Goal-oriented quantization: Applications to convex cost functions with polyhedral decision space - Archive ouverte HAL
Communication Dans Un Congrès Année : 2022

Goal-oriented quantization: Applications to convex cost functions with polyhedral decision space

Résumé

In this paper, the situation in which a receiver has to execute a task from a quantized version of the information source of interest is considered. The task is modeled by the minimization problem of a general cost function f (x; g) for which the decision x has to be taken from quantized parameters g. Especially, we focus on the particular scenario where the decision space is a convex polyhedron with cost function being convex. Furthermore, we propose a new goal-oriented quantization algorithm by combining the procedure of iteratively expanding and reinstating decision set together with Jensen's inequality. Proposed method could also be extended to some non-convex scenarios, namely, weakly convex cost function whose eigenvalues of Hessian matrix w.r.t decision x are lower-bounded. Numerical results show that proposed algorithm can considerably reduce the optimality loss (OL) compared to conventional approaches or the required number of quantization bits to achieve a certain relative optimality loss.
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Dates et versions

hal-03874050 , version 1 (28-11-2022)

Identifiants

Citer

Hang Zou, Yifei Sun, Chao Zhang, Samson Lasaulce, Michel Kieffer, et al.. Goal-oriented quantization: Applications to convex cost functions with polyhedral decision space. 20th International Symposium on Modeling and Optimization in Mobile, Ad hoc, and Wireless Networks, WiOpt 2022, Sep 2022, Turin, Italy. pp.291-297, ⟨10.23919/WiOpt56218.2022.9930620⟩. ⟨hal-03874050⟩
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