Original scientific paper

https://doi.org/10.17559/TV-20230502000598

Channel Estimation in MIMO TFT-OFDM Using Hybrid BESOA- CSOA Algorithms

M. Venkatramanan ; E. G. S. Pillay Engineering College, Nagapattinam *
M. Chinnadurai ; Department of Computer Science Engineering, E. G. S. Pillay Engineering College, Nagapattinam

* Corresponding author.

Abstract

In wireless communication systems, maximizing spectral efficiency and enhancing link reliability are key objectives. One effective solution is the combination of Orthogonal Frequency Division Multiplexing (OFDM) and Multiple-Input Multiple-Output (MIMO) techniques. OFDM divides the frequency band into non-overlapping sub-bands, enabling parallel data transmission. This helps overcome the limitations of traditional wireless communication systems with high-rate input data streams. MIMO-OFDM has emerged as a promising technology for achieving high data rates and robustness in wireless communications by supporting multiple inputs and outputs. However, optimizing both spectral efficiency and performance in rapidly fading channels can be challenging. To address these issues, we propose Time Frequency Training Orthogonal Frequency Division Multiplexing (TFT-OFDM). This approach utilizes group pilots for spectrum efficiency and shared channel estimates to maintain system performance stability. Channel estimation plays a critical role in TFT-OFDM, and both Least Square (LS) and Minimum Mean Square Error (MMSE) methods are considered. LS estimation has a simple approach but lower performance, while MMSE significantly reduces mean square error at the cost of computational complexity. To estimate the channel in TFT-OFDM, joint time and frequency channel estimation techniques are employed. This involves using sophisticated algorithms to optimize parameters such as maximum or minimum function values in the solution space. The intelligent algorithms, specifically the Bald Eagle Search Optimization (BESOA) technique and the Cat Swarm Optimization Algorithm (CSOA), are utilized to estimate the channel efficiently in spectrally efficient MIMO-OFDM wireless networks. Performance improvement is observed with the intelligent algorithms. For instance, at a signal-to-noise ratio (SNR) of 15 dB, the proposed BESOA method achieves a Bit Error Rate (BER) of 10-6, while TFT-OFDM with group pilots achieves a BER of 10-3, and conventional CP-OFDM yields a BER of 10-2. Furthermore, the CSOA method outperforms the Firefly Algorithm in terms of BER performance.

Keywords

Bald Eagle Search Optimization technique; Cat Swarm Optimization Algorithm; MIMO; OFDM; Time Frequency Training Orthogonal Frequency Division Multiplexing

Hrčak ID:

312894

URI

https://hrcak.srce.hr/312894

Publication date:

31.12.2023.

Visits: 750 *