Joint State Sensing and Communication: Optimal Tradeoff for a Memoryless Case

TL;DR

This paper characterizes the optimal tradeoff between data transmission rate and state sensing accuracy in a memoryless channel, proposing an algorithm to optimize input distribution and demonstrating advantages over separate approaches.

Contribution

It provides a capacity-distortion tradeoff characterization for joint sensing and communication in memoryless channels with an iterative optimization algorithm.

Findings

Joint sensing and communication outperform separation-based methods.

The capacity-distortion tradeoff is explicitly characterized.

An iterative algorithm effectively optimizes input distribution.

Abstract

A communication setup is considered where a transmitter wishes to simultaneously sense its channel state and convey a message to a receiver. The state is estimated at the transmitter by means of generalized feedback, i.e. a strictly causal channel output that is observed at the transmitter. The scenario is motivated by a joint radar and communication system where the radar and data applications share the same frequency band. For the case of a memoryless channel with i.i.d. state sequences, we characterize the capacity-distortion tradeoff, defined as the best achievable rate below which a message can be conveyed reliably while satisfying some distortion constraint on state sensing. An iterative algorithm is proposed to optimize the input probability distribution. Examples demonstrate the benefits of joint sensing and communication as compared to a separation-based approach.