Abstract-Shaping the pulse of FilterBank MultiCarrier with Offset Quadrature Amplitude Modulation subcarrier modulation (FBMC-OQAM) systems offers a new degree of freedom for the design of mobile communication systems. In previous studies, we evaluated the gains arising from the application of Prototype Filter Functions (PFFs) and subcarrier spacing matched to the delay and Doppler spreads of doubly dispersive channels. In this paper, we investigate the impact of having imperfect channel knowledge at the receiver on the performance of Channel Adaptive Modulation (CAM) in terms of channel estimation errors and Bit Error Rate (BER). To this end, the channel estimation error for two different interference mitigation schemes proposed in the literature is derived analytically and its influence on the BER performance is analyzed for practical channel scenarios. The results show that FBMC-OQAM systems utilizing CAM and scattered pilotbased channel estimation provide a significant performance gain compared with the current one system design for a variety of channel scenarios ("one-fits-all") approach. Additionally, we verified that the often used assumption of a flat channel in the direct neighborhood of a pilot symbol is not valid for practical scenarios.
Reliability and user compliance of the applied sensor system are two key issues of digital healthcare and biomedical informatics. For gait assessment applications, accurate joint angle measurements are important. Inertial measurement units (IMUs) have been used in a variety of applications and can also provide significant information on gait kinematics. However, the nonlinear mechanism of human locomotion results in moderate estimation accuracy of the gait kinematics and thus joint angles. To develop “digital twins” as a digital counterpart of body lower limb joint angles, three-dimensional gait kinematic data were collected. This work investigates the estimation accuracy of different neural networks in modeling lower body joint angles in the sagittal plane using the kinematic records of a single IMU attached to the foot. The evaluation results based on the root mean square error (RMSE) show that long short-term memory (LSTM) networks deliver superior performance in nonlinear modeling of the lower limb joint angles compared to other machine learning (ML) approaches. Accordingly, deep learning based on the LSTM architecture is a promising approach in modeling of gait kinematics using a single IMU, and thus can reduce the required physical IMUs attached on the subject and improve the practical application of the sensor system.
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