Performance Analysis of MIMO-OFDM System for Image Transmission

Abstract

Reliable image transmission over wireless channels is difficult due to the presence of multipath fading, noise, and limited bandwidth. MIMO-OFDM has proven to be an effective solution to these challenges by jointly exploiting spatial diversity and frequency-selective transmission. In this work, a MIMO-OFDM-based image transmission system is designed and evaluated using MATLAB simulations. The input image is converted into a binary bitstream, encoded, modulated, and transmitted over fading wireless channels. At the receiver, channel equalization and decoding techniques are applied to recover the transmitted image. System performance is assessed in terms of bit error rate (BER), reconstructed image quality, and robustness under additive white Gaussian noise and Rayleigh fading conditions. Simulation results demonstrate that the proposed MIMO-OFDM framework achieves improved BER performance and enhanced visual image quality, as reflected by higher PSNR values.

Introduction

Wireless multimedia applications like telemedicine, remote sensing, and surveillance demand reliable, high-quality image transmission. However, wireless channel impairments—noise, multipath fading, and Doppler effects—degrade image quality, increase bit errors, and reduce visual fidelity.

Technologies Used:

• OFDM (Orthogonal Frequency Division Multiplexing): Efficiently combats frequency-selective fading and spectrum limitations but is insufficient alone under severe fading.

• MIMO (Multiple-Input Multiple-Output): Uses multiple transmit and receive antennas to exploit spatial diversity, improving reliability, capacity, and BER performance.

• Space-Time Block Coding (STBC, e.g., Alamouti): Enhances robustness under fading channels.

System Model:

• Transmitter: Image → binary bitstream → digital modulation (QPSK, 16-QAM, 64-QAM) → OFDM modulation (IFFT, cyclic prefix insertion) → MIMO antenna transmission.

• Channel: Rayleigh fading + AWGN.

• Receiver: CP removal → FFT → channel estimation → MIMO equalization (ZF/MMSE) → demodulation → bitstream reconstruction → image formation. JPEG compression can be applied for efficiency.

Performance Metrics:

• Bit Error Rate (BER): Probability of bit errors vs. SNR.

• Peak Signal-to-Noise Ratio (PSNR): Quantifies reconstructed image quality.

Simulation:

• MATLAB-based simulations using 2×2 and 4×4 MIMO configurations.

• Tested modulation schemes: QPSK, 16-QAM, 64-QAM.

• Channel coding: Rate-1/2 convolutional coding.

• Wireless channel: Rayleigh fading + AWGN.

• Results evaluated for BER, PSNR, and visual quality over SNR range 0–40 dB.

Key Findings:

• MIMO-OFDM significantly improves image transmission quality over fading channels.

• Spatial diversity and STBC reduce BER and enhance reconstructed image PSNR.

• The approach links physical-layer impairments directly to application-layer image quality, highlighting practical benefits for real-time wireless multimedia systems.

Conclusion

This paper presented a MIMO–OFDM–based image transmission framework capable of achieving reliable reconstruction performance over AWGN and frequency-selective Rayleigh fading channels. By integrating convolutional channel coding with Viterbi decoding, space–time block coding for diversity, and MMSE equalization at the receiver, the proposed system effectively mitigates channel impairments and significantly reduces the bit-error rate. Simulation results demonstrate that QPSK modulation provides superior robustness at low SNR regimes, while 16-QAM offers a favourable trade-off between spectral efficiency and reconstruction quality. Although 64-QAM enables higher data throughput, it exhibits increased sensitivity to noise, requiring improved channel conditions for acceptable image quality. The combined BER, PSNR, and visual analyses confirm the effectiveness of the proposed MIMO–OFDM architecture for wireless image transmission. Future work will focus on incorporating adaptive modulation and coding strategies, advanced channel estimation and equalization techniques assisted by machine learning, and powerful forward error-correction schemes such as LDPC or Turbo codes. Furthermore, real-time hardware implementation using FPGA or SDR platforms will be explored to validate the practical feasibility of the proposed system for next-generation 5G/6G multimedia communication applications.

References

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Copyright

Copyright © 2026 Guni Chandana. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.