stem's or circuit's behavior, expressions should be kept as simple as possible. The SymbolicHTM algorithm presented in this section takes a block diagram as in- put and produces symbolic expressions for the elements of the system's overall input- output HTM. It demonstrates that traditional techniques for automated symbolic analy- sis of LTI systems [Fern98, Gie91, Lin91] can be extended to handle LPTV systems as well. Moreover, for symbolic LPTV system analysis, the SymbolicHTM algorithm is more efficient than techniques based on weakly nonlinear time-invariant symbolic circuit analysis [Wamb98a]. This is because of the fact that SymbolicHTM exploits the (time-varying) linear nature of a multiplication with a periodic carrier wave. We demonstrate the algorithm by generating expressions for the input-output HTM ele- ments of two different downconversion stages. In what follows, we first review prior art on symbolic circuit and system analysis. Next, we discuss the construction of the block diagrams that are needed as input (sec- tion 4.3.3). This is followed by a description of the data structures (section 4.3.4) and the computational flow (section 4.3.5) of the Symbolic HTM algorithm. We conclude this section with experimental results obtained from the application of SymbolicHTM to two different downconversion stages (section 4.3.7 and 4.3.8). 4.3.1 Prior art Symbolic analysis has a long standing history [Fern98, Gie91, Lin91]. Traditionally, it focuses on the behavior of LTI networks with extensions towards symbolic weakly- nonlinear circuit analysis [Wamb98a]. Expressions are derived using analytical tech- 4.3 AUTOMATED SYMBOLICLPTV SYSTEM ANALYSIS niques (either matrix-based or graph-based). More recently, a fitting-based approach for constructing symbolic expressions was suggested as an alternative to the analytical methods [Dae03]. 4.3.1.1 LTI network analysis The first research boom in the field of symbolic circuit analysis focused its efforts on LTI networks [Fern98, Gie91, Lin91]. These LTI networks were