Date of Award
M.S. in Engineering Science
John N. Daigle
The objective of this thesis is to develop an algorithm for distributing content at small cells in 5G, wherein small cells (SCs) are set up instead of macro cells to serve mobile users. Users may simultaneously access between one and some maximum number of SCs, the actual number of SCs being drawn from a distribution. The source library is located away from these sites, and it stores files as a collection of RaptorQ-encoded symbols. By using RaptorQ symbols a very large number of distinct encoding symbols can be generated and a collection of received encoded symbols slightly larger than the number of source symbols can be used to recover the original file with linear time complexity. A number of these encoded symbols should be placed in SC caches to facilitate efficient download. The main objective of this thesis is to develop an algorithm of low complexity to determine the number of encoded symbols of each file that should be cached at each SCs as a function of cache size. Each file is characterized by the number of encoded symbols required to reconstruct the file at the user equipment and its download preference probability. The optimization problem considered is to determine the symbols distribution for a set of files stored in the library in order to minimize backhaul. The objective function is the average value of storing a set of encoded symbols per file download, constrained by available cache memory. Parameters are the files’ preference probabilities and sizes, coverage areas probabilities, the total number of files in the library, and the cache capacity. This study contributes to the literature by developing an n log n algorithm to solve the optimization problem, extending previous results from constant files size for all files to arbitrary actual files sizes for all files, and extending distribution portions from continuous fractions of files to integer number of symbols.
Freewan, Ibrahim Khalaf, "Maximizing Cache Value For Distributing Content Via Small Cells In 5G" (2017). Electronic Theses and Dissertations. 521.
Emphasis: Electrical Engineering