Symbol detection using the differential evolution algorithm in MIMO-OFDM systems

Seyman M. N., TAŞPINAR N.

TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES, cilt.21, sa.2, ss.373-380, 2013 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 21 Sayı: 2
  • Basım Tarihi: 2013
  • Doi Numarası: 10.3906/elk-1103-16
  • Dergi Adı: TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, TR DİZİN (ULAKBİM)
  • Sayfa Sayıları: ss.373-380
  • Anahtar Kelimeler: Differential evolution, particle swarm optimization, genetic algorithm, maximum likelihood algorithm, MIMO-OFDM, symbol detection, CHANNEL ESTIMATION, JOINT DATA, OPTIMIZATION
  • Erciyes Üniversitesi Adresli: Evet

Özet

Channel estimation and symbol detection in multiple-input and multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM) systems are essential tasks. Although the maximum likelihood (ML) detector reveals excellent performance for symbol detection, the computational complexity of this algorithm is extremely high in systems with more transmitter antennas and high-order constellation size. In this paper, we propose the differential evolution (DE) algorithm in order to reduce the search space of the ML detector and the computational complexity of symbol detection in MIMO-OFDM systems. The DE algorithm is also compared to some heuristic approaches, such as the genetic algorithm and particle swarm optimization. According to the simulation results, the DE has the advantage of significantly less complexity and is closer to the optimal solution.