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Type of Document	Dissertation
Author	Foltz, Kevin
URN	etd-05132005-151145
Persistent URL	http://resolver.caltech.edu/CaltechETD:etd-05132005-151145
Title	Periodic broadcast scheduling for data distribution
Degree	PhD
Option	Electrical Engineering
Advisory Committee	Advisor Name Title Jehoshua Bruck Committee Chair
Keywords	none
Date of Defense	2002-05-17
Availability	unrestricted
Abstract As wireless computer networks grow in size and complexity, we are faced with the problem of providing scalable, high-bandwidth service to their users. Wired networks typically use "data pull," where users send requests to a server and the server responds with the desired information. In the wireless domain, "data push" promises to provide better performance for many applications [1]. The broadcast domain that is typical of wireless communication is very effective in distributing information to large audiences. The idea of broadcast disks has been around since the Teletext system [3]. There is now an interest in applying these ideas to wireless computer networks. There are some interesting research questions about scheduling for data distribution. Computing optimal schedules has been shown to be difficult [18]. The optimal schedules themselves, however, seem to be less complex, and often periodic [4]. Xu [24] looks at the scheduling of streaming data, which involves splitting the data into smaller pieces. The idea of error correction is also important for wireless transmission due to the noisy nature of the channel [6]. We look at scheduling data for broadcast. We compare time-division scheduling and frequency-division scheduling for data items of equal length. We show that time-division is better for sending dynamic data. We then find optimal time-division schedules for two items. We show how the freedom to split items into smaller pieces can give improvements in performance. With a single split, where each of two items is split in half, we find the optimal schedules for items of equal length. We continue with the idea of splitting items, and show what happens when the number of splits is very large. Then, we examine what happens when we add streaming data to our broadcast. We compare time-division and frequency-division as before, and now also look at a mix of the two. We prove bounds on where the mix is the best broadcast method.
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