256 research outputs found
Model-Driven Sensing-Node Selection and Power Allocation for Tracking Maneuvering Targets in Perceptive Mobile Networks
Maneuvering target tracking will be an important service of future wireless
networks to assist innovative applications such as intelligent transportation.
However, tracking maneuvering targets by cellular networks faces many
challenges. For example, the dense network and high-speed targets make the
selection of the sensing nodes (SNs), e.g., base stations, and the associated
power allocation very difficult, given the stringent latency requirement of
sensing applications. Existing methods have demonstrated engaging tracking
performance, but with very high computational complexity. In this paper, we
propose a model-driven deep learning approach for SN selection to meet the
latency requirement. To this end, we first propose an iterative SN selection
method by jointly exploiting the majorization-minimization (MM) framework and
the alternating direction method of multipliers (ADMM). Then, we unfold the
iterative algorithm as a deep neural network (DNN) and prove its convergence.
The proposed model-driven method has a low computational complexity, because
the number of layers is less than the number of iterations required by the
original algorithm, and each layer only involves simple matrix-vector
additions/multiplications. Finally, we propose an efficient power allocation
method based on fixed point (FP) water filling (WF) and solve the joint SN
selection and power allocation problem under the alternative optimization
framework. Simulation results show that the proposed method achieves better
performance than the conventional optimization-based methods with much lower
computational complexity
Remote Human Vital Sign Monitoring Using Multiple-Input Multiple-Output Radar at Millimeter-Wave Frequencies
Non-contact respiration rate (RR) and heart rate (HR) monitoring using millimeter-wave (mmWave) radars has gained lots of attention for medical, civilian, and military applications. These mmWave radars are small, light, and portable which can be deployed to various places. To increase the accuracy of RR and HR detection, distributed multi-input multi-output (MIMO) radar can be used to acquire non-redundant information of vital sign signals from different perspectives because each MIMO channel has different fields of view with respect to the subject under test (SUT). This dissertation investigates the use of a Frequency Modulated Continuous Wave (FMCW) radar operating at 77-81 GHz for this application. Vital sign signal is first reconstructed with Arctangent Demodulation (AD) method using phase change’s information collected by the radar due to chest wall displacement from respiration and heartbeat activities. Since the heartbeat signals can be corrupted and concealed by the third/fourth harmonics of the respiratory signals as well as random body motion (RBM) from the SUT, we have developed an automatic Heartbeat Template (HBT) extraction method based on Constellation Diagrams of the received signals. The extraction method will automatically spot and extract signals’ portions that carry good amount of heartbeat signals which are not corrupted by the RBM. The extracted HBT is then used as an adapted wavelet for Continuous Wavelet Transform (CWT) to reduce interferences from respiratory harmonics and RBM, as well as magnify the heartbeat signals. As the nature of RBM is unpredictable, the extracted HBT may not completely cancel the interferences from RBM. Therefore, to provide better HR detection’s accuracy, we have also developed a spectral-based HR selection method to gather frequency spectra of heartbeat signals from different MIMO channels. Based on this gathered spectral information, we can determine an accurate HR even if the heartbeat signals are significantly concealed by the RBM. To further improve the detection’s accuracy of RR and HR, two deep learning (DL) frameworks are also investigated. First, a Convolutional Neural Network (CNN) has been proposed to optimally select clean MIMO channels and eliminate MIMO channels with low SNR of heartbeat signals. After that, a Multi-layer Perceptron (MLP) neural network (NN) is utilized to reconstruct the heartbeat signals that will be used to assess and select the final HR with high confidence
Vital Sign Monitoring in Dynamic Environment via mmWave Radar and Camera Fusion
Contact-free vital sign monitoring, which uses wireless signals for
recognizing human vital signs (i.e, breath and heartbeat), is an attractive
solution to health and security. However, the subject's body movement and the
change in actual environments can result in inaccurate frequency estimation of
heartbeat and respiratory. In this paper, we propose a robust mmWave radar and
camera fusion system for monitoring vital signs, which can perform consistently
well in dynamic scenarios, e.g., when some people move around the subject to be
tracked, or a subject waves his/her arms and marches on the spot. Three major
processing modules are developed in the system, to enable robust sensing.
Firstly, we utilize a camera to assist a mmWave radar to accurately localize
the subjects of interest. Secondly, we exploit the calculated subject position
to form transmitting and receiving beamformers, which can improve the reflected
power from the targets and weaken the impact of dynamic interference. Thirdly,
we propose a weighted multi-channel Variational Mode Decomposition (WMC-VMD)
algorithm to separate the weak vital sign signals from the dynamic ones due to
subject's body movement. Experimental results show that, the 90
percentile errors in respiration rate (RR) and heartbeat rate (HR) are less
than 0.5 RPM (respirations per minute) and 6 BPM (beats per minute),
respectively
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