Evaluation of 5G and Fixed-Satellite Service Earth Station (FSS-ES) Downlink Interference Based on Artificial Neural Network Learning Models (ANN-LMS)  
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important advancement in mobile broadband. A considerable number of countries had already deployed 5G services by 2020. Increased efficiency and performance provides new user information and links to new industries. Recent industries, such as universal surroundings connected to the vast Internet of Things and vehicle communication, have emerged. The industries demand ultralow latency and extreme reliability of 5G mobile communication access. Accordingly, Malaysia has reserved frequencies in the 3.5 GHz range in the C-band and 29.5 GHz in the mmWave band [1]. The 5G 3.5 GHz network is currently in use even in developed countries. However, more bandwidths may be needed with the increase in the volume of mobile traffic. Additional frequency resources are required to meet the demands of 5G. Therefore, the International Telecommunication Union Radio (ITU-R) Regulations designated these frequency resources for the frequency allocation table for the downlink (DL) of the fixed-satellite service Earth station (FSS-ES) and mobile communications services [2; 3; 4; 5]. On this basis, the interference effects-based field test and simulations by using probability or sharing between radio systems operating in co-channel and adjacent channel interferences should be examined to minimise the interference effect and address the limited frequency resources. The Electronic Communications Committee evaluated the existing block edge mask requirements for 5G through an FSS and radar coexistence study conducted at the European Conference of Postal and Telecommunication Administrations. Specific to the range of 3.55 GHz to 3.65 GHz, we examined how 5G base stations (BSs) affect the FSS and ESs. The simulation results based on ITU-R M. 2101 and ITU-R P.452-16 were used to analyse two instances where 5G-BS and FSS-ES interfered with the co-channel and adjacent channels. However, the 5G-BS interference is observed when the DL is considerable. This study used measurements and simulations to design the optimal exclusive zone. The co-channel interference of 5G-BS and FSS-ES was investigated [6; 7]. In the literature, Monte Carlo (MC) simulations were used to evaluate the protection distance for 5G-BS and FSS-ES [8; 9]. A frequency overlap between 5G-BS and FSS-ES (3.4-3.8 GHz) causes interference. **The main contributions of this study are as follows:**  
1. A 5G model was created using data collected during a measuring campaign in Malaysia. 2. The model's exclusion zones are based on geographical realities and useful considerations. 3. The paper focuses on minimising excessive interference, lowering designation error in the model, and enhancing comparison rates. 4. In this paper, novel machine learning approaches are used. The radial basis function neural network (RBFNN) and the general regression neural network (GRNN) are used to create multi-objective comparative learning models. 5. The machine learning model has been improved to significantly lessen interference during training. Future 5G technologies, such as 5G-NR, are intended to improve the capabilities and performance of wireless networks. These advancements may result in the introduction of new features and techniques that alter the interference landscape. 5G-NR, for example, incorporates advanced beamforming and massive Multiple-Input Multiple-Output (MIMO) technologies, which facilitate highly directional and focused transmission. This has the potential to reduce interference to some degree, as the transmission can be more effectively isolated from satellite signals. Future 5G technologies may also include enhanced spectrum management techniques, adaptive modulation and coding schemes, and sophisticated interference cancellation algorithms.