A Spectral-Domain Approach for Early Blockage Detection in Sub-Terahertz Wireless Networks
محورهای موضوعی : Communication Systems & Devices
Neravati Nagaraja Kumar
1
,
Anil Kumar
2
,
Subba Raju MP
3
,
Kalyani Kapula
4
,
Surya Kala Nagireddi
5
,
Yarrapragada Rao K. S. S.
6
1 - Department of Electronics and Communication Engineering, Rajeev Gandhi Memorial College of Engineering and Technology, Nandyal, India
2 - Department of Electronics and Communication Engineering, Aditya University, Surampalem, India
3 - Department of Electrical and Electronics Engineering, Aditya University, Surampalem, India
4 - Department of Electronics and Communication Engineering, Aditya University, Surampalem, India
5 - Department of Information Technology, Aditya University, Surampalem, India
6 - Department of Mechanical Engineering, Aditya University, Surampalem, India
کلید واژه: Sub-Terahertz Communication, Human Body Blockage, Proactive Blockage Detection, Short-Time Fourier Transform, Spectral-Domain Analysis, Threshold-Based Detection Algorithm,
چکیده مقاله :
The paper presents the problem of human body blockage in sub-terahertz and millimeter wave wireless systems. These communication systems are operated at very high frequencies. The generated high frequency signals are very sensitive to obstacles like the human body. A blockage event abruptly reduces the received signal power. This causes a loss of signal connection between transmitter and receiver. To avoid this problem, the network detects a blockage before it actually happens. Many existing solutions use machine learning models in the time domain. These methods are complex. Long training times are required. The signal shows clear oscillations just before a blockage happens. These oscillations are weak in the time domain. The paper uses the short-time Fourier transform to extract spectral features from the received signal. The difference between normal conditions and pre-blockage conditions becomes very large. In some cases, the gap reaches two orders of magnitude. Based on this observation, a threshold-based proactive blockage detection algorithm is designed. The algorithm is simple and does not rely on machine learning. In order to solve this, MATLAB simulation environment is assumed with parameter values and data collected at 156~GHz in an indoor environment. The performance is measured using blockage detection probability, mean time to blockage and false alarm rate. The paper explains the spectral analysis offers a simple and effective way to enable proactive blockage detection for future sub-terahertz wireless networks.
The paper presents the problem of human body blockage in sub-terahertz and millimeter wave wireless systems. These communication systems are operated at very high frequencies. The generated high frequency signals are very sensitive to obstacles like the human body. A blockage event abruptly reduces the received signal power. This causes a loss of signal connection between transmitter and receiver. To avoid this problem, the network detects a blockage before it actually happens. Many existing solutions use machine learning models in the time domain. These methods are complex. Long training times are required. The signal shows clear oscillations just before a blockage happens. These oscillations are weak in the time domain. The paper uses the short-time Fourier transform to extract spectral features from the received signal. The difference between normal conditions and pre-blockage conditions becomes very large. In some cases, the gap reaches two orders of magnitude. Based on this observation, a threshold-based proactive blockage detection algorithm is designed. The algorithm is simple and does not rely on machine learning. In order to solve this, MATLAB simulation environment is assumed with parameter values and data collected at 156~GHz in an indoor environment. The performance is measured using blockage detection probability, mean time to blockage and false alarm rate. The paper explains the spectral analysis offers a simple and effective way to enable proactive blockage detection for future sub-terahertz wireless networks.
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