Browsing by Author "Tseng, Li-Chuan"

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Cognitive Radio Networks: Game Modeling and Self-organization Using Stochastic Learning
(2013-01-01)
Lin, Chen-Hao
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Tseng, Li-Chuan
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Huang, ChingYao
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電機工程學系
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Department of Electrical and Computer Engineering
Due to the high demand of spectrum utilization, cognitive radio (CR) network has been a promising solution to the problem of spectrum scarcity by using dynamic spectrum access technique. In this paper, we study one of the CR network architectures where the CR base stations (CRBSs) demand spectrum resources for the CR users to directly access and utilize. We applied an economical Cournot Game model to the system where the CRBSs are the players in this game. In order to optimize the game, we propose a stochastic learning (SL) based scheme for the CRBSs to adjust the demand amount of resources based on the action-reward history. Numerical results show the convergence toward a Nash Equilibrium (NE) point, and the system performs well in terms of the total utility comparing with other schemes.
Distributed channel assignment for network MIMO: game-theoretic formulation and stochastic learning
(2015-05-01)
Tseng, Li-Chuan
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Chien, Feng-Tsun
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Chang, Ronald Y.
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Chung, Wei-Ho
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Huang, ChingYao
The cooperative frequency reuse among base stations (BSs) can improve the system spectral efficiency by reducing the intercell interference through channel assignment and precoding. This paper presents a game-theoretic study of channel assignment for realizing network multiple-input multiple-output (MIMO) operation under time-varying wireless channel. We propose a new joint precoding scheme that carries enhanced interference mitigation and capacity improvement abilities for network MIMO systems. We formulate the channel assignment problem from a game-theoretic perspective with BSs as the players, and show that our game is an exact potential game given the proposed utility function. A distributed, stochastic learning-based algorithm is proposed where each BS progressively moves toward the Nash equilibrium (NE) strategy based on its own action-reward history only. The convergence properties of the proposed learning algorithm toward an NE point are theoretically and numerically verified for different network topologies. The proposed learning algorithm also demonstrates an improved capacity and fairness performance as compared to other schemes through extensive link-level simulations.
Distributed Channel Selection in Multilink MISO Networks: Stochastic Learning under Time-Varying Channel States
(2014)
Tseng, Li-Chuan
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Chien, Feng-Tsun
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Chang, Ronald Y.
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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 study the channel selection problem for selfish and altruistic precoding in multilink multiple-input single-output (MISO) networks from a distributed game-theoretic perspective. Our goal is to find for each link a proper channel selection strategy that is robust against time-varying channel states. This motivates the development of stochastic learning that finds Nash equilibrium (NE) of an expected game. The convergence properties of the proposed learning algorithm are theoretically and numerically verified. The proposed algorithm demonstrates good sum-rate performance in the system-level simulation of a multilink MISO network based on the 3GPP-LTE model.
Distributed Spectrum Trading in Multiple-Seller Cognitive Radio Networks
(2014)
Tseng, Li-Chuan
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Chien, Feng-Tsun
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Chang, Ronald Y.
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Chung, Wei-Ho
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電子工程學系及電子研究所
This paper studies spectrum trading in cognitive radio networks in which multiple service providers (SPs) sell unused spectrum to multiple unlicensed secondary users (SUs). Motivated by the nature of the problem with new considerations, spectrum trading is modeled as a multi-leader multi-follower expected Stackelberg game with two levels of competition. The SPs as leaders compete in offering subscription prices (upper-level subgame) and the SUs as followers compete in selecting service from the SPs (lower-level subgame). The lower-level subgame incorporates the time-varying spectrum availability as the external state so that the proposed scheme does not require knowledge of dynamic spectrum availability. To achieve self-organized network operation, we propose decentralized, stochastic learning-based algorithms for the game. The convergence properties of the proposed algorithms toward the Nash equilibrium (NE) are theoretically and numerically studied. The proposed scheme demonstrates good utility performance for the SUs as compared to other service selection schemes.
Downlink Scheduling in Network MIMO Using Two-Stage Channel State Feedback
(2012)
Tseng, Li-Chuan
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Jin, Xin
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Marzouki, Abdelwaheb
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Huang, ChingYao
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交大名義發表
In this paper, we propose a user scheduling scheme which is based merely on the ergodic rate. We derive the ergodic rate of each user for our network MIMO system from the average Signal-to-Noise Ratio (SNR) and the covariance of prediction error. Further more, we design a joint precoding for the selected users. The precoding matrices are contrived according to the predicted Channel State Information at Transmitter (CSIT) in the future time slot to reduce the deviation caused by delay. Such a network MIMO system is under consideration, where the users are cooperatively served by multiple Base Stations (BSs). User scheduling and joint precoding are executed by a central unit connected to all BSs. Both the above functions require the CSIT which is acquired by an uplink feedback overhead. Simulation results demonstrate a conspicuous improvement in user spectral efficiency with reduced feedback.
Dynamic Resource Management for OFDMA-based Femtocells in the Uplink
(2011)
Tseng, Li-Chuan
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Huang, ChingYao
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Hanif, Ahmed Farhan
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交大名義發表
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National Chiao Tung University
Femtocells are small base stations that are usually installed in indoor environments to improve the data rate in coverage holes of cellular systems. Since femtocells are usually deployed in an ad hoc manner by users and share the same frequency bands, interference mitigation becomes the main concerns of resource management function. In this paper, we consider the OFDMA-based femtocell networks, and treat the resource allocation problem by developing dynamic subchannel assignment and power allocation algorithms. The proposed algorithms utilize the user diversity within femtocells and take into account the interference conditions of local and neighboring femtocells. The simulation results show that such dynamic resource assignment provide higher system throughput as well as per-user QoS. Additionally subchannel usage ratio is increased.
Network Selection in Cognitive Heterogeneous Networks Using Stochastic Learning
(2013-12-01)
Tseng, Li-Chuan
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Chien, Feng-Tsun
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Zhang, Daqiang
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Chang, Ronald Y.
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Chung, Wei-Ho
Coexistence of multiple radio access technologies (RATs) is a promising paradigm to improve spectral efficiency. This letter presents a game-theoretic network selection scheme in a cognitive heterogeneous networking environment with time-varying channel availability. We formulate the network selection problem as a noncooperative game with secondary users (SUs) as the players, and show that the game is an ordinal potential game (OPG). A decentralized, stochastic learning-based algorithm is proposed where each SU's strategy progressively evolves toward the Nash equilibrium (NE) based on its own action-reward history, without the need to know actions in other SUs. The convergence properties of the proposed algorithm toward an NE point are theoretically and numerically verified. The proposed algorithm demonstrates good throughput and fairness performances in various network scenarios.
Robust online music identification using spectral entropy in the compressed domain
(2014)
Yin, Changqing
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Li, Wei
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Luo, Yuanqing
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Tseng, Li-Chuan
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電子工程學系及電子研究所
Audio identification has been an active research field with wide applications for years. However, most of previously reported methods work on the raw audio format in spite of the fact that nowadays compressed format audio, especially MP3 music, has grown into the dominant way to transmit on the Internet. So far, most of the previous methods take advantage of MDCT coefficients or derived energy type of features. As a first attempt, in this paper we propose a novel audio fingerprinting algorithm utilizing compressed-domain spectral entropy as audio features. Such fingerprint exhibits strong robustness against various audio signal distortions such as recompression, noise interference, echo addition, equalization, band-pass filtering, pitch shifting, and moderate time-scale modification etc. In addition, the algorithm for compressed-domain can be applied in Internet of Things (IoT). Experimental results show that in our test database which is composed of 9823 popular songs, a 5s music clip is able to transmit in IoT and identify its original recording, with more than 90% top five precision rate even under the above severe time-frequency audio signal distortions.
Self-Organized Cognitive Sensor Networks: Distributed Channel Assignment for Pervasive Sensing
(2014)
Tseng, Li-Chuan
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Chien, Feng-Tsun
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Marzouki, Abdelwaheb
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Chang, Ronald Y.
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Chung, Wei-Ho
We study the channel assignment strategy in multichannel wireless sensor networks (WSNs) where macrocells and sensor nodes are overlaid. The WSNs dynamically access the licensed spectrum owned by the macrocells to provide pervasive sensing services. We formulate the channel assignment problem as a potential game which has at least one pure strategy Nash equilibrium (NE). To achieve the NE, we propose a stochastic learning-based algorithm which does not require the information of other players' actions and the time-varying channel. Cluster heads as players in the game act as self-organized learning automata and adjust assignment strategies based on their own action-reward history. The convergence property of the proposed algorithm toward pure strategy NE points is shown theoretically and verified numerically. Simulation results demonstrate that the learning algorithm yields a 26% sensor node capacity improvement as compared to the random selection, and incurs less than 10% capacity loss compared to the exhaustive search.