Information-Theoretic Limits of Bistatic Integrated Sensing and Communication

TL;DR

The paper tackles the capacity-distortion tradeoff in bistatic integrated sensing and communication with a state-dependent memoryless channel, where the sensing receiver lacks knowledge of the transmitted message. It develops a multi-letter representation of the capacity-distortion function $C(D)$ and derives tractable single-letter lower and upper bounds, including an exact single-letter characterization for degraded bistatic ISAC channels. The approach leverages decoding-and-estimation strategies, such as blind, partial-decoding, and full-decoding estimators, and introduces genie-aided bounds and lossy-source considerations to bound $C(D)$, extending to a bistatic ISAC broadcast channel. Numerical examples demonstrate tangible gains of ISAC over separate sensing and communication and highlight the role of communication in facilitating sensing in bistatic configurations. Collectively, the results provide a rigorous framework for designing and analyzing bistatic ISAC systems, with explicit guidance in degraded scenarios and practical insights from the example channels.

Abstract

Bistatic sensing refers to scenarios where the transmitter (illuminating the target) and the sensing receiver (estimating the target state) are physically separated, in contrast to monostatic sensing, where both functions are co-located. In practical settings, bistatic sensing may be required either due to inherent system constraints or as a means to mitigate the strong self-interference encountered in monostatic configurations. A key practical challenge in bistatic radio-frequency radar systems is the synchronization and calibration of the separate transmitter and sensing receiver. In this paper, we are not concerned with these signal processing aspects and take a complementary information-theoretic perspective on bistatic integrated sensing and communication (ISAC). Namely, we aim to characterize the capacity-distortion function-the fundamental tradeoff between communication capacity and sensing accuracy. We consider a general discrete channel model for a bistatic ISAC system and derive a multi-letter representation of its capacity-distortion function. Then, we establish single-letter upper and lower bounds and provide exact single-letter characterizations for degraded bistatic ISAC channels. Furthermore, we extend our analysis to a bistatic ISAC broadcast channel and derive the capacity-distortion region with a single-letter characterization in the degraded case. Numerical examples illustrate the theoretical results, highlighting the benefits of ISAC over separate communication and sensing, as well as the role of leveraging communication to assist sensing in bistatic systems.

Figures (5)

• Figure 1: The bistatic ISAC system model.
• Figure 2: Two-receiver SDMC model.
• Figure 3: Two-receiver SDMBC model.
• Figure 4: The capacity-distortion functions with parameter $q = 0.7$ in Example 1.
• Figure 5: The capacity-distortion functions with parameters $e = 0.25$ and $q = 0.7$ in Example 2.

Theorems & Definitions (35)

• Lemma 1
• proof
• Theorem 1
• proof
• Theorem 2
• proof
• Corollary 1
• proof
• Theorem 3
• proof