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Journals / Turkish Journal of Electrical Engineering and Computer Science / 2013 / Cilt: 21 - Sayı: 2

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

Muhammet Nuri SEYMAN
Necmi TAŞPINAR
Pages
373–380
Publication Date
2013-02-01
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

According to the simulation results, the DE has the advantage of significantly less complexity and is closer to the optimal solution.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Channel estimation and symbol detection in multiple-input and multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM) systems are essential tasks.
📊
Key Findings & Data: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.
💡
Conclusion & Impact:According to the simulation results, the DE has the advantage of significantly less complexity and is closer to the optimal solution.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Differential evolution · particle swarm optimization · genetic algorithm

Abstract

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.

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.

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Cite This Article

SEYMAN, M. N., TAŞPINAR, N. (2013). Symbol detection using the differential evolution algorithm in MIMO-OFDM systems. Turkish Journal of Electrical Engineering and Computer Science, Cilt: 21 - Sayı: 2, 373–380. https://ajindex.com/articles/188732

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