Browsing by Author "Xu, Wenjun"

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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.
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 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.
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.
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