322272
JournalProceedings - IEEE Global Communications Conference, GLOBECOM
DOIs
Publication statusPublished - 2020
Event2020 IEEE Global Communications Conference, GLOBECOM 2020 - Virtual, Taipei, Taiwan, Province of China
Duration: 2020 Dec 72020 Dec 11

Keywords

  • artificial intelligence.
  • MIMO
  • OFDM
  • PAPR reduction
  • swarm intelligence

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