Cooperative MIMO OFDM system based on Amplify and Forward Relay: Evaluation of ZF-SIC and MMSE-SIC equalization

Abstract

In the world of wireless communication, cooperative communication is a modern and unprecedented approach which enables wireless nodes to transfer the information in a cooperative manner and finally to reach the destination without least possible deterioration. Orthogonal frequency division multiplexing (OFDM) can provide high data rate and better performance and a combination of OFDM and cooperative communication may push a wireless network forward to its next generation with enhanced reliability and efficiency. In this work, we develop a mathematical model for cooperative multiple input multiple output (MIMO) OFDM system. Results are achieved in the study to evaluate and compare bit error rate (BER) for different detection schemes. Based on the results it can be mentioned that the performance of cooperative MIMO-OFDM with space time block code (STBC) under amplify and forward (AF) relay for the MMSE-SIC detection scheme was found very satisfactory with improved signal to noise ratio (SNR). Streszczenie. W artykule przedstawiono model matematyczny kooperacyjnej komunikacji bezprzewodowej wiele wejść, wiele wyjść (MIMO) z multipleksingiem i ortogonalnym podziałem częstotliwości (OFDM). Określono współczynnik BER (bit error rate) dla różnych metod detekcji. Najlepsze parametry otrzymano dla systemu STBC (space time block code). Kooperacyjny system komunikacji bezprzewodowej MIMO OFDM bazujący na układzie Forward Relay: ocena ekwalizacji ZF-SIC i MMSE-SIC

Model-Driven Deep Learning Scheme for Adaptive Transmission in MIMO-SCFDE System

“…When the signal is equalized, the equalization matrix corresponding to the sub-channel is first calculated according to the channel matrix, and then the frequency domain equalization (FDE) on each sub-channel is performed to compensate the frequency selectivity of the channel directly [22]. The commonly used linear equalization methods include zero-forcing (ZF) equalization and minimum mean square error (MMSE) equalization [23]. The ZF receiver can eliminate the MAI by distinguishing the data streams of different antennas, and the equalization matrix can be expressed as…”

Performance Analysis of Combining Beamforming and ZF/MMSE-SIC Equalization Techniques for MIMO DWPT-COFDM Systems

“…Equalization techniques in MIMO and MIMO-OFDM systems, including Zero-Forcing (ZF), Minimum Mean Square Error (MMSE), ZF-Successive Interference Cancellation (ZF-SIC), and MMSE-SIC, have been studied to mitigate ISI and enhance link reliability [24][25][26][27][28][29][30][31]. Spatial signal processing in multiple-antenna wireless devices, like ZF-Beamforming (ZF-BF) and MMSE-Beamforming (MMSE-BF), further enhances system performance [37].…”

Bit Error Rate Performance Analysis of a Multiple Amplify and Forward Relaying Aided Cooperative MIMO-OFDM System

“…For the simplification of implementation, fast Fourier transform is used which is a good choice of OFDM. A MIMO system incorporates multiple input multiple output technology by combining multiple antenna to ensure enhanced bit rate at receiver's end [1], and a cooperative system involves multiple number of nodes to form a virtual array of antenna [2]. That's why the diversity of the transceiver is minimized.…”

“…This approach is especially effective in using diversity gain by involving number of relays into operation, which is extremely useful to mitigate channel fading [3]. Additionally, this system promotes the low usage of power during transmission and improves the spatial diversity gain [1].…”