Artificial intelligence has transformed multiple input multiple output (MIMO) technology into a promising candidate for six-generation networks. However, several interference signals impact the data transmission between various antennas; therefore, sophisticated signal detection techniques are required at the MIMO receiver to estimate the transmitted data. This paper presents an optimized AI-based signal detection technique called AIDETECT for MIMO systems. The proposed AIDETECT network model is developed based on an optimized deep neural network (DNN) architecture, whose efficiency lies in its lightweight network architecture. To train and test the AIDETECT network model, we generate and process the data in a suitable form based on the transmitted signal, channel information, and noise. Based on this data, we calculate the received signal at the receiver end, where the received signal and channel information were integrated into the AIDETECT network model to perform reliable signal detection. Simulation results show that at a 20-dB signal-to-noise ratio (SNR), the proposed AIDETECT technique achieves between 97.33% to 99.99% better performance compared to conventional MIMO detectors and is also able to accomplish between 25.34% to 99.98% better performance than other AI-based MIMO detectors for the considered performance metrics. In addition, due to lightweight network architecture, the proposed AIDETECT technique has also achieved much lower computational complexity than conventional and AI-based MIMO detectors.

This paper investigates the performance of deep neural network (DNN)-based signal detection in multiple input, multiple output (MIMO), communication systems. MIMO technology plays a critical role in achieving high data rates and improved capacity in modern wireless communication standards like 5G. However, signal detection in MIMO systems presents significant challenges due to channel complexities. This study conducts a comparative analysis of signal detection techniques within both the single input, single output (SISO), and MIMO frameworks. The analysis focuses on the entire transmission chain, encompassing transmitters, channels, and receivers. The effectiveness of three traditional methods—maximum likelihood detection (MLD), minimum mean square error (MMSE), and zero-forcing (ZF)—is meticulously evaluated alongside a novel DNN-based approach. The proposed study presents a novel DNN-based signal detection model. While this model demonstrates superior computational efficiency and symbol error rate (SER) performance compared to more conventional techniques like MLD, MMSE, and ZF in the context of a SISO system, MIMO systems face some challenges in outperforming the conventional techniques specifically in terms of computation times. This complexity of MIMO systems presents challenges that the current DNN design has yet to fully address, indicating the need for further developments in wireless communication technology. The observed performance difference between SISO and MIMO systems underscores the need for further research on the adaptability and limitations of DNN architectures in MIMO contexts. These findings pave the way for future explorations of advanced neural network architectures and algorithms specifically designed for MIMO signal-processing tasks. By overcoming the performance gap observed in this work, such advancements hold significant promise for enhancing the effectiveness of DNN-based signal detection in MIMO communication systems.

Artificial intelligence (AI) is revolutionizing multiple-input-multiple-output (MIMO) technology, making it a promising contender for the coming sixth-generation (6G) and beyond-fifth-generation (B5G) networks. However, the detection process in MIMO systems is highly complex and computationally demanding. To address this challenge, this paper presents an optimized AI-based signal detection method known as AIDETECT-2 which is based on feed forward neural network (FFNN) for MIMO systems. The proposed AIDETECT-2 network model demonstrates superior efficiency in signal detection in comparison with conventional and AI-based MIMO detection methods, particularly in terms of symbol error rate (SER) at various signal-to-noise ratios (SNR). This paper thoroughly explores various signal detection aspects using FFNN, including the design of system architecture, preparation of data, training processes of the network model, and performance evaluation. Simulation results show that the proposed model demonstrates a significant performance improvement ranging between 13.75% to 99.995% better SER compared to the best conventional method and also achieved between 56.52% to 97.69 better SER compared to benchmark AI-based MIMO detectors at 20 dB SNR for given MIMO scenarios respectively. It also presented the computational complexity analysis of different conventional and AI-based MIMO detectors. We believe that this optimized AI-based network model can serve as a comprehensive guide for deploying deep-learning (DL) neural networks for signal detection in the forthcoming 6G wireless networks.

Artificial intelligence (AI) has emerged as a transformative technology in the evolution of massive multiple-input multiple-output (ma-MIMO) systems, positioning them as a cornerstone for sixth-generation (6G) wireless networks. Despite their significant potential, ma-MIMO systems face critical challenges at the receiver end, particularly in signal detection under high-dimensional and noisy environments. To address these limitations, this paper proposes MIMONet, a novel deep learning (DL)-based MIMO detection framework built upon a lightweight and optimized feedforward neural network (FFNN) architecture. MIMONet is specifically designed to achieve a balance between detection performance and complexity by optimizing the neural network architecture for MIMO signal detection tasks. Through extensive simulations across multiple MIMO configurations, the proposed MIMONet detector consistently demonstrates superior bit error rate (BER) performance. It achieves a notably lower error rate compared to conventional benchmark detectors, particularly under moderate to high signal-to-noise ratio (SNR) conditions. In addition to its enhanced detection accuracy, MIMONet maintains significantly reduced computational complexity, highlighting its practical feasibility for advanced wireless communication systems. These results validate the effectiveness of the MIMONet detector in optimizing detection accuracy without imposing excessive processing burdens. Moreover, the architectural flexibility and efficiency of MIMONet lay a solid foundation for future extensions toward large-scale ma-MIMO configurations, paving the way for practical implementations in beyond-5G (B5G) and 6G communication infrastructures.

The revolution of Artificial Intelligence (AI) transforms the Multiple Input Multiple Output (MIMO) technology into Massive MIMO (Ma-MIMO) technology. However, despite the promising benefits of Ma-MIMO technology, it is very difficult to design a reliable and energy-efficient detector at the receiver end. To overcome this research challenge, this paper presents a new deep Ma-MIMO Detection (DM-DETECT) scheme for Ma-MIMO detection. The DM-DETECT uses deep learning (DL) to construct an AI-based network model. The DM-DETECT model is extensively trained and optimized to achieve high performance in realistic MIMO and Ma-MIMO use cases. Simulation results depict that the DM-DETECT performs better at a certain limit than the conventional detectors in terms of Symbol Error Rate (SER). Moreover, this study presents an in-depth analysis of the activation functions for deep neural networks at different SNR ranges, which offers valuable insights into optimizing the performance of the proposed DM-DETECT for Ma-MIMO technology.