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Abstract-Multiple-input multiple-output (MIMO) precoding scheme is developed for time division duplex (TDD) systems in a multicell environment. The proposed scheme is designed to maximize the total achievable rate, and to work in the decentralized manner with only locally available channel state information (CSI) at each transmitter. We first establish and solve a decentralized optimization problem for the case of multiple-input singleoutput (MISO) channels. We introduce a new precoding design metric called signal to generating interference plus noise ratio (SGINR). Based on the SGINR metric, the MISO precoding scheme is extended to general MIMO channels. Simulation results confirm that proposed precoding scheme offers significant throughput enhancement in multicell environments.

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Blood pressure (BP) is a vital sign that provides fundamental health information regarding patients. Continuous BP monitoring is important for patients with hypertension. Various studies have proposed cuff-less BP monitoring methods using pulse transit time. We propose an end-to-end deep learning architecture using only raw signals without the process of extracting features to improve the BP estimation performance using the attention mechanism. The proposed model consisted of a convolutional neural network, a bidirectional gated recurrent unit, and an attention mechanism. The model was trained by a calibration-based method, using the data of each subject. The performance of the model was compared to the model that used each combination of the three signals, and the model with the attention mechanism showed better performance than other state-of-the-art methods, including conventional linear regression method using pulse transit time (PTT). A total of 15 subjects were recruited, and electrocardiogram, ballistocardiogram, and photoplethysmogram levels were measured. The 95% confidence interval of the reference BP was [86.34, 143.74] and [51.28, 88.74] for systolic BP (SBP) and diastolic BP (DBP), respectively. The R 2 values were 0.52 and 0.49, and the mean-absolute-error values were 4.06 ± 4.04 and 3.33 ± 3.42 for SBP and DBP, respectively. In addition, the results complied with global standards. The results show the applicability of the proposed model as an analytical metric for BP estimation.

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