Browsing by Author "Lee, Chia-Han"

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DATA-COGNITION-EMPOWERED INTELLIGENT WIRELESS NETWORKS: DATA, UTILITIES, COGNITION BRAIN, AND ARCHITECTURE
(2018-02-01)
Xu, Wenjun
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Xu, Yue
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Lee, Chia-Han
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Feng, Zhiyong
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Zhang, Ping
With the proliferation of heterogeneous wireless networks, classical information-theoretical methods can no longer provide accurate mathematical models to analyze the complicated network performance for the regulation and optimization of networks. The emerging landscape of big data and machine learning has provided a new paradigm for the design and optimization of intelligent wireless networks. In this article, a general architecture for data-cognition-empowered intelligent wireless networks is proposed. Characteristics of wireless data, the frequently used utilities for data cognition, and data cognition methods are discussed to depict the building blocks and challenges in the exploration of the architecture for data-cognition-empowered intelligent wireless networks.
Deep Learning-Constructed Joint Transmission-Recognition for Internet of Things
(2019-01-01)
Lee, Chia-Han
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Lin, Jia-Wei
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Chen, Po-Hao
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Chang, Yu-Chieh
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電信工程研究所
The widely deployed Internet-of-Things (IoT) devices provide intelligent services with its cognition capability. Since the IoT data are usually transmitted to the server for recognition (e.g., image classification) due to low computational capability and limited power supply, achieving recognition accuracy under limited bandwidth and noisy channel of wireless networks is a crucial but challenging task. In this paper, we propose a deep learning-constructed joint transmission-recognition scheme for the IoT devices to effectively transmit data wirelessly to the server for recognition, jointly considering transmission bandwidth, transmission reliability, complexity, and recognition accuracy. Compared with other schemes that may be deployed on the IoT devices, i.e., a scheme based on JPEG compression and two compressed sensing-based schemes, the proposed deep neural network-based scheme has much higher recognition accuracy under various transmission scenarios at all signal-to-noise ratios (SNRs). In particular, the proposed scheme maintains good performance at the very low SNR. Moreover, the complexity of the proposed scheme is low, making it suitable for IoT applications. Finally, a transfer learning-based training method is proposed to effectively mitigate the computing burden and reduce the overhead of online training.
Distributed Beamforming with Improper Gaussian Signaling for MIMO Interference Broadcast Channels
(2017-01-01)
Lin, Jhe-Yi
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Chang, Ronald Y.
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Lee, Chia-Han
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Tsao, Hen-Wai
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電機工程學系
For rate optimization in interference limited network, improper Gaussian signaling has shown its capability to outperform the conventional proper Gaussian signaling. In this work, we study a weighted sum rate maximization problem with improper Gaussian signaling for the multiple-input multiple-output interference broadcast channel (MIMO-IBC). To solve this nonconvex and NP-hard problem, we propose an effective two-stage algorithm. In the first stage, a weighted mean square error (MSE) minimization algorithm is adopted to compute the transmit covariance matrices, and in the second stage, an alternating direction method of multipliers (ADMM)-based distributed multi-agent algorithm is proposed to obtain the globally optimal pseudo-covariance matrices in a parallel and scalable fashion. Finally, simulation results are presented to demonstrate the superior performance of our algorithm under different signal-to-noise ratio (SNR) and various network scenarios.
Distributed Video Codec with Spatiotemporal Side Information
(2017-01-01)
Lee, Yueh-Ying
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Kuo, Pin-Hung
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Lee, Chia-Han
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Chen, Yen-Kuang
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Chien, Shao-Yi
In this paper, a distributed video coding (DVC) system with spatiotemporal side information is proposed. The proposed framework addresses the problem of poor compression performance of DVC for high-motion video sequences by integrating temporal and spatial prediction schemes in one framework. Super-resolution techniques are employed for spatially-predicted side information generation, and a support vector machine is trained to adaptively select the coding structure with both spatial and temporal prediction. In addition, an encoder-driven coding mode selection at different granularities, including frame, block and coefficient levels, is adopted to further improve the coding performance for various video conditions. Experimental results show that the average BD rate reduction of the proposed framework is 12.93% compared with the DISCOVER DVC, and the coding gain is significant, especially for high-motion sequences. Moreover, the average computing complexity is only 92.26% of the DISCOVER DVC.
Energy Efficient UAV-Enabled Multicast Systems: Joint Grouping and Trajectory Optimization
(2019-01-01)
Deng, Chang
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Xu, Wenjun
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Lee, Chia-Han
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Gao, Hui
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Xu, Wenbo
We study an energy-efficient unmanned aerial vehicle (UAV) multicast system, in which ground terminals (GTs) requiring a file of common information (CI) are grouped and a UAV flies to each group to deliver the CI using minimum energy consumption. A machine learning (ML) empowered joint multicast grouping and UAV trajectory optimization framework is proposed to tackle the challenging joint optimization problem. In this framework, we first propose the compressed-feature regression and clustering machine learning ((CML)-M-2) for multicast grouping. A support vector regression (SVR) is trained with the silhouette coefficient, a one-dimensional compressed feature regarding the distribution of GTs, to efficiently determine the number of groups that guides the K-means clustering to approach the optimal multicast grouping. With the (CML)-M-2-enabled multicast grouping, we solve the UAV trajectory optimization problem by formulating an equivalent centroid-adjustable traveling salesman problem (CA-TSP). An efficient CA-TSP inspired iterative optimization algorithm is proposed for UAV trajectory planning. The proposed ML-empowered joint optimization framework, which integrates the offline (CML)-M-2-enabled multicast grouping and the online CA-TSP inspired UAV trajectory optimization, is shown to achieve excellent energy-saving performance.
Energy-Efficient Activation and Uplink Transmission for Cellular IoT
(2020-02-01)
Liu, Chun-Hung
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Shen, Yu-Han
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Lee, Chia-Han
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電機工程學系
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電信工程研究所
Consider a large-scale cellular network in which base stations (BSs) serve massive Internet of Things (IoT) devices. Since IoT devices are powered by a capacity-limited battery, how to prolong their working lifetime is a paramount problem for the success of cellular IoT systems. This article proposes how to use BSs to manage the active and dormant operating modes of the IoT devices via downlink signaling in an energy-efficient fashion and how the IoT devices perform energy-efficient uplink power control to improve their uplink coverage. We first investigate the fundamental statistical properties of an activation signaling process induced by BSs that would like to activate the devices in their cells, which helps to derive the neat expressions of the true, false, and total activation probabilities that reveal joint downlink power control and BS coordination is an effective means to significantly improve the activation performance. We then propose an energy-efficient uplink power control for IoT devices which is shown to save power and ameliorate the uplink coverage probability at the same time. We also propose an energy-efficient downlink power control and BS coordination scheme, which is shown to remarkably improve the activation and uplink coverage performances at the same time.
Energy-Efficient Joint Sensing Duration, Detection Threshold, and Power Allocation Optimization in Cognitive OFDM Systems
(2016-12)
Xu, Wenjun
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Zhou, Xuemei
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Lee, Chia-Han
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Feng, Zhiyong
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Lin, Jiaru
This paper investigates an energy efficiency optimization problem in cognitive orthogonal frequency division multiplexing systems. The goal is to maximize the energy efficiency by adapting the sensing duration, detection threshold, and transmit power to the constraints of the energy consumption of the secondary network and the interference to the primary network in a statistical manner. First, the case of identical detection threshold for all subcarriers is considered. In order to circumvent the intractability of the resulting problem, an alternate iteration framework is proposed to iteratively solve the three decoupled subproblems: sensing duration optimization, detection threshold optimization, and power allocation optimization. By exploiting the characteristics of each subproblem, the proposed framework is proved to be convergent. Then, the case with individual detection threshold for each subcarrier is explored. By proving that the optimal detection threshold is the root of a quadratic equation with one unknown variable, the proposed framework can be applied with minor modification. Simulation results show that the proposed alternating optimization framework can approach rapidly to the optimal solution, with less than 1% gap. Compared with the existing schemes, both the cases with identical and individual detection thresholds can achieve a considerable energy efficiency gain, with the latter further outperforming the former.
Joint Beamforming and Time Duration Optimization for Battery-free-Multi-Antenna-Relay-Assisted WPCN
(2018-01-01)
Yang, Fan
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Xu, Wenjun
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Lee, Chia-Han
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Feng, Zhiyong
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Lin, Jiaru
This paper studies an energy beamforming and time duration joint optimization problem for maximizing the sum-rate of a battery-free-multi-antenna-relay-assisted wireless powered communication network (WPCN). The considered problem is non-convex due to the strongly coupled optimization variables. By introducing new optimization variables and reformulating the problem into a convex problem, the optimal solution is obtained based on convex optimization methods. Furthermore, an alternate iteration algorithm is proposed by decoupling the problem into two subproblems, and a semi-closed form solution of the optimal energy beamforming matrix is derived with given time allocation. Simulation results indicate that the proposed battery-free multiantenna relay architecture can deliver a significant sum-rate gain for the WPCN. In addition, the proposed suboptimal algorithm only has 5% sum-rate loss compared to the optimal solution.
Joint Dynamic Spectrum Access and Multi-Relay Selection: A Matching-Theory-based Approach
(2017-01-01)
Xu, Wenjun
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Li, Xue
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Wang, Yixin
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Lee, Chia-Han
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Feng, Zhiyong
In this paper, the problem of joint dynamic spectrum access and multi-relay selection is investigated in relay-enabled cooperative communication systems to maximize the system sum-capacity. Since the considered problem is a mixed integer nonlinear program, which is generally intractable to find the optimal solution, two matching theory-based suboptimal algorithms are proposed to reduce the computational complexity for two different cases. For the case that each source node can only be assisted by one relay, a cyclic three-sided matching algorithm is firstly proposed to attain the stable matching results for the selection of the source node and the relay with the spectrum band used. Then, for the case that each source node can be assisted by more than one relay, a two-step matching algorithm is proposed to perform joint dynamic spectrum access and multi-relay selection. Simulation results show that the proposed algorithms, with much lower complexity compared to the optimal exhaustive search, can achieve the near-optimal performance with a gap to the optimum being less than 5%.
Joint Sensing Duration Adaptation, User Matching, and Power Allocation for Cognitive OFDM-NOMA Systems
(2018-02-01)
Xu, Wenjun
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Li, Xue
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Lee, Chia-Han
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Pan, Miao
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Feng, Zhiyong
In this paper, the non-orthogonal multiple access (NOMA) technology is integrated into cognitive orthogonal frequency-division multiplexing (OFDM) systems, called cognitive OFDM-NOMA, to boost the system capacity. First, a capacity maximization problem is considered in half-duplex cognitive OFDM-NOMA systems with two accessible users on each subcarrier. Due to the intractability of the considered problem, we decompose it into three subproblems, i.e., the optimization of, respectively, sensing duration, user scheduling, and power allocation. By investigating and exploiting the characteristics of each subproblem, the optimal sensing duration adaptation, a matching-theory-based user scheduling, and the optimal power allocation are proposed correspondingly. An alternate iteration framework is further proposed to jointly optimize these three subproblems, with its convergence proved. Moreover, based on the non-cooperative game theory, a generalized power allocation algorithm is proposed and then used in the framework to accommodate half-duplex cognitive OFDM-NOMA systems with multiple users on each subcarrier. Finally, the proposed framework is extended to solve the capacity maximization problem in full-duplex cognitive OFDM-NOMA systems. Simulation results validate the superior performance of the proposed algorithms. For example, for the case of two accessible users, the proposed framework approaches the optimal solution with less than 1% capacity loss and 120 times lower complexity compared with exhaustive search.
Machine Learning-Based Energy-Spectrum Two-Dimensional Cognition in Energy Harvesting CRNs
(2020-01-01)
Fan, Yongjian
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Xu, Wenjun
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Lee, Chia-Han
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Wu, Silei
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Yang, Fan
Energy harvesting cognitive radio network (EH-CRN) is a promising approach to address the shortage of spectrum resources and the increase of energy consumption simultaneously in wireless networks. In this article, we propose a novel machine learning (ML)-based energy-spectrum two-dimensional (2D) cognition technology to improve the sensing accuracy as well as the network throughput in EH-CRNs, which consists of sensing, prediction and decision modules. More specifically, we first study the 2D sensing module which is achieved by a carefully constructed dynamic Bayesian network (DBN) to effectively exploit the coupling between spectrum usage and energy harvesting in EH-CRNs. Then we propose a deep neural network (DNN) based 2D transmission decision module to optimize the transmission energy of secondary users (SUs). With our proposed novel 2D cognition scheme, SUs can characterize the energy-spectrum correlation and transmit data with optimal transmission energy. The proposed ML-based 2D cognition is evaluated via extensive simulations in terms of sensing accuracy, prediction accuracy, and network throughput, and simulation results indicate that our proposed scheme significantly outperforms the conventional one-dimensional (1D) cognition scheme working in spectrum or energy dimension only.
Modeling Molecular Communications in Tubes With Poiseuille Flow and Robin Boundary Condition
(2019-08-01)
Lo, Yun-Feng
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Lee, Chia-Han
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Chou, Po-Chun
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Yeh, Ping-Cheng
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電機工程學系
Accurately modeling the molecular channel is crucial for the design and analysis of molecular communications. In this letter, we propose a Markovian-based channel model for molecular communications in tubes with Poiseuille flow and the Robin boundary condition. The cross section of the tube is divided into rings and the continuously varying flow velocity field is approximated by the mean of the flow velocity on each ring. The radial displacement of molecules versus time is modeled by a Markov chain, and then the probability density function of the axial displacement is modeled using this Markov chain and the approximated flow velocity field. The accuracy of the proposed model is verified through particle-based simulations.
Multi-Agent Distributed Beamforming With Improper Gaussian Signaling for MIMO Interference Broadcast Channels
(2019-01-01)
Lin, Jhe-Yi
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Chang, Ronald Y.
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Lee, Chia-Han
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Tsao, Hen-Wai
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Su, Hsuan-Jung
For rate optimization in interference limited networks, improper Gaussian signaling has shown its ability to outperform conventional proper Gaussian signaling. In this paper, we study a weighted sum-rate maximization problem with improper Gaussian signaling for the multiple-input multiple-output interference broadcast channel. To solve this nonconvex and NP-hard problem, we propose an effective separate covariance and pseudo-covariance matrices optimization algorithm. In the covariance optimization, a weighted minimum mean square error algorithm is adopted, and in the pseudo-covariance optimization, an alternating optimization (AO) algorithm is proposed, which guarantees convergence to a stationary solution and ensures a sum-rate improvement over proper Gaussian signaling. An alternating direction method of multipliers-based multi-agent distributed algorithm is proposed to solve an AO subproblem with the globally optimal solution in a parallel and scalable fashion. The proposed scheme exhibits favorable convergence, optimality, and complexity properties for future large-scale networks. The simulation results demonstrate the superior sum-rate performance of the proposed algorithm as compared with the existing schemes with proper as well as improper Gaussian signaling under various network configurations.
On Capacity Bounds of Two-Way Diffusion Channel with Molecule Harvesting
(2017-01-01)
Huang, Jiun-Ting
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Lee, Chia-Han
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交大名義發表
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National Chiao Tung University
In this paper, we investigate a binary two-way diffusion channel from an information theoretical perspective. Molecule harvesting is considered by which molecules can be reused by the receiver for subsequent transmissions. An outer bound of the capacity region for the general case and inner bounds for specific cases are derived. With the proposed outer bound, we show that no signaling rate is achievable in the considered system if messenger molecules perform free diffusion in an unconfined space and the nodes are not replenished with extra molecules. The proposed lower bounds are tighter than the ones resulted from the traditional joint typicality decoding method.
Optimal Beamforming and Duration/Power Allocation for Cooperative PB-enabled WPCN
(2017-01-01)
Shi, Xinxin
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Xu, Wenjun
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Lee, Chia-Han
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Feng, Zhiyong
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Lin, Jiaru
This paper studies the spectrum efficiency (SE) maximization problem for cooperative multi-antenna power beacon (PB)-enabled wireless powered communication networks (WPCN), where each transmitter harvests energy from surrounding PBs and then transmits data to the corresponding receiver within its allocated duration. The considered problem is formulated as jointly optimizing the energy beamforming vectors of PBs, the transmission duration, and the transmit power of users to maximize the total SE. In order to derive an efficient algorithm, the SE maximization problem is decomposed into two subproblems: the SE maximization problem for data transmission and the energy consumption minimization problem for energy transfer. We prove that the optimal SE is concave with the harvested energy of each user, and based on this concavity, an efficient algorithm is proposed to achieve the optimal solution. Finally, simulation results validate the optimality of the proposed algorithm, and verify the superiority of the proposed scheme-more than 150% performance gain is obtained, compared with the scheme of single PB with single antenna.
Pair Auction and Matching for Resource Allocation in Full-Duplex Cellular Systems
(2020-04-01)
Chao, Cheng-Chih
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Wang, Chih-Yu
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Lee, Chia-Han
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Wei, Hung-Yu
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Chen, Wen-Tsuen
Full-duplex techniques improve spectral efficiency in cellular networks by allowing a transceiver to simultaneously transmit and receive data with the same radio resource. To minimize self-interference and inter-node interference due to the deployment of full-duplex radio, users have to feed back their measurements of interference to allow the base station to effectively allocate resources and form transmission pairs. Considering that the interference measurements are private information, users may not report the true values and may try to gain their own benefits. To solve this problem, we propose a strategy-proof resource allocation mechanism for full-duplex cellular networks. The proposed scheme, which is based on auction and matching theories, guarantees that users report true values of their channel quality and utilities, and therefore guarantees the correctness of feedback information and the efficiency of resource allocation. Moreover, the proposed matching-based mechanism is theoretically proved to be stable and Pareto efficient. Simulation results confirm that, compared to a centralized scheme with non-truthful information from users, the proposed scheme can achieve an 11% gain.
Special Issue on Wireless Powered Communications Networks
(2018-06-01)
Lee, Chia-Han
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Xu, Wenjun
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交大名義發表
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National Chiao Tung University
Training-based Synchronization for Quantity-based Modulation in Inverse Gaussian Channels
(2017-01-01)
Hsu, Bo-Kai
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Chou, Po-Chun
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Lee, Chia-Han
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Yeh, Ping-Cheng
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電機工程學系
In time-slotted molecular communication systems, clock synchronization between transmitter and receiver is crucial. In this paper, training-based synchronization schemes in a molecular communication system with quantity-based modulation are proposed by utilizing the information of arrival times of molecules. For timing offset estimation, three estimators are proposed, including maximum likelihood estimation (MLE), unbiased linear estimation (ULE), and iterative ULE. For clock skew estimation, two algorithms, arithmetic mean estimation and weighted mean estimation, are proposed. Simulation results demonstrate that the proposed estimators are effective.
Wireless Max-Min Utility Fairness With General Monotonic Constraints by Perron-Frobenius Theory
(2016-12)
Zheng, Liang
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Hong, Y. -W. Peter
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Tan, Chee Wei
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Hsieh, Cheng-Lin
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Lee, Chia-Han
This paper presents a systematic approach for solving wireless max-min utility fairness optimization problems in multiuser wireless networks with general monotonic constraints. These problems are often challenging to solve due to their non-convexity. By establishing a connection between this class of optimization problems and the class of conditional eigenvalue problems that can be addressed by a generalized nonlinear Perron-Frobenius theory, we show how these problems can be solved optimally using an iterative algorithm that converges geometrically fast. The mathematical development in this paper unifies previous work and allows us to handle a broader class of competitive utility functions with general nonlinear monotonic constraints. Several representative applications illustrate the effectiveness of the proposed framework, including the max-min quality-of-service subject to robust interference temperature constraints in cognitive radio networks, the min-max weighted mean-square error subject to signal-to-interference-and-noise ratio constraints in multiuser downlink systems, the max-min throughput subject to nonlinear power constraints in energy-efficient wireless networks, the max-min sigmoid utility in multimedia wireless networks, and the min-max outage probability subject to outage constraints in heterogeneous wireless networks. Numerical results are presented to demonstrate the fast-convergence behavior of the algorithms to the optimal fixed-point solution characterized by our generalized nonlinear Perron-Frobenius theoretic framework.