Non-linear programming has been extensively used in wireless telecommunication systems design. An important criterion in optimization is the minimization of mean square error. This thesis examines two applications: peak to average power ratio (PAPR) reduction in orthogonal frequency division multiplexing (OFDM) systems and wireless airtime traffic estimation. These two applications are both of interests to wireless service providers. PAPR reduction is implemented in the handheld devices and low complexity is a major objective. On the other hand, exact traffic prediction can save a huge cost for wireless service providers by better resource management through off-line operations. <br /><br /> High PAPR is one of the major disadvantages of OFDM system which is resulted from large envelope fluctuation of the signal. Our proposed technique to reduce the PAPR is based on constellation shaping that starts with a larger constellation of points, and then the points with higher energy are removed. The constellation shaping algorithm is combined with peak reduction, with extra flexibilities defined to reduce the signal peak. This method, called MMSE-Threshold, has a significant improvement in PAPR reduction with low computational complexity. <br /><br /> The peak reduction formulated into a quadratic minimization problem is subsequently optimized by the semidefinite programming algorithm, and the simulation results show that the PAPR of semidefinite programming algorithm (SDPA) has noticeable improvement over MMSE-Threshold while SDPA has higher complexity. Results are also presented for the PAPR minimization by applying optimization techniques such as hill climbing and simulated annealing. The simulation results indicate that for a small number of sub-carriers, both hill climbing and simulated annealing result in a significant improvement in PAPR reduction, while their degree of complexity can be very large. <br /><br />
The second application of non-linear optimization is in airtime data traffic estimation. This is a crucial problem in many organizations and plays a significant role in resource management of the company. Even a small improvement in the data prediction can save a huge cost for the organization. Our proposed method is based on the definition of extra parameters for the basic structural model. In the proposed technique, a novel search method that combines the maximum likelihood estimation with mean absolute percentage error of the estimated data is presented. Simulated results indicate a substantial improvement in the proposed technique over that of the basic structural model and seasonal autoregressive integrated moving average (SARIMA) package. In addition, this model is capable of updating the parameters when new data become available.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:OWTU.10012/818 |
| Date | January 2006 |
| Creators | Kohandani, Farzaneh |
| Publisher | University of Waterloo |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
| Format | application/pdf, 518321 bytes, application/pdf |
| Rights | Copyright: 2006, Kohandani, Farzaneh. All rights reserved. |
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