Joint Detection and Identification for Scalable Control of Nanorobot Swarms under Harsh Communication Constraints

TL;DR

This paper introduces JDAI, a control-oriented framework for scalable nanorobot swarm management under severe communication constraints, enabling subset activation without explicit messaging.

Contribution

It proposes a novel system-level approach that combines detection and identification for scalable control in resource-limited nanorobot systems.

Findings

Identification scales favorably asymptotically.

Finite blocklength effects impact practical implementation.

JDAI enables implicit subset activation without explicit communication.

Abstract

The coordination of large populations of highly constrained devices, such as micro- and nanoscale agents in biomedical applications, poses fundamental challenges to classical communication paradigms. In scenarios such as targeted drug delivery, devices operate under severe limitations in energy, size, and communication capabilities, while requiring precise and selective activation within spatially localized regions. In this work, we propose the framework of Joint Detection and Identification (JDAI) as a system-level approach for scalable control under such constraints. The key idea is to shift from reliable message transmission to a control-oriented paradigm, in which devices locally decide whether a broadcast signal is relevant. This enables implicit addressing and subset activation without the need for explicit per-device communication. We demonstrate how message identification can be combined with sensing. This enables the realization of a closed-loop system that integrates detection, communication, and actuation. Using the example of targeted nanorobot therapy, we analyze the interplay between sensing resolution, communication constraints, and system dynamics. In particular, we show that while identification exhibits favorable asymptotic scaling, practical implementations are governed by finite blocklength effects, noise, and latency. The proposed framework complements existing physical-layer communication approaches, including molecular, electromagnetic, and acoustic techniques, by providing a control-layer abstraction for scalable subset selection. Overall, JDAI connects identification-theoretic principles with system-level design to control large, resource-limited environments.