→Research

My current interests include:

- Algorithm game theory and its applications in communication networks,

- Machine learning and artificial intelligence with applications to learning for multi-agent stochastic systems,

- Distributed optimization methods and algorithms for wireless networks,

- Cognitive radio, heterogeneous networks, LTE Advanced system, carrier aggregation.

1. Joint Distributed Optimization for Cognitive Radio Networks

By allowing the mobile device to flexibly adapt its operation to the surrounding environment, cognitive radio networks have the potential to solve the spectrum over-subscription/under-utilization problem in wireless networks. In a cognitive radio network, secondary users (SUs) can access the licensed spectrum allocated to primary users (PUs) if it is unoccupied or under-utilized. The main challenge for this network is that SUs need to intelligently decide what and how much licensed resources (e.g., time, spectrum and space) can be further exploited within the tolerable interference level of PUs.

In this work, we consider an OFDMA-based cognitive radio network model in which the SUs distributedly compete with each other in the spectrum owned by multiple PUs. We investigate three types of interactions in this cognitive radio network. The first one is the interaction among PUs. By assuming each PU can charge a certain price to the SUs for accessing its spectrum, we study the effects of different pricing competition strategies on the payoff of the PU networks. The second one is the interaction among SUs. We study how the SUs can compete for the limited number of sub-bands owned by the PUs to maximize the SUs’ performance. To further improve the efficiency of the SU network, we allow multiple SUs to share the same sub-band and cooperate with one another to send and receive signals.

Reference:

[1] Yong Xiao, Guoan Bi, Dusit Niyato and Luiz A. Dasilva, "A Hierarchical Game Theoretic Framework for Cognitive Radio Networks", IEEE Journal on Selected Areas in Communications: Cognitive Radio Series, vol. 30, no. 10, pp. 2053-2069, Nov. 2012.

[2] Yong Xiao, Guoan Bi and Dusit Niyato, "A Simple Distributed Power Control Algorithm for Cognitive Radio networks", IEEE Transaction on Wireless Commun., vol. 10, no. 11, pp. 3594-3600, Nov. 2011.

[3] Yong Xiao and Luiz A. DaSilva, "Dynamic Pricing Coalitional Game for Cognitive Radio Networks", IFIP Networking 2012 Workshop, Prague, Czech Republic, 25 May 2012. in Lecture Notes in Computer Science, vol 7291, pp. 19-26.

2. Game Theory and Its Application for Wireless Networks

When studying a cognitive radio network, it is important to take into account the interaction between different autonomous decision maker. This motivates the use of game theory to analyze resource management in wireless networks.

Reference:

[1] Yong Xiao, Jianwei Huang, Chau Yuen, and Luiz A. DaSilva, “Fairness and Efficiency Tradeoffs for User Cooperation in Distributed Wireless Networks,” IEEE International Conference on Computer Communications (Infocom), Turin, Italy, April 14-19, 2013.

[2] Yong Xiao, Guoan Bi and Dusit Niyato, "Game Theoretic Analysis for Spectrum Sharing with Multi-hop Relaying", IEEE Transaction on Wireless Commun., vol. 10, no. 5, pp. 1527 -1537, May 2011.

3. LTE and Heterogeneous Networks

Carrier aggregation (CA) is the LTE-Advanced (LTE-A) term that refers to the process of aggregating different blocks of spectrum, called component carriers (CCs), to form larger transmission bandwidths.

In this work, we extend the concept of CA by investigating the ability of independent mobile network operators to dynamically schedule access to portions of each other’s

spectrum. In our model, each mobile network operator allows a portion of its spectrum, i.e. the range of frequencies exclusively assigned to it under a licence, to be aggregated by others for a limited time. We refer to this type of dynamic access for the purpose of CA between separate and independent mobile network operators as Dynamic Inter-network Carrier Aggregation (DI-CA).

We study two fundamental problems for this system: 1) Under which conditions can DI-CA improve performance for all the mobile network operators? 2) How to achieve

distributed scheduling when each mobile network operator does not have any instantaneous information (i.e., payoffs, aggregated bandwidth, etc.) about others?

Reference:

[1] Yong Xiao, Chau Yuen, Paolo Di Francesco and Luiz A. DaSilva, “Dynamic Spectrum Scheduling for Carrier Aggregation: A Game Theoretic Approach,” IEEE International Conference on Communications (ICC), Budapest, Hungary, June 9-13, 2013.

[2] Yong Xiao, T. Forde, I. Macaluso, L. A. DaSilva and L. Doyle, "Spatial Spectrum Sharing-based Carrier Aggregation for Heterogeneous Networks", IEEE Globecom, Anaheim, CA, December 3-7, 2012.

4. Information Theory for Cooperative Networks

The diversity-multiplexing tradeoff is one of the most important criteria to evaluate the performance of wireless relay networks. Previous work shows that, compared to decode-and-forward and estimate-and-forward relaying protocols, amplify-and-forward achieves the worst diversity-multiplexing tradeoff and cannot reach the diversity-multiplexing tradeoff upper bound. In this paper, we propose a new relaying protocol, called dynamic amplify-and-forward, which allows the relay to adapt the receiving and forwarding time durations to the channel conditions. We show that dynamic amplify-and-forward achieves the diversity-multiplexing tradeoff upper bound when the multiplexing gain r is between 0 and α, where 0 < α < 0.5 is a constant set according to the relaying channel conditions.

Reference:

[1] Yong Xiao, Luiz A. Dasilva and X. J. Zhang, "On the Diversity-Multiplexing Tradeoff of an Improved Amplify-and-forward Relaying Strategy", IEEE Communications Letters, vol. 16, no. 4, pp. 482 - 485, April 2012.

[2] Yong Xiao, Guoan Bi, "An Improved Amplify-and-Forward Strategy for User Cooperation Channels", ISCIT 2009, Korea, 10-13 Oct., 2009.

[3] Yong Xiao and Guoan Bi, "Spectrum Sharing with Multi-hop Relaying", APSIPA Annual Summit and Conference, Singapore, 15-17 Dec., 2010.

Page updated

Google Sites

Report abuse