Joint Identification and Sensing with Noisy Feedback: A Task-Oriented Communication Framework for 6G

2026-03-31

AI Summary

This work addresses the challenge of joint message identification and channel state sensing over noisy feedback channels in task-oriented 6G communications. The paper proposes a Joint Identification and Sensing (JIDAS) framework, wherein the transmitter simultaneously conveys an identification message and exploits strictly causal, noisy feedback to estimate the time-varying channel state over a state-dependent discrete memoryless channel. By integrating deterministic and stochastic encoding strategies, the authors establish, for the first time, tight upper and lower bounds on the capacity–distortion function of JIDAS under noisy feedback. These bounds not only characterize the fundamental trade-off between identification reliability and sensing accuracy but also unify and generalize existing results derived for the idealized noiseless feedback setting.

Abstract

Task-oriented communication is a key enabler of emerging 6G systems, where the objective is to support decisions and actions rather than full message reconstruction. From an information-theoretic perspective, identification (ID) codes provide a natural abstraction for this paradigm by enabling receivers to test whether a task-relevant message was sent, without decoding the entire message. Motivated by the strong impact of feedback on ID and by the growing interest in integrated communication and sensing, this paper studies joint identification and sensing (JIDAS) over state-dependent discrete memoryless channels with noisy strictly causal feedback. The transmitter conveys identification messages while simultaneously estimating the channel state from the feedback signal. For both deterministic and randomized coding schemes, we derive lower and upper bounds on the capacity--distortion function. The results quantify the fundamental limits of JIDAS under noisy feedback and recover existing noiseless-feedback characterizations as special cases.

Problem

• task-oriented communication
• identification
• sensing
• noisy feedback
• 6G

Innovation

• task-oriented communication
• identification codes
• joint identification and sensing
• noisy feedback
• capacity-distortion function