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Approximation Algorithms Design for Resource Management in Communication Networks

Mao, Zhoujia
http://rave.ohiolink.edu/etdc/view?acc_num=osu1373966693

Abstract Details

, Doctor of Philosophy, Ohio State University, Electrical and Computer Engineering.
The broadcast nature of the wireless medium, random channel variations, unpredictable user demands, and stochastic variability of renewable resources, make the design and control of wireless systems highly challenging. One of the major difficulties stems from the fact that resources, such as energy, bandwidth and time slots, are scarce, and hence they must be efficiently utilized. Furthermore, the broadcast nature of the medium may cause obvious security and privacy issues, rendering the systems vulnerable to external active and passive eavesdropping. In this dissertation, we aim to develop the analytical foundations for the design of energy efficient and secure wireless networks that are robust with respect to the environmental variations as well as the interference from the wireless medium under Quality-of-Service (QoS) requirement. The outcome of our research will lead to practical distributed algorithms that are either optimal or provably efficient. We pursue our investigations in three main directions. In the first direction, we consider energy efficiency in wireless networks and address the problem in three different settings: 1) We study the problem of constructing maximum lifetime data gathering forest in an energy limited sensor network. We propose a polynomial time algorithm that has provable efficiency and practical implementation. 2) We then study the power allocation problem to maximize the total data rate of a decode-and-forward relay network. We propose an optimal distributed algorithm that has fast convergence. By studying these problems, we understand how to efficiently allocate energy resources and to provide service so that the desired objective can be achieved, and the service requirement is satisfied in various kinds of energy-driven networks. 3) We also investigate the sum rate maximization problem under QoS constraints in rechargeable networks. We develop a simple online control framework that achieves asymptotically optimal sum rate under the required constraint while the battery and data storage is sufficiently large. In the second direction, we consider physical layer security in wireless networks. Our objective is to achieve information secrecy/privacy by exploiting various phenomena that are present in the physical medium, such as static noise and interference. We start from a single link communication model in which the transmitter is required to transmit data securely to the receiver from the eavesdropper under perfect knowledge of channel information. We propose an algorithm that maximizes the long term average secrecy rate and also smoothes the secrecy transmission by using a key queue in order to achieve low queueing delay. We then extend this ideal scenario to the case when the transmitter does not have perfect knowledge of the eavesdropper's channel. In this scenario, some of the data that is supposed to be transmitted securely may not be actually secure from the eavesdropper, which leads to secrecy outage. Similarly, we propose an algorithm that maximizes the average secrecy rate and smoothes the secrecy transmission rate subject to QoS constraint on secrecy outage by using a key queue. Finally, we extend this secrecy rate allocation framework to a downlink cellular network where each receiver tends to receive independent information securely under eavesdropping of other receivers from the information broadcasted by the base station. Finally, we investigate scheduling delay sensitive jobs in multihop networks. We aim to maximum the weighted revenue of packets that arrive to the destination before their deadlines. We first explore the difficulties in developing optimal online algorithms that have the same performance as the optimal offline algorithm, and then propose an optimal online algorithm for a restrictive network topology. We finally develop an online algorithm that is order optimal in competitive ratio among all online algorithms for general multihop networks.
Ness Shroff (Advisor)
C. Emre Koksal (Committee Member)
Atilla Eryilmaz (Committee Member)
Computer Engineering; Computer Science; Electrical Engineering

Recommended Citations

Citations

  • Mao, Z. (n.d.). Approximation Algorithms Design for Resource Management in Communication Networks [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1373966693

    APA Style (7th edition)

  • Mao, Zhoujia. Approximation Algorithms Design for Resource Management in Communication Networks. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1373966693.

    MLA Style (8th edition)

  • Mao, Zhoujia. "Approximation Algorithms Design for Resource Management in Communication Networks." Doctoral dissertation, Ohio State University. Accessed APRIL 28, 2024. http://rave.ohiolink.edu/etdc/view?acc_num=osu1373966693

    Chicago Manual of Style (17th edition)

osu1373966693
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This open access ETD is published by The Ohio State University and OhioLINK.