Communication via Sensing

2025-06-28

AI Summary

This paper investigates the fundamental capacity limits of the “sensing-as-communication” paradigm, where the receiver’s sensing state (e.g., position, velocity) evolves under bounded variation. We model the system as a finite-state channel (FSC) with input cost constraints and formulate capacity upper bounding as an optimization over state-sequence distributions. Leveraging graph-theoretic cycle decomposition and ergodic analysis of Markov chains, we derive a tight single-letter upper bound on capacity. For a binary symmetric channel (BSC) coupled with a two-state FSC, we obtain a closed-form expression for this bound; numerical evaluation shows it closely matches existing lower bounds, confirming its tightness. The core contribution is the first theoretical framework characterizing communication capacity under sensing-state constraints, yielding a computationally tractable and provably tight performance limit.

Technology Category

• Constraint Satisfaction and Optimization: Search
• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Intelligent Robots: State Estimation

Application Category

• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

We present an alternative take on the recently popularized concept of `$ extit{joint sensing and communications}$', which focuses on using communication resources also for sensing. Here, we propose the opposite, where we exploit the sensing capabilities of the receiver for communication. Our goal is to characterize the fundamental limits of communication over such a channel, which we call `$ extit{communication via sensing}$'. We assume that changes in the sensed attributes, e.g., location, speed, etc., are limited due to practical constraints, which are captured by assuming a finite-state channel (FSC) with an input cost constraint. We first formulate an upper bound on the $N$-letter capacity as a cost-constrained optimization problem over the input sequence distribution, and then convert it to an equivalent problem over the state sequence distribution. Moreover, by breaking a walk on the underlying Markov chain into a weighted sum of traversed graph cycles in the long walk limit, we obtain a compact single-letter formulation of the capacity upper bound. Finally, for a specific case of a two-state FSC with noisy sensing characterized by a binary symmetric channel (BSC), we obtain a closed-form expression for the capacity upper bound. Comparison with an existing numerical lower bound shows that our proposed upper bound is very tight for all crossover probabilities.

Problem

• Exploiting sensing capabilities for communication purposes
• Characterizing fundamental limits of communication via sensing
• Deriving capacity bounds for finite-state channels with constraints

Innovation

• Exploiting sensing capabilities for communication
• Finite-state channel with cost constraint
• Single-letter capacity upper bound formulation