Performance Analysis of Blockage Detection in 6G Wireless Networks

This paper presents a proactive blockage detection algorithm and performance for sub-terahertz (sub-THz) communication systems. It is a critical technology for next-generation wireless networks. Human body blockage is a significant challenge for millimeter wave and sub-THz systems. It often causes severe signal degradation and connectivity loss. Current solutions, such as time-domain approaches or machine learning models. These methods suffer from high computational complexity, limited accuracy and an inability to detect blockages before they occur. The proposed algorithm overcomes these limitations by applying spectral-domain analysis using the short-time Fourier transform (STFT). It provides precise detection of early blockage conditions. The blockage detection algorithm adopts a simple and threshold-based detection mechanism. The algorithm operates in three stages: initialization, active monitoring, and blockage recovery. The proposed algorithm demonstrates a detection probability of 81% and highlights its effectiveness in reliable detection. It detects blockages at least before their occurrence. Additionally, the algorithm achieves a higher mean time to blockage of up to 150ms under optimal parameter settings. These findings highlight the algorithm's effectiveness in mitigating connectivity issues in dense indoor deployments. It is adaptable to different network configurations and is suitable for various deployment scenarios. The proposed solution is well-suited for ultra-reliable low-latency communications, augmented reality, autonomous vehicles, and industrial IoT. These outcomes mark a key step toward seamless 6G communication.