Browsing by Author "Chiu, Hsiao-Ting"

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A Geometric Approach for Optimal Power Control and Relay Selection in NOMA Wireless Relay Networks
(2020-04-01)
Gau, Rung-Hung
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Chiu, Hsiao-Ting
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Liao, Chien-Hsun
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電信工程研究所
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Institute of Communications Engineering
In this paper, we propose a geometric approach for optimal power control and relay selection in NOMA wireless relay networks. First, for each pair of relays, to derive an optimal vector of transmission power that maximizes the network throughput, we formulate a non-convex optimization problem. To obtain a closed-form solution for the non-convex optimization problem, we adopt ellipses and hyperbolas for classification of transmission power vectors. In addition, the proposed geometric approach could be used to select an optimal pair of relays and an optimal vector of transmission power in polynomial time. Furthermore, we observe that allocating all transmission power of the BS to send data to the stronger relay is not necessarily optimal in a NOMA wireless relay network. Simulation results show that the proposed geometric approach could significantly increase the end-to-end sum rate and reduce the power consumption of wireless communications for NOMA wireless relay networks with low computational complexity.
Bayesian Tree Search for Beamforming Training in Millimeter Wave Wireless Communication Systems
(2018-01-01)
Chen, Wei-Chen
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Chiu, Hsiao-Ting
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Gau, Rung-Hung
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電信工程研究所
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Institute of Communications Engineering
In this paper, we propose novel algorithms of beamforming training for millimeter wave wireless communication systems. Instead of searching the whole codebook organized as a binary tree, we propose using Bayesian tree search algorithms to reduce the average delay of beamforming training. In addition, we design algorithms that derive an optimal threshold for the proposed opportunistic one-threshold tree search algorithm and an optimal pair of thresholds for the proposed opportunistic two-threshold tree search algorithm. Furthermore, we propose using quantization to efficiently calculate the likelihood ratio in the proposed opportunistic tree search algorithms. Our simulation results show that the proposed algorithms could significantly reduce the average delay of beamforming training.
Channel-Aware Signal-Centric Medium Access Control for Machine Type Communications
(2018-01-01)
Chiu, Hsiao-Ting
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Gau, Rung-Hung
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電信工程研究所
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Institute of Communications Engineering
In this paper, we propose the green stopping signal-centric predictive polling algorithm for machine type communications. Based on Markov optimal stopping theory, the green stopping signal-centric predictive polling algorithm exploits channel state information to reduce the energy consumption of wireless communications without knowing the mean channel gain. To reduce the computational complexity, the proposed algorithm is built upon a closed-form optimal stopping rule. Furthermore, we derive analytical results that characterize the optimal stopping time when the proposed green stopping signal-centric predictive polling algorithm is used. Simulation results show that the proposed approach could significantly reduce the average energy consumption at machines and the average signal prediction error at the base station in comparison with alternative approaches.
Machine Learning-Driven Optimal Proactive Edge Caching in Wireless Small Cell Networks
(2019-01-01)
Lin, Pei-Ying
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Chiu, Hsiao-Ting
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Gau, Rung-Hung
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電信工程研究所
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Institute of Communications Engineering
In this paper, we propose a novel approach for proactive edge caching in wireless small cell networks. Specifically, we propose using a recurrent neural network for predicting the content popularity with low computational complexity. The mean estimation error of the adopted recurrent neural network could be very close to that of the optimal linear prediction filter utilizing all past history. Based on the predicted content popularity, we formulate and solve a minimum cost flow problem in order to optimally place content files at edge caches. Since the computational complexity of the adopted recurrent neural network is relatively low and the minimum cost flow problem can be solved in polynomial time, the proposed approach is feasible in practice. Simulation results show that the proposed approach outperforms a greedy approach and can significantly reduce the bandwidth consumption of the backhaul network.
Opportunistic Matrix Precoding for Non-Separable Wireless MIMO-NOMA Networks
(2018-01-01)
Chiu, Hsiao-Ting
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Gau, Rung-Hung
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交大名義發表
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National Chiao Tung University
In this paper, we propose an opportunistic matrix precoding algorithm for non-separable wireless MIMO-NOMA networks. It is known that NOMA is beneficial when the difference between two channel gains is very large and therefore the two channels are separable. We study general wireless MIMO-NOMA networks in which the two channels might be nonseparable. Based on channel state information, we formulate and solve a non-convex optimization problem in order to maximize the sum rate and provide guaranteed rate to the weaker user. We divide the non-convex optimization problem into two parts and propose algorithms for solving them. Our simulation results show that the proposed algorithm could significantly increase the sum rate for non-separable wireless MIMO-NOMA networks in comparison with a number of alternative approaches.
Optimal Power Control for NOMA Wireless Networks With Relays
(2018-02-01)
Gau, Rung-Hung
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Chiu, Hsiao-Ting
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Liao, Chien-Hsun
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Wu, Chi-Lun
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電信工程研究所
In this letter, we propose a novel approach for dynamically selecting an optimal relay mode and optimal transmission power levels in non-orthogonal multiple access wireless networks with relays. To maximize the network throughput, the proposed approach alternates between the one-relay mode and the two-relay mode based on channel state information. For the two-relay mode, we derive novel closed-form results on optimal power control. Unlike in a conventional one-hop network, it is not always optimal to allocate all power to the relay with the largest channel gain in the studied two-hop network. Based on the closed-form results, one could determine the optimal power levels quickly. Simulation results show that the proposed approach could significantly increase the network throughput.
Optimizing The Service Capacity of SDN-based Cellular Networks with Service Chaining and NFV
(2016)
Gau, Rung-Hung
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Chiu, Hsiao-Ting
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Tsai, Pei-Kan
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電機工程學系
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Department of Electrical and Computer Engineering
In this paper, we investigate the service capacity of SDN-based cellular networks with service chaining and network function virtualization. Due to service chaining requirements, the service capacity is different from the well-known maximum flow-minimum-cut capacity. To maximize the service capacity, we take a novel approach of traffic engineering. In particular, we formulate and solve a linear optimization problem to obtain optimal service paths for flows and an optimal bandwidth allocation for virtual machines. We also include numerical results that justify the usage of the proposed approach.
User Assignment and Discrete Power Control for Scalable NOMA Multicast in Cellular Networks
(2016)
Gau, Rung-Hung
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Chiu, Hsiao-Ting
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電機工程學系
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Department of Electrical and Computer Engineering
In this paper, we propose using non-orthogonal multiple access to increase the throughput of wireless multicast. To achieve optimal user assignment and discrete power control, we formulate a combinatorial optimization problem. In particular, we concentrate on the general case in which an user cluster consists of an arbitrary number of users. To enhance scalability, we propose novel and efficient algorithms for finding out an optimal pair of user assignment and transmission power vector for NOMA multicast. Simulation results show that the proposed approach could significantly improve the multicast throughput.