On the Fundamental Tradeoff of Integrated Sensing and Communications Under Gaussian Channels

Abstract

Sensing and communications (S&C) have been historically developed in parallel. In recent decade, they have been evolving from separation to integration, giving rise to the integrated sensing and communications (ISAC) paradigm, that has been recognized as one of the six key 6G usage scenarios. Despite the plethora of research works dedicated to ISAC signal processing, the fundamental performance limits of S&C remain widely unexplored in an ISAC system. In this tutorial paper, we attempt to summarize the recent research findings in characterizing the performance boundary of ISAC systems and the resulting S&C tradeoff from an information-theoretical viewpoint. We begin with a folklore "torch metaphor" that depicts the resource competition mechanism of S&C. Then, we elaborate on the fundamental capacity-distortion (C-D) theory, indicating the incompleteness of this metaphor. Towards that end, we further elaborate on the S&C tradeoff by discussing a special case within the C-D framework, namely the Cramér-Rao bound (CRB)-rate region. In particular, S&C have preference discrepancies over both the subspace occupied by the transmitted signal and the adopted codebook, leading to a "projector metaphor" complementary to the ISAC torch analogy. We also present two practical design examples by leveraging the lessons learned from fundamental theories. Finally, we conclude the paper by identifying a number of open challenges.

A deep model‐based channel interference mitigation for OTFS signals in ISAC systems

“…Rectangular filters are a practical but imperfect alternative, introducing interference in high‐speed mobile scenarios. OTFS symbol blocks correspond to multiple multi‐carrier symbols, leading to a large channel matrix [22]. Traditional linear interference cancellation methods have high complexity.…”

Integrated Sensing and Communications: Recent Advances and Ten Open Challenges

“…First of all, the fundamental theories of radar sensing and wireless communication are challenging to be unified under the joint concept of ISAC systems, since radar and wireless communication systems glean different information from the received signals [66], [76]- [79]. Specifically, radar systems focus on how to minimize the uncertainty concerning the target environments based on the received echo signals.…”

“…• MMSE and MI for radar sensing [74], [75] • Wireless localization [76] • I-MMSE equation [77] • Distortion-capacity [78], [79] • CRB-rate region [66], [80], [81] • MI-based ISAC framework [82]- [85] • Joint communication and binary state detection [86] • The first principle in ISAC information theory • Proper strategy and coding approach • KLD measure vs. mutual information tradeoff • Ergodic CRB and PCRB ♣ How much channel information can be inferred from the sensory data?…”

Integrated Sensing and Communication via Orthogonal Time Frequency Space Signaling with Hybrid Message Passing Detection and Fractional Parameter Estimation

“…Upon examining the peaks within the DD plane denoted as , we are limited to discerning only the integer components of the DD indices [ 63 , 64 , 65 , 66 ]. This constraint implies that the fractional segments of these respective indices remain undisclosed, thereby diminishing the precision of the sensing outcomes, as elaborated upon in references [ 67 , 68 , 69 , 70 ]. To rectify these concerns, this subsection will present an uncomplicated technique for estimating the fractional parameters.…”