MIMO Detection Optimized Neural Network is a M.Tech project topic for Electronics & Communication Engineering. It gives students a clear starting point for research, implementation planning, and documentation.
MIMO Detection Optimized Neural Network Project Details
| Abstract |
In 5G wireless systems, Multiple‑Input Multiple‑Output (MIMO) technology is essential for boosting spectral efficiency and data rates. In the uplink of millimeter‑wave (mm‑wave) networks, however, strong multi‑user interference often overwhelms standard linear detectors like Maximum Ratio (MR) and Minimum Mean Square Error (MMSE). To overcome this problem, we propose a non‑linear MIMO detector based on an Optimized Extreme Learning Machine Neural Network (OELMNN). The detector uses the Adaptive Rat Optimization (ARO) algorithm to fine‑tune the input weights and biases of the Extreme Learning Machine (ELM). This avoids getting stuck in local minima and speeds up training. We evaluate the OELMNN detector through simulations and compare it with traditional linear detectors.
The comparison looks at key communication metrics such as Bit Error Rate (BER), Mean Squared Error (MSE), Signal‑to‑Noise Ratio (SNR), and overall spectral efficiency. The results show that this machine‑learning‑assisted approach offers a strong, high‑performance solution for next‑generation wireless receivers.
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| Reference Paper |
MIMO Detection using Optimized Neural Network model for 5G Communication |
| Domain |
Electronics & Communication Engineering |
| Sub-Domain |
Communication Systems / Wireless Communications / MIMO |
| PDF Download |
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