Summary
Sometimes, devices that send messages can't use their usual communication tools, so they send signals by changing their physical positions instead. The authors studied how multiple sensors spread out around the devices can work together to better tell these physical signals apart. They found that combining what different sensors see can solve confusion that a single sensor cannot. This teamwork among sensors can make decoding messages more reliable and faster than just repeating the measurements at one place. The authors also created methods to design where sensors should be placed and how signals should be arranged to improve communication accuracy.
embodied communicationaccess pointsmessage distinguishabilityGaussian sensing modelChernoff informationerror floorreceiver cooperationmessage decoding reliabilityphysical signal encodingsensor network design
Abstract
Embodied agents need to convey information to surrounding infrastructure, but their active communication interfaces may be unavailable, constrained, or intentionally inactive.Their ability to manipulate physical states offers a complementary path: messages can be encoded in deliberately selected configurations and recovered through infrastructure sensing. This principle underlies embodied communication. Yet physical differences do not guarantee distinguishable messages: a single sensing viewpoint may leave ambiguities that repeated sensing cannot resolve. This paper develops networked embodied communication, where distributed access points (APs) jointly observe message-bearing scatterer positions under fixed illumination.Under a correlated Gaussian sensing model, we characterize the additional distinguishability supplied by receive APs, establish exact redundancy conditions, and reveal how distinctions absent from individual observations can emerge through cross-AP statistical relationships. We then establish the exact asymptotic optimal maximum-error behavior of a finite alphabet under repeated independent sensing. The largest group of indistinguishable messages determines the error floor; once all messages are distinguishable, the minimum pairwise Chernoff information determines the error exponent. For a given alphabet, receiver cooperation can therefore eliminate an error floor that repetition at any individual AP cannot overcome. Building on these results, we derive finite-budget reliability conditions and jointly design the receive AP set and message-bearing positions. Numerical results show that the proposed search closely approaches exact benchmarks on reduced instances with substantially fewer candidate evaluations than exhaustive enumeration, while receiver cooperation reduces the sensing intervals needed to guarantee reliable decoding.