DESIGN OF A MINIATURIZED SOP INTEGRATED GNSS RF FRONT-END MODULE

Navigation devices used to be bulky and expensive and were not widely commercialized for personal use. Nowadays, all useful electronic devices are turning into being handheld so that they can be conveniently used anytime and anywhere. One can claim that almost any mobile phone, used today, has quite strong navigational capabilities that can efficiently work anywhere in the globe. No matter where you are, you can easily know your exact location and make your way smoothly to wherever you would like to go. This couldn’t have been made possible without the existence of efficient and small microwave circuits responsible for the transmission and reception of high quality navigation signals. This thesis is mainly concerned with the design of novel highly miniaturized and efficient filtering components working in the Global Navigational Satellite Systems (GNSS) frequency band to be integrated within an efficient Radio Frequency (RF) front-end module (FEM). A System-on-Package (SoP) integration technique is adopted for the design of all the components in this thesis. Two novel miniaturized filters are designed, where one of them is a wideband filter targeting the complete GNSS band with a fractional bandwidth of almost 50% at a center frequency of 1.385 GHz. This filter utilizes a direct inductive coupling topology to achieve the required wide band performance. It also has very good out-of-band rejection and low IL. Whereas the other dual band filter will only cover the lower and upper GNSS bands with a rejection notch in between the two bands. It has very good inter band rejection. The well-known “divide and conquer” design methodology was applied for the design of this filter to help save valuable design and optimization time. Moreover, the performance of two commercially available ultra-Low Noise Amplifiers (LNAs) is studied. The complete RF FEM showed promising preliminary performance in terms of noise figure, gain and bandwidth, where it out performed other commercial front-ends in these three aspects. All the designed circuits are fabricated and tested. The measured results are found to be in good agreements with the simulations.

New Rigorous Decomposition Methods for Mixed-integer Linear and Nonlinear Programming

Process systems design, operation and synthesis problems under uncertainty can readily be formulated as two-stage stochastic mixed-integer linear and nonlinear (nonconvex) programming (MILP and MINLP) problems. These problems, with a scenario based formulation, lead to large-scale MILPs/MINLPs that are well structured. The first part of the thesis proposes a new finitely convergent cross decomposition method (CD), where Benders decomposition (BD) and Dantzig-Wolfe decomposition (DWD) are combined in a unified framework to improve the solution of scenario based two-stage stochastic MILPs. This method alternates between DWD iterations and BD iterations, where DWD restricted master problems and BD primal problems yield a sequence of upper bounds, and BD relaxed master problems yield a sequence of lower bounds. A variant of CD, which includes multiple columns per iteration of DW restricted master problem and multiple cuts per iteration of BD relaxed master problem, called multicolumn-multicut CD is then developed to improve solution time. Finally, an extended cross decomposition method (ECD) for solving two-stage stochastic programs with risk constraints is proposed. In this approach, a CD approach at the first level and DWD at a second level is used to solve the original problem to optimality. ECD has a computational advantage over a bilevel decomposition strategy or solving the monolith problem using an MILP solver. The second part of the thesis develops a joint decomposition approach combining Lagrangian decomposition (LD) and generalized Benders decomposition (GBD), to efficiently solve stochastic mixed-integer nonlinear nonconvex programming problems to global optimality, without the need for explicit branch and bound search. In this approach, LD subproblems and GBD subproblems are systematically solved in a single framework. The relaxed master problem obtained from the reformulation of the original problem, is solved only when necessary. A convexification of the relaxed master problem and a domain reduction procedure are integrated into the decomposition framework to improve solution efficiency. Using case studies taken from renewable resource and fossil-fuel based application in process systems engineering, it can be seen that these novel decomposition approaches have significant benefit over classical decomposition methods and state-of-the-art MILP/MINLP global optimization solvers.