English

Functional Kuppinger-Durisi-B\"{o}lcskei Uncertainty Principle

Functional Analysis 2024-02-08 v1 Information Theory math.IT

Abstract

Let X\mathcal{X} be a Banach space. Let {τj}j=1n,{ωk}k=1mX\{\tau_j\}_{j=1}^n, \{\omega_k\}_{k=1}^m\subseteq \mathcal{X} and {fj}j=1n\{f_j\}_{j=1}^n, {gk}k=1mX\{g_k\}_{k=1}^m\subseteq \mathcal{X}^* satisfy fj(τj)1 |f_j(\tau_j)|\geq 1 for all 1jn 1\leq j \leq n, gk(ωk)1|g_k(\omega_k)|\geq 1 for all 1km1\leq k \leq m. If xX{0}x \in \mathcal{X}\setminus \{0\} is such that x=θτθfx=θωθgxx=\theta_\tau\theta_f x=\theta_\omega\theta_g x, then we show that \begin{align}\label{FKDB} (1) \quad\quad\quad\quad \|\theta_fx\|_0\|\theta_gx\|_0\geq \frac{\bigg[1-(\|\theta_fx\|_0-1)\max\limits_{1\leq j,r \leq n,j\neq r}|f_j(\tau_r)|\bigg]^+\bigg[1-(\|\theta_g x\|_0-1)\max\limits_{1\leq k,s \leq m,k\neq s}|g_k(\omega_s)|\bigg]^+}{\left(\displaystyle\max_{1\leq j \leq n, 1\leq k \leq m}|f_j(\omega_k)|\right)\left(\displaystyle\max_{1\leq j \leq n, 1\leq k \leq m}|g_k(\tau_j)|\right)}. \end{align} We call Inequality (1) as \textbf{Functional Kuppinger-Durisi-B\"{o}lcskei Uncertainty Principle}. Inequality (1) improves the uncertainty principle obtained by Kuppinger, Durisi and B\"{o}lcskei \textit{[IEEE Trans. Inform. Theory (2012)]} (which improved the Donoho-Stark-Elad-Bruckstein uncertainty principle \textit{[SIAM J. Appl. Math. (1989), IEEE Trans. Inform. Theory (2002)]}). We also derive functional form of the uncertainity principle obtained by Studer, Kuppinger, Pope and B\"{o}lcskei \textit{[EEE Trans. Inform. Theory (2012)]}.

Keywords

Cite

@article{arxiv.2402.04255,
  title  = {Functional Kuppinger-Durisi-B\"{o}lcskei Uncertainty Principle},
  author = {K. Mahesh Krishna},
  journal= {arXiv preprint arXiv:2402.04255},
  year   = {2024}
}

Comments

9 Pages, 0 Figures