Reducing Interference in Wireless Communication Systems Using High-Pass Filtering and Sampling Techniques with Python Implementation

Abstract

Interference remains a significant challenge in modern wireless communication systems, as it degrades signal quality and reduces overall system performance. This research presents an approach to mitigating interference by combining high-pass filtering and adaptive sampling techniques, implemented using Python. The primary objective of the study is to enhance signal clarity and communication reliability by effectively isolating the desired signal from unwanted interference. The methodology involves applying a high-pass filter to remove low-frequency noise and varying the number of sampling points per pulse—specifically testing 10, 50, and 100 samples. The results demonstrate that increasing the sampling resolution leads to improved signal reconstruction, with matching rates observed at approximately 40%, 70%, and 90%, respectively. These findings confirm that optimal sampling plays a critical role in detecting and suppressing interference. Moreover, the study highlights the potential integration of machine learning algorithms to dynamically adjust sampling strategies in real-time environments, further enhancing interference suppression efficiency. The proposed approach not only improves the quality of the received signal but also lays the groundwork for more intelligent and adaptive communication systems. Future research could explore the use of deep learning models for automatic interference classification and mitigation, as well as the implementation of adaptive filtering methods that respond to environmental changes. This work contributes to advancing the reliability and scalability of wireless communication networks, particularly in high-demand applications such as 5G and the Internet of Things (IoT), where maintaining signal integrity is crucial for performance and user experience.