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Register a new userOn the Verification of a WiMax Design Using Symbolic Simulation
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EPTCS
Publication date
2013
fields
Informatics Engineering
and research's language is
English
Authors
Salim Ismail Al-Akhras
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In top-down multi-level design methodologies, design descriptions at higher levels of abstraction are incrementally refined to the final realizations. Simulation based techniques have traditionally been used to verify that such model refinements do not change the design functionality. Unfortunately, with computer simulations it is not possible to completely check that a design transformation is correct in a reasonable amount of time, as the number of test patterns required to do so increase exponentially with the number of system state variables. In this paper, we propose a methodology for the verification of conformance of models generated at higher levels of abstraction in the design process to the design specifications. We model the system behavior using sequence of recurrence equations. We then use symbolic simulation together with equivalence checking and property checking techniques for design verification. Using our proposed method, we have verified the equivalence of three WiMax system models at different levels of design abstraction, and the correctness of various system properties on those models. Our symbolic modeling and verification experiments show that the proposed verification methodology provides performance advantage over its numerical counterpart.
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We present a faster symbolic algorithm for the following central problem in probabilistic verification: Compute the maximal end-component (MEC) decomposition of Markov decision processes (MDPs). This problem generalizes the SCC decomposition problem of graphs and closed recurrent sets of Markov chains. The model of symbolic algorithms is widely used in formal verification and model-checking, where access to the input model is restricted to only symbolic operations (e.g., basic set operations and computation of one-step neighborhood). For an input MDP with $n$ vertices and $m$ edges, the classical symbolic algorithm from the 1990s for the MEC decomposition requires $O(n^2)$ symbolic operations and $O(1)$ symbolic space. The only other symbolic algorithm for the MEC decomposition requires $O(n sqrt{m})$ symbolic operations and $O(sqrt{m})$ symbolic space. A main open question is whether the worst-case $O(n^2)$ bound for symbolic operations can be beaten. We present a symbolic algorithm that requires $widetilde{O}(n^{1.5})$ symbolic operations and $widetilde{O}(sqrt{n})$ symbolic space. Moreover, the parametrization of our algorithm provides a trade-off between symbolic operations and symbolic space: for all $0<epsilon leq 1/2$ the symbolic algorithm requires $widetilde{O}(n^{2-epsilon})$ symbolic operations and $widetilde{O}(n^{epsilon})$ symbolic space ($widetilde{O}$ hides poly-logarithmic factors). Using our techniques we present faster algorithms for computing the almost-sure winning regions of $omega$-regular objectives for MDPs. We consider the canonical parity objectives for $omega$-regular objectives, and for parity objectives with $d$-priorities we present an algorithm that computes the almost-sure winning region with $widetilde{O}(n^{2-epsilon})$ symbolic operations and $widetilde{O}(n^{epsilon})$ symbolic space, for all $0 < epsilon leq 1/2$.
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