Browsing by Author "Gau, Rung-Hung"

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A Bargaining Game Based Access Network Selection Scheme for HetNet
(2014-01-01)
Liou, Yi-Shing
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Gau, Rung-Hung
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Chang, Chung-Ju
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電機資訊學士班
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Undergraduate Honors Program of Electrical Engineering and Computer Science
We propose a novel bargaining-game-based access network selection scheme for call requests in heterogeneous networks (HetNet). The bargaining game takes into consideration a number of factors including the mobility pattern of the mobile device, the load of the candidate access network, and the preference of the candidate access network to the call request. The studied bargaining game has a unique solution since the set of feasible payoff vectors is convex. Simulation results show that the proposed scheme could significantly outperform the TOPSIS scheme and the evolutionary game approach in terms of the handoff occurrence ratio and the call dropping probability.
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.
An Implementation Framework of MapReduce Email Social Network Analysis
(2011)
Gau, Rung-Hung
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Hsieh, Tzu-Chiang
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Tsai, Sheng-Wen
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Cheng, Ching-Pei
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電機工程學系
In this paper, we introduce our own implementation of Map Reduce graph-theoretic algorithms for Email social network analysis on the Hadoop platform. Graph theory is a powerful tool for social network analysis and Map Reduce is a well-known paradigm for distributed parallel computing. However, based on our own experience, unlike writing conventional Java/C++ programs, writing Java programs to implement Map Reduce graph-theoretic algorithms is not straight-forward, even for some fundamental graph-theoretic algorithms. In this paper, for the problem of Email social network analysis, we compare the performance of cloud computing programs with that of conventional computer programs. We show that as long as the size of the input data exceeds a threshold, the cloud computing programs outperform their conventional counterparts.
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 Prediction at the Destination for Relay Training Overhead Reduction in Cooperative Wireless Networks
(2012)
Chung, Wen-Ching
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Wu, Jwo-Yuh
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Gau, Rung-Hung
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Chang, Chung-Ju
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電機工程學系
Signaling overhead reduction has been a key approach to realizing energy-efficient wireless cooperative communication systems. Motivated by the fact that consecutive temporal samples of real-world wireless channels are typically correlated, this paper proposes to exploit such time-domain correlation for relay training overhead reduction in cooperative networks. Specifically, we consider a cooperative transmit beamforming system, in which relay terminals employing the decode-and-forward protocol collaboratively transmit the source message according to the pre maximal-ratio-combining (pre-MRC) principle. During the training phase, the relays send training signals to aid channel estimation at the destination. Based on the acquired record of the channel state information (CSI), the destination then employs a linear minimum-mean-square-errors (LMMSE) channel predictor to update the CSI; in this way, training overhead dedicated by relays can then be reduced. We derive a closed-form expression for the receive SNR at the destination when the pre-MRC beamforming factors are computed in accordance with the predicted CSI. Our analytic results can be used for characterizing the performance degradation of channel prediction as the duration of the prediction phase is enlarged. The proposed analytic studies are corroborated by numerical simulations.
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.
Dynamically Tuning Aggression Levels for Capacity-Region-Aware Medium Access Control in Wireless Networks
(2014-04-01)
Liou, Yi-Shing
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Gau, Rung-Hung
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Chang, Chung-Ju
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電子工程學系及電子研究所
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Department of Electronics Engineering and Institute of Electronics
We propose adaptive capacity-region-aware algorithms for medium access control in wireless networks. In particular, the proposed algorithms are aware of the information-theoretic capacity region of a multiple access channel. According to the proposed algorithms, a node dynamically adjusts its channel access strategy based on the previous strategies of other nodes and the channel feedback. A strategy is composed of a transmission threshold and an aggression level. We propose symmetric learning algorithms for maximizing throughput. In addition, we propose asymmetric learning algorithms to strike a good balance between throughput and fairness. Furthermore, we propose novel methods to properly choose a finite number of available data transmission rates. We use both analytical results and simulation results to justify the usage of the proposed algorithms.
Exploiting Spatial and Temporal Correlations for Signal-Centric MAC in M2M Communications
(2016-01-01)
Gau, Rung-Hung
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Kuo, Fu-Ta
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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 a signal-centric medium access control scheme that simultaneously exploits spatial and temporal correlations among sensing results for machine-to-machine communications. To model sensing results with spatial and temporal correlations, we propose using a vector autoregressive process. To minimize the overall prediction error, we propose using the space-time predictive polling algorithm. In addition, we derive the a-priori prediction error that is essential for making an optimal polling decision. Simulation results show that the proposed space-time predictive polling algorithm could significantly outperform algorithms that use only temporal correlation.
Fusion-Based Cooperative Support Identification for Compressive Networked Sensing
(2020-02-01)
Yang, Ming-Hsun
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Wu, Jwo-Yuh
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Wang, Tsang-Yi
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Maunder, Robert G.
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Gau, Rung-Hung
This letter proposes a fusion-based cooperative support identification scheme for distributed compressive sparse signal recovery via resource-constrained wireless sensor networks. The proposed support identification protocol involves: (i) local sparse sensing for economizing data gathering and storage, (ii) local binary decision making for partial support knowledge inference, (iii) binary information exchange among active nodes, and (iv) binary data aggregation for support estimation. Then, with the aid of the estimated signal support, a refined local decision is made at each node. Only the measurements of those informative nodes will be sent to the fusion center, which employs a weighted l(1)-minimization for global signal reconstruction. The design of a Bayesian local decision rule is discussed, and the average communication cost is analyzed. Computer simulations are used to illustrate the effectiveness of the proposed scheme.
Group Partition and Dynamic Rate Adaptation for Scalable Capacity-Region-Aware Device-to-Device Communications
(2015-02-01)
Liou, Yi-Shing
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Gau, Rung-Hung
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Chang, Chung-Ju
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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 group partition and dynamic rate adaptation for scalable throughput optimization of capacity-region-aware device-to-device communications. We adopt network information theory that allows a receiving device to simultaneously decode multiple packets from multiple transmitting devices, as long as the vector of transmitting rates is inside the capacity region. Based on graph theory, devices are first partitioned into subgroups. To optimize the throughput of a subgroup, instead of directly solving an integer-linear programming problem, we propose using a fast iterative algorithm to select active devices and using aggression levels for rate adaptation based on channel state information. Simulation results show that the proposed algorithm is scalable and could significantly outperform the greedy algorithm by more than 50%.
Group Partition for Capacity-Region-Aware Device-to-Device Communications
(2014)
Liou, Yi-Shing
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Gau, Rung-Hung
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Chang, Chung-Ju
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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 a group partition approach for scalable throughput optimization of capacity-region-aware Device-to-Device communications. Instead of the conventional collision model, we adopt network information theory that allows a receiving device to simultaneously decode multiple packets from multiple transmitting devices, as long as the vector of transmitting rates is inside the capacity region. We propose a novel approach to partition devices into subgroups based on coloring a conflict graph and then solve an optimization problem for each subgroup. In particular, the formation of the conflict graph explicitly takes the capacity region into consideration. Simulation results show that the proposed approach could significantly outperform the greedy algorithm.
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.
Modeling the Slotted Nonpersistent CSMA Protocol for Wireless Access Networks with Multiple Packet Reception
(2009-10-01)
Gau, Rung-Hung
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電子工程學系及電子研究所
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Department of Electronics Engineering and Institute of Electronics
In this paper, we propose probability models for performance analysis of the slotted nonpersistent CSMA protocol in wireless networks with multiple packet reception. Variants of the CSMA protocol are well-known and play important roles in medium access control. Most of the previous works on CSMA use the conventional (0, 1, e) channel model, which prohibits an access point from concurrently receiving multiple packets from distinct nodes. In this paper, we use the Poisson random traffic model to study the performance of the slotted nonpersistent CSMA protocol when the access point could simultaneously receive two or more packets from different nodes in wireless access networks. We show that in terms of the network throughput, our analytical results are consistent with simulation results.
On Group Partition for Wireless Multicast Flow Control
(2012-06-01)
Gau, Rung-Hung
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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 partitioning group members into subgroups according to their instantaneous channel gains for single-hop wireless multicast flow control. We study the case in which there are a number of logical channels and the access point uses a logical channel to broadcast information to a subgroup. In particular, distinct logical channels could be allocated different levels of transmission power and fractions of time. In order to maximize the network throughput, we formulate and solve a discrete optimization problem. In addition, we use simulation results to show group partition is a promising approach for wireless multicast flow control.
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.
Opportunistic Polling for Capacity-Region-Aware MAC in Wireless Networks with Partial CSI
(2015)
Liou, Yi-Shing
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Gau, Rung-Hung
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Chang, Chung-Ju
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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 two opportunistic polling algorithms for capacity-region-aware medium access control (MAC) in wireless networks with partial channel state information. Based on the probability density function of the unknown channel gain, the proposed algorithms opportunistically exploit the capacity region of a multiple access channel in network information theory and benefit from successive interference cancellation techniques. Simulation results show that both algorithms could increase the network throughput. Furthermore, the active opportunistic polling algorithm could increase the network throughput by more than 30%.
Optimal Power Allocation and Signal Phase Selection in NOMA Wireless Relay Networks
(2020-01-01)
Liu, Yen-Chun
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Gau, Rung-Hung
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電信工程研究所
;
Institute of Communications Engineering
In this paper, we propose a novel approach of optimal power allocation and signal phase selection for NOMA wireless relay networks. Specifically, to optimize the network throughput, the base station and the stronger relay optimally allocate their transmission power, while the weaker relay selects an optimal signal phase. To maximize the end-to-end sum rate, we formulate a non-convex optimization problem and derive novel analytical results. In addition, we propose the four-candidate greedy algorithm for optimal power allocation and signal phase selection in NOMA wireless relay networks. Simulation results show that the proposed greedy algorithm could significantly improve the system performance.
Optimal Power Control and Beamforming for Full-Duplex Small Cell Wireless Networks
(2017-01-01)
Gau, Rung-Hung
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Xiao, Zh-Hong
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Yuan, Tseng-Lung
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電信工程研究所
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Institute of Communications Engineering
In this paper, we propose an optimization framework for selecting an optimal downlink beamforming vector and an optimal uplink transmission power level in full-duplex small cell wireless networks. We study the case in which the full-duplex base station is equipped with multiple antennas and each half-duplex UE is equipped with a single antenna. To benefit from recent progresses in all-digital self-interference cancellation, we formulate an optimization problem to achieve optimal beamforming and power control. Since the studied optimization problem is not convex, we first choose the optimal direction for the beamforming vector. Next, we adopt difference of concave programming to obtain the optimal magnitude of the beamforming vector and the optimal uplink transmission power level. Simulation results show that the proposed approach could significantly improve the performance of small cell wireless networks, especially when the residual self-interference is small.
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.
Optimal Traffic Engineering and Placement of Virtual Machines in SDNs with Service Chaining
(2017-01-01)
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 investigate the service capacity of a software-defined network with network function virtualization and service chaining. To obtain and achieve the service capacity, we formulate a mixed integer linear programming problem to jointly optimize the placement of virtual machines and the steering of network flows. In addition, we propose the greedy linear programming relaxation algorithm for efficiently obtaining solutions in three phases based on auxiliary linear programming problems and augmented service paths. We justify the usage of the proposed approach by analytical results and simulation results.