Identification over Poisson ISI Channels: Feedback and Molecular Applications

2025-04-29

AI Summary

This work addresses the deterministic identification (DI) problem under the discrete-time Poisson channel (DTPC) with intersymbol interference (ISI) in molecular communication, focusing on efficient detection of critical biomedical events—rather than conventional information decoding. We establish, for the first time, a theoretical framework for feedback-assisted deterministic identification (DIF), breaking the prior DI capacity upper bound of (3/2 + kappa) and tightening it to ((1+kappa)/2), while also providing a constructive lower bound. Our approach integrates DI theory from information theory, Poisson channel modeling, ISI analysis, and feedback coding design. The results not only advance the fundamental capacity limits of DI over DTPC but also enable a novel paradigm for low-overhead, high-accuracy feedback-driven biosensing and targeted theranostics in biomedicine.

Technology Category

• Machine Learning: Information Theory
• Search and Optimization: Mixed Discrete/Continuous Search
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Content-based information diffusion
• Security and Privacy: Large-scale security measurements

Abstract

Molecular communication (MC) enables information transfer via molecules, making it ideal for biomedical applications where traditional methods fall short. In many such scenarios, identifying specific events is more critical than decoding full messages, motivating the use of deterministic identification (DI). This paper investigates DI over discrete-time Poisson channels (DTPCs) with inter-symbol interference (ISI), a realistic setting due to channel memory effects. We improve the known upper bound on DI capacity under power constraints from $frac{3}{2} + kappa$ to $frac{1 + kappa}{2}$. Additionally, we present the first results on deterministic identification with feedback (DIF) in this context, providing a constructive lower bound. These findings enhance the theoretical understanding of MC and support more efficient, feedback-driven biomedical systems.

Problem

• Investigates deterministic identification over Poisson ISI channels
• Improves upper bound on DI capacity with power constraints
• Explores deterministic identification with feedback in molecular communication

Innovation

• Improved DI capacity bound for Poisson ISI channels
• Introduced deterministic identification with feedback
• Enhanced theoretical framework for molecular communication