A Syllable-based Technique for Word Embeddings of Korean Words

Word embedding has become a fundamental component to many NLP tasks such as named entity recognition and machine translation. However, popular models that learn such embeddings are unaware of the morphology of words, so it is not directly applicable to highly agglutinative languages such as Korean. We propose a syllable-based learning model for Korean using a convolutional neural network, in which word representation is composed of trained syllable vectors. Our model successfully produces morphologically meaningful representation of Korean words compared to the original Skip-gram embeddings. The results also show that it is quite robust to the Out-of-Vocabulary problem.Comment: 5 pages, 3 figures, 1 table. Accepted for EMNLP 2017 Workshop - The 1st Workshop on Subword and Character level models in NLP (SCLeM

Optimizing communication performance of low-resolution ADC systems with hybrid beamforming

Low-resolution analog-to-digital converter (ADC) systems and hybrid analog-and-digital beamforming systems have drawn extensive attention as a promising receiver architecture for millimeter wave (mmWave) communications by reducing hardware cost and power consumption. In this dissertation, hybrid beamforming systems that employ low-resolution ADCs are considered to achieve a better trade-off between communication performance and power consumption. Due to non-negligible quantization errors, however, existing state-of-the-art hybrid beamforming techniques cannot be directly applied to such systems as they ignore the impact of the quantization error. In this regard, I propose new receiver architectures and algorithms for hybrid beamforming with low-resolution ADC systems to enhance spectral efficiency under coarse quantization in different layers of the network stack, and provide subsequent analyses. First, problems of optimizing the number of ADC bits and designing analog combiners with fixed-resolution ADCs are tackled to design an energy-efficient receiver architecture with phase shifter-based hybrid beamforming. A hybrid receiver architecture with resolution-adaptive ADCs for mmWave communications is proposed to optimize the power distribution over ADCs. For the proposed architecture, a near-optimal bit-allocation solution is derived in closed form. In addition, the performance lower bound of the proposed receiver architecture is derived in ergodic rate. For a fixed-resolution ADC system, a new analog combining architecture is proposed for mmWave communications. The proposed analog combiner consists of two consecutive analog combiners that maximize channel gain and minimize effective quantization error. An approximated ergodic rate of the proposed receiver is also derived in closed form. Next, considering switch-based analog beamforming, antenna selection at a base station is investigated for low-resolution ADC systems. Unlike downlink transmit antenna selection problems, a quantization-aware antenna selection criterion is necessary and derived to incorporate quantization error for uplink receive antenna selection problems. Leveraging the criterion, a quantization-aware antenna selection algorithm is proposed and analyzed for uplink. Last, in a higher layer of the network stack, a user scheduling problem is investigated for hybrid beamforming systems with low-resolution ADCs. New user scheduling criteria are derived to maximize scheduling gain under coarse quantization and efficient scheduling algorithms are proposed accordingly. Subsequent analysis for the proposed algorithm provides closed-form ergodic ratesElectrical and Computer Engineerin

A Case Study on One-Source Multi-Platform Mobile Game Development Using Cocos2d-x

In this paper, by introducing a case study on development of a first-person shooter game ldquoBiosisrdquo playable in both iOS and Android platforms, we present guidelines for developing one-source multi-platform mobile games using cocos2d-x game engine.nbsp This paper also describes the ldquoResourceMakerrdquo implemented to share and manage game assets efficiently in our multi-targeted development environment and the level engine by using which game planners can easily apply their designs to game levels.nbsp nbspWe expect that the presented guidelines will help game developers reduce the time and cost for development in the mobile game ecosystem, the life-cycle of which is very short

Joint Optimization for Secure and Reliable Communications in Finite Blocklength Regime

To realize ultra-reliable low latency communications with high spectral efficiency and security, we investigate a joint optimization problem for downlink communications with multiple users and eavesdroppers in the finite blocklength (FBL) regime. We formulate a multi-objective optimization problem to maximize a sum secrecy rate by developing a secure precoder and to minimize a maximum error probability and information leakage rate. The main challenges arise from the complicated multi-objective problem, non-tractable back-off factors from the FBL assumption, non-convexity and non-smoothness of the secrecy rate, and the intertwined optimization variables. To address these challenges, we adopt an alternating optimization approach by decomposing the problem into two phases: secure precoding design, and maximum error probability and information leakage rate minimization. In the first phase, we obtain a lower bound of the secrecy rate and derive a first-order Karush-Kuhn-Tucker (KKT) condition to identify local optimal solutions with respect to the precoders. Interpreting the condition as a generalized eigenvalue problem, we solve the problem by using a power iteration-based method. In the second phase, we adopt a weighted-sum approach and derive KKT conditions in terms of the error probabilities and leakage rates for given precoders. Simulations validate the proposed algorithm.Comment: 30 pages, 8 figure

Coordinated Per-Antenna Power Minimization for Multicell Massive MIMO Systems with Low-Resolution Data Converters

A multicell-coordinated beamforming solution for massive multiple-input multiple-output orthogonal frequency-division multiplexing (OFDM) systems is presented when employing low-resolution data converters and per-antenna level constraints. For a more realistic deployment, we aim to find the downlink (DL) beamformer that minimizes the maximum power on transmit antenna array of each basestation under received signal quality constraints while minimizing per-antenna transmit power. We show that strong duality holds between the primal DL formulation and its manageable Lagrangian dual problem which can be interpreted as the virtual uplink (UL) problem with adjustable noise covariance matrices. For a fixed set of noise covariance matrices, we claim that the virtual UL solution is effectively used to compute the DL beamformer and noise covariance matrices can be subsequently updated with an associated subgradient. Our primary contributions are then (1) formulating the quantized DL OFDM antenna power minimax problem and deriving its associated dual problem, (2) showing strong duality and interpreting the dual as a virtual quantized UL OFDM problem, and (3) developing an iterative minimax algorithm based on the dual problem. Simulations validate the proposed algorithm in terms of the maximum antenna transmit power and peak-to-average-power ratio.Comment: submitted for possible IEEE journal publicatio

Learning-Based One-Bit Maximum Likelihood Detection for Massive MIMO Systems: Dithering-Aided Adaptive Approach

In this paper, we propose a learning-based detection framework for uplink massive multiple-input and multiple-output (MIMO) systems with one-bit analog-to-digital converters. The learning-based detection only requires counting the occurrences of the quantized outputs of -1 and +1 for estimating a likelihood probability at each antenna. Accordingly, the key advantage of this approach is to perform maximum likelihood detection without explicit channel estimation which has been one of the primary challenges of one-bit quantized systems. However, due to the quasi-deterministic reception in the high signal-to-noise ratio (SNR) regime, one-bit observations in the high SNR regime are biased to either +1 or -1, and thus, the learning requires excessive training to estimate the small likelihood probabilities. To address this drawback, we propose a dither-and-learning technique to estimate likelihood functions from dithered signals. First, we add a dithering signal to artificially decrease the SNR and then infer the likelihood function from the quantized dithered signals by using an SNR estimate derived from a deep neural network-based estimator which is trained offline. We extend our technique by developing an adaptive dither-and-learning method that updates the dithering power according to the patterns observed in the quantized dithered signals. The proposed framework is also applied to channel-coded MIMO systems by computing a bit-wise and user-wise log-likelihood ratio from the refined likelihood probabilities. Simulation results validate the performance of the proposed methods in both uncoded and coded systems.Comment: Accepted for publication in IEEE Transactions on Vehicular Technologie

Unified Modeling and Rate Coverage Analysis for Satellite-Terrestrial Integrated Networks: Coverage Extension or Data Offloading?

With the growing interest in satellite networks, satellite-terrestrial integrated networks (STINs) have gained significant attention because of their potential benefits. However, due to the lack of a tractable network model for the STIN architecture, analytical studies allowing one to investigate the performance of such networks are not yet available. In this work, we propose a unified network model that jointly captures satellite and terrestrial networks into one analytical framework. Our key idea is based on Poisson point processes distributed on concentric spheres, assigning a random height to each point as a mark. This allows one to consider each point as a source of desired signal or a source of interference while ensuring visibility to the typical user. Thanks to this model, we derive the probability of coverage of STINs as a function of major system parameters, chiefly path-loss exponent, satellites and terrestrial base stations' height distributions and density, transmit power and biasing factors. Leveraging the analysis, we concretely explore two benefits that STINs provide: i) coverage extension in remote rural areas and ii) data offloading in dense urban areas.Comment: submitted to IEEE journa

Analysis of Thin Film Parylene-Metal-Parylene Device Based on Mechanical Tensile Strength Measurement

International audienceThis paper presents an FEM analysis and experiment of parylene-metal-parylene flexible substrate for implantable medical devices. Tensile strength measurement of the parylene-metal-parylene has been carried out and corresponding FEM modeling and simulation has been done to understand its mechanical behaviour. Besides, frequently encountered metal delamination on parylene substrate has been studied based on cohesive zone model of interface between the two materials