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Ph.D. Dissertation Defense: Yu-Han Yang
Friday, November 8, 2013
10:30 a.m.
Room 2211, Kim Building
For More Information:
Maria Hoo
301 405 3681
mch@umd.edu

ANNOUNCEMENT: Ph.D. Dissertation Defense

NAME: Yu-Han Yang

Committee:

Professor K. J. Ray Liu (Advisor/Chair)

Professor Min Wu

Professor Michael Rotkowitz

Dr. Zoltan Safar

Professor Lawrence C. Washington (Dean's Representative)

Date/Time: Friday, November 8, 2013 at 10:30 AM

Location: Room 2211, Kim Building

Title: Waveform Design and Network Selection in Wideband Small Cell Networks

Abstract:

The explosion in demand for wireless data traffic in recent years has triggered rapid development and pervasive deployment of wireless communication networks. To meet the exponentially increasing demand, a promising solution is the concept of wideband small cells, which is based on the idea of using broader frequency bandwidth and employing more efficient radio frequency resource reuse by dense deployment of wideband, short-range, low cost and low power base-stations. Broader bandwidth provides substantial degrees of freedom as well as challenges for system design due to the abundant multipaths and thus interference in high speed systems under large delay spread channels. Reducing the transmission range and increasing the number of cells permit better spatial reuse of spectrum. With the proliferation of wideband small cells, the strategy of selection among multiple

networks has significant impacts to the performance of users and to the load balance of the system. In this dissertation, we address these problems with a focus on waveform design and network selection.

In time-reversal communication systems, the time-reversal transmit waveform can boost the signal-to-noise ratio at the receiver with simple single-tap detection by utilizing channel reciprocity with very low transmitter complexity. However, the large delay spread gives rise to severe inter-symbol interference when the data rate is high, and the achievable transmission rate is further degraded in the multiuser downlink due to the inter-user rerference. We study the weighted sum rate optimization problem by means of waveform design in the time-reversal multiuser downlink. We propose a new power allocation algorithm, which is able to achieve comparable sum rate performance to that of globally optimal power allocation. Further, we study the joint waveform design and interference pre-cancellation by exploiting the symbol information to further improve the performance by utilizing the information of previous symbols. In the proposed joint design, the causal interference is subtracted using interference pre-cancellation and the anti-causal interference can be further suppressed by waveform design with more degrees of freedom.

The second part of this dissertation is concerned with the

wireless access network selection problem considering the negative network externality, i.e, the influence of subsequent users' decisions on an individual's throughput due to the limited available resources. We formulate the wireless network selection problem as a stochastic game with negative network externality and show that finding the optimal decision rule can be modelled as a multi-dimensional Markov decision process. A modified value

iteration algorithm is proposed to efficiently obtain the optimal decision rule with a simple threshold structure, which enables us to reduce the storage space of the strategy profile. We further investigate the mechanism design problem with incentive compatibility constraints, which enforce the networks to reveal the truthful state information. We analyze a data set of wireless LAN traces collected from campus networks, from which we observe

that the number of user arrivals is approximately Poisson

distributed; the session time and the waiting time to switch network can be approximated by exponential distributions. Based on the analysis, we formulate a wireless access network association game with both arriving strategy and switching strategy and validate the effectiveness of the proposed best response strategy.

This Event is For: Graduate • Faculty

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