Joint Sensing and Communication over Memoryless Broadcast Channels

Abstract

The paper characterizes the fundamental limits of integrated sensing and communication (ISAC) systems with a bi-static radar, where the radar receiver is located close to the transmitter and estimates or detects the state based on the transmitter's channel inputs and the backscattered signals. Two models are considered. In the first model, the memoryless state sequence is distributed according to a fixed distribution and the goal of the radar receiver is to reconstruct this statesequence with smallest possible distortion. In the second model, the memoryless state is distributed either according to PS or to QS and the radar's goal is to detect this underlying distribution so that the missed-detection error probability has maximum exponential decay-rate (maximum Stein exponent). Similarly to previous results, our fundamental limits show that the tradeoff between sensing and communication solely stems from the empirical statistics of the transmitted codewords which influences both performances. The main technical contribution are two strong converse proofs that hold for all probabilities of communication error and excess-distortion probability or false-alarm probability δ summing to less than 1, +δ < 1. These proofs are based on two parallel change-of-measure arguments on the sets of typical sequences, one change-of-measure to obtain the desired bound on the communication rate, and the second to bound the sensing performance.Index Terms-Integrated sensing and communication, strong converse, change of measure arguments.

An Information-Theoretic Approach to Joint Sensing and Communication

“…zero-mean Gaussian of variance σ 2 fb ≥ 0. We consider the quadratic distortion measure d(s, ŝ) = (s − ŝ) 2 .…”

Coding for Sensing: An Improved Scheme for Integrated Sensing and Communication over MACs

“…In various demanding applications such as smart cities and autonomous driving, terminals have to communicate data to other terminals while at the same time also sense the environment for changes in locations, shapes, and status characteristics of static or locomoting objects. This integrated sensing and communication scenarios have recently received lots of attention from the communications and signal processing communities [1]- [12] and first information-theoretic studies were presented in [13]- [15]. Specifically, [13], [15] identify the optimal tradeoff between the set of achievable data rates and distortions of the state estimates that can be attained over state-dependent point-to-point (P2P) or degraded broadcast channels (BCs) with generalized feedback.…”

“…This integrated sensing and communication scenarios have recently received lots of attention from the communications and signal processing communities [1]- [12] and first information-theoretic studies were presented in [13]- [15]. Specifically, [13], [15] identify the optimal tradeoff between the set of achievable data rates and distortions of the state estimates that can be attained over state-dependent point-to-point (P2P) or degraded broadcast channels (BCs) with generalized feedback. Inner and outer bounds on this tradeoff for general BCs were proposed in [15].…”

“…Specifically, [13], [15] identify the optimal tradeoff between the set of achievable data rates and distortions of the state estimates that can be attained over state-dependent point-to-point (P2P) or degraded broadcast channels (BCs) with generalized feedback. Inner and outer bounds on this tradeoff for general BCs were proposed in [15]. In [13], [15] it was further established that the transmitter's optimal estimators in the P2P and BC setup are symbol-wise estimators applied to the sequences of the transmitter's channel inputs and feedback outputs.…”

“…Inner and outer bounds on this tradeoff for general BCs were proposed in [15]. In [13], [15] it was further established that the transmitter's optimal estimators in the P2P and BC setup are symbol-wise estimators applied to the sequences of the transmitter's channel inputs and feedback outputs. As a consequence, the sensing performance of these systems depends only on the distribution of the input symbols but not on the applied coding schemes.…”