Wavelet-based EMG Sensing Interface for Pattern Recognition

Department of Electrical EngineeringAs interest in healthcare and smart devices has increased in recent years, the studies that are sensing and analyzing various bio signals, such as EMG, ECG, and EEG, have been growing. These studies and advances in smart devices have allowed human to increase access to their own physical information. With the physical information, human can diagnose himself or herself. These advances in technology will improve the quality of human life and provide solutions in various fields. The convergence of information and communication technologies has led to the fourth industrial revolution and the development of artificial intelligence, big data and the Internet of Things(IoT) by increasing computing power has led to various data analysis using machine learning. Various fields are moving toward the next level using machine learning, and this trend is also happening in the healthcare field. The era of self-diagnosis begins when medical knowledge, which had previously been entrusted to doctors is passed directly to consumers through big data and machine learning. Thanks to these developments, the healthcare interface, such as front-end integrated chip, is also working to leverage machine learning to deliver various solutions to consumers. Existing papers related to bio signals are focused on reducing power consumption, allowing long-term monitoring or reducing various noise. This paper provides an idea to extend the scope of data processes through machine learning while maintaining existing trends. Wavelet transform is implemented as a circuit to reduce computing power and eliminate specific frequency range including noise and motion artifact. The data from the chip is transmitted to external device (MATLAB) by wireless communication (Bluetooth) to be analyzed by machine learning. This paper present wavelet-based EMG sensing interface which includes front-end amplifier, wavelet filters, Analog to digital converter and Microcontroller. The main idea of the paper is front-end amplifiers which reduce a noise and motion artifact, wavelet filters that decompose the input signal for wavelet transform and machine learning for gesture recognition.ope

Optimization and Deep Learning for Wireless Communications and Robotics Automation

Department of Electrical Engineeringclos

강화학습을 이용한 최적의 무선 자원할당 기법 및 최적의 양자화 레벨 분석

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Deep Reinforcement Learning-Based Resource Allocation and Power Control in Small Cells With Limited Information Exchange

In multi-user downlink small cell networks, cooperative resource allocation (RA) within a small cell cluster is a key technique to enhance network capacity. However, capacity-maximizing RA in frequency-selective fading channels requires global channel state information (CSI) of users within a small cell cluster, which makes it infeasible in practical networks with limited direct link capacity. To circumvent this global CSI assumption, most of the existing studies on RA have been based on several CSI assumptions such as local CSI and local CSI at the transmitters (CSIT). Nevertheless, cost functions with local CSI or local CSIT in the literature rely on heuristic formulations, because the sum-rate cannot be computed if without global CSI. In this paper, we propose a deep reinforcement learning-based RA algorithm to maximize the sum-rate for any given limited information on instantaneous CSI or sum-rate at the previous period. The proposed scheme is not restricted to certain CSI assumptions, but attempts to find the best RA for any given information such as quantized local CSI and quantized local CSIT; thus, it is applicable to any given direct link capacity. The proposed algorithm is self-adaptive in time-varying channels, since it is not divided into training and test phases. We modify the target neural network (TNN) scheme to enhance the sum-rate and the convergence speed. Numerical simulations confirm that: i) the proposed algorithm outperforms the conventional algorithms even under the same CSI assumption such as local CSI and local CSIT; ii) a flexible trade-off between the amount of CSI and the sum-rate is realizable in practical systems

DNN-based Sum-Rate Maximization of Multicell MISO Networks

A beamforming vector design based on a deep neural network (DNN) is proposed for multicell multi-input single-output channels with scalar information exchange and local channel state information (CSI). The beamforming vectors are designed making zero generating-interference to the selected interference-free users (IFUs). The set of IFUs is chosen by the DNN based on supervised learning where the inputs can be obtained with only local CSI and limited scalar information exchange. Simulation results show that the DNN is well-trained in estimating the unknown CSI from the inputs with only local CSI in multicell networks.11Nsciescopu

강화학습 기반 무선 사용자 연결 기법에 관한 연구

2

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Performance Comparison of SU- and MU-MIMO in 802.11ax: Delay and Throughput

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Resource Allocation and Power Control in Cooperative Small Cell Networks With Backhaul Constraint

A joint resource allocation (RA), user association (UA), and power control (PC) problem is addressed for proportional fairness (PF) maximization in cooperative multiuser downlink small cell networks with limited backhaul capacity, based on orthogonal frequency division multiplexing. Previous studies have relaxed the per-resource-block (RB) UA and RA problem to a continuous optimization problem based on long-term signal-to-noise ratio, because the original problem is known as a combinatorial NP-hard problem. We tackle the original per-RB UA and RA problem to obtain a near-optimal solution with feasible complexity. Motivated by the fact that the condition for obtaining the near-global solution with the dual problem approach is rarely satisfied for increasing number of users, we derive explicit first order optimality conditions to obtain a 2-distance ring solution of the primal UA and RA problem, and propose a sequential optimization method. In addition, we propose a PC algorithm based on the first order KKT optimality conditions, in which transmission power of each RB is iteratively updated. The overall proposed scheme can be implemented with feasible complexity even with a large variable dimension. Numerical results show that the proposed scheme achieves the PF close to its outer-bound. Though there have been extensive studies on UA, RA, and PC in multicell networks, the proposed scheme is first to closely achieve the optimal PF performance in frequency-selective fading channels with feasible computational complexity