# Optimization of Signal Detection Using Deep CNN in Ultra-Massive MIMO
###### Abstract  
This paper addresses the evolving landscape of communication technology, emphasizing the pivotal role of 5G and the emerging 6G networks in accommodating the increasing demand for high-speed and accurate data transmission. We delve into the advancements in 5G technology, particularly the implementation of millimeter wave (mmWave) frequencies ranging from 30 to 300 GHz. These advancements are instrumental in enhancing applications requiring massive data transmission and reception, facilitated by massive MIMO (multiple input multiple output) systems. Looking towards the future, this paper forecasts the necessity for faster data transmission technologies, shifting the focus toward the development of 6G networks. These future networks are projected to employ ultra-massive MIMO systems in the terahertz band, operating within 0.1-10 THz frequency ranges. A significant part of our research is dedicated to exploring advanced signal detection techniques, helping to mitigate the impact of interference and improve accuracy in data transmission and enabling more efficient communication, even in environments with high levels of noise, and including zero forcing (ZF) and minimum mean square error (MMSE) methods, which form the cornerstone of our proposed approach. Additionally, signal detection contributes to the development of new communication technologies such as 5G and 6G, which require a high data transmission efficiency and rapid response speeds. The core contribution of this study lies in the application of deep learning to signal detection in ultra-massive MIMO systems, a critical component of 6G technology. We compare this approach with existing ELMx-based machine learning methods, focusing on algorithmic efficiency and computational performance. Our comparative analysis included the regularized extreme learning machine (RELM) and the outlier robust extreme learning machine (ORELM), juxtaposed with ZF and MMSE methods. Simulation results indicated the superiority of our convolutional neural network for signal detection (CNN-SD) over the traditional ELMx-based, ZF, and MMSE methods, particularly in terms of channel capacity and bit error rate. Furthermore, we demonstrate the computational efficiency and reduced complexity of the CNN-SD method, underscoring its suitability for future expansive MIMO systems. signal detection; ELM; deep learning; ultra-massive MIMO 2024  
Internet of Things (IoT). However, research and development into 6G are ongoing, aiming to support more connected devices and higher capacities, while providing faster data rates and lower latency than 5G [6]. Additionally, 6G seeks to enhance communication security and reliability and may introduce new applications, such as holographic communication. Technologies for AI and autonomous cars are also being developed in anticipation of 6G. Compared to 5G, 6G is expected to increase data rates by 10 to 100 times, supporting up to Tb/s and 10 Gb/s user experience data rates. Moreover, 6G could use flexible frequency-sharing technology, to optimize frequency reuse. Ultra-massive MIMO technology, a crucial aspect of 6G's future, can be categorized into four main areas: frequency bands, transmission mode, intelligent transmission, and integrated network. As mentioned, 6G will support the use of terahertz frequencies ranging from 0.1 THz to 10 THz, which will aid in the development of systems and meet future needs such as the application of AI, medical devices, or autonomous driving systems that require lower latency and higher precision. This also includes high-speed internet access, allowing everyone to access increased communication resources [7, 8]. In order to overcome limitations and highlight differences in wireless communications, signal detection techniques like zero forcing (ZF) and MMSE (minimum mean square error) are utilized, along with deep learning techniques [9, 10]. Current 5G technology aims to provide higher data rates and lower latency than 4G, accommodating new applications like virtualization and more connected devices.