Tsegamlak Terefe (PhD)Adomeas Asfaw2024-07-312024-07-312024-05https://etd.aau.edu.et/handle/123456789/3354The Fifth Generation (5G) wireless technology has significant advancements in communication speed, capacity and latency, revolutionizing various industries and enabling transformative applications. However, these benefits are challenged by the complexities of the wireless environment, characterized by multipath propagation, fading, and interference. This thesis address the challenge of mitigating errors within 5G communication systems. The multipath propagation and fading present in wireless channels often lead to Inter Symbol Interference (ISI) and other forms of distortion. As a mitigation for this cases an autoencoder-based equalizer tailored for 5G communication systems is proposed and thoroughly evaluated. Leveraging the power of deep learning, the autoencoder architecture is adept at extracting complex features from received signals, thus enabling equalization in the presence of channel impairments. Our focus is on mitigating errors within the context of the International Telecommunication Union (ITU) 2020 channel model and Quadrature Amplitude Modulation (QAM) schemes (16-QAM and 64-QAM). Through simulation the performance of the proposed equalizer is assessed using constellation plots, Symbol Error Rate (SER), Bit Error Rate (BER) and convergence rate. Results indicate that the designed autoencoder achieved an SER of approximately 10−4 and a BER of 10−5 for the 16-QAM and an SER of approximately 10−3 and a BER of 10−4 for the 64-QAM. Our comparison analysis reveals the efficacy and competitiveness of the autoencoder-based equalizer in mitigating the effects of the channel for 5G downlink outdoor to indoor communication system.en-USAutoencoder5GITU/IMT 2020 channel modelEqualizationNeural networks.Thesis