AIDETECT - AI-based Integratable Detection for Beyond 5G Networks
Muhammad Yunis Daha, Joseph Rafferty, Muhammad Ikram Ashraf, Muhammad Usman Hadi · 2023
The integration of Artificial Intelligence (AI) technologies has revolutionized wireless communication in Multiple Input Multiple Output (MIMO) systems. However, signal detection remains a challenging task, and traditional algorithms have shown limited success in Massive MIMO communication systems. To address this, a lightweight AI-based integratable detection scheme called AIDETECT has been proposed in this paper. The AIDETECT model is trained and optimized to achieve superior performance in realistic MIMO and Massive MIMO use case scenarios, and extensive simulations have been conducted to evaluate its effectiveness of AIDETECT. The results of this evaluation demonstrate that the proposed AIDETECT scheme outperforms conventional detectors in terms of Symbol Error Rate (SER) for a certain range of SNR. Additionally, this study provides a detailed analysis of the optimal number of deep neural network hidden size layers for different SNR ranges, providing valuable insight into optimizing the performance of lightweight AI models for MIMO and Massive MIMO systems. This paper underscores the potential of deep learning techniques in addressing signal detection challenges and informs future research in this field.