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

2026-03-30

AI Summary

This work addresses the significant challenge of achieving scalable, precise, and selective control of large-scale nanorobot swarms under severe constraints on energy, size, and communication capabilities. The authors propose a joint detection and identification (JDAI) framework that, for the first time, integrates identification theory with system-level control design. Departing from conventional reliable communication paradigms, the approach employs an implicit addressing mechanism enabling individual devices to locally assess the relevance of broadcast signals and thereby activate designated subsets. This method seamlessly unifies sensing, communication, and actuation, and is compatible with diverse physical-layer technologies, including molecular, electromagnetic, and acoustic modalities. Theoretical analysis demonstrates favorable asymptotic scalability, while practical performance is governed by finite code length, noise, and latency, offering a viable control architecture for applications such as targeted nanotherapeutics.

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.

Problem

• nanorobot swarms
• scalable control
• harsh communication constraints
• targeted drug delivery
• subset activation

Innovation

• Joint Detection and Identification
• scalable control
• nanorobot swarms
• communication constraints
• implicit addressing