🤖 AI Summary
This paper investigates the fundamental capacity-distortion tradeoff for state-dependent integrated sensing and communication (ISAC) over a multiple-access channel (MAC), where two transmitters simultaneously convey messages to a receiver while jointly estimating a sequence of channel-coupled state parameters via shared echo signals. To address this, we propose a novel achievable scheme featuring message cooperation and joint compression of historical codewords with echo observations. We further develop a new outer bound framework integrating dependence balance, auxiliary state estimators, and distortion constraints. Leveraging discrete memoryless MAC models, joint source-channel coding, state-dependent information theory, and rate-distortion theory, we derive tight inner and outer bounds on the capacity-distortion region—strictly improving upon prior state-of-the-art results. Numerical evaluations confirm substantial performance gains under typical ISAC scenarios.
📝 Abstract
A state-dependent discrete memoryless multiple access channel is considered to model an integrated sensing and communication system, where two transmitters wish to convey messages to a receiver while simultaneously estimating the state parameter sequences through echo signals. In particular, the sensing state parameters are assumed to be correlated with the channel state. In this setup, improved inner and outer bounds for capacity-distortion region are derived. The inner bound is based on an achievable scheme that combines message cooperation and joint compression of past transmitted codewords and echo signals at each transmitter, resulting in unified cooperative communication and sensing. The outer bound is based on the ideas of dependence balance for communication rate, genie-aided state estimator and rate-limited constraints on sensing distortion. The proposed inner and outer bounds are proved to improve the state-of-the-art bounds. Finally, numerical examples are provided to demonstrate that our new inner and outer bounds strictly improve the existing results.