MIMO Integrated Sensing and Communication with Extended Target: CRB-Rate Tradeoff

Hua, Haocheng; Song, Xianxin; Yuan, Fang; Han, Tony Xiao; Xu, Jie

doi:10.1109/globecom48099.2022.10000676

Cited by 20 publications

(17 citation statements)

References 18 publications

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“…From the aforementioned discussion, we may conclude that a key condition for (13) to hold is the memorylessness of channels. Indeed, for channels with memory, even the capacity itself remains open.…”

Section: A Rate-distortion Regionmentioning

confidence: 94%

“…Remember that our expectation on the capacity-distortion theory in the first place was to help understand the nature of the S&C tradeoff, hopefully beyond the ST. However, we cannot even dig ST itself out of (13), not to mention any further insight.…”

Section: B Computing the Capacity-distortion Boundarymentioning

confidence: 99%

“…The fundamental S&C tradeoff in ISAC has been exemplified (implicitly or explicitly) by the "torch metaphor" in some works [5], [13], as illustrated in Fig. 1.…”

Section: The Torch Metaphormentioning

confidence: 99%

“…Remarkably, the result (13) suggests that the expected sensing distortion may be alternatively viewed as a kind of "sensing-induced communication cost". This understanding enables us to extend even further the range that the capacity-distortion theory is applicable, to scenarios in which the sensing performance cannot be described by a proper distortion function.…”

Section: A Rate-distortion Regionmentioning

confidence: 99%

“…where α ∈ [0, 1] is a weighting factor controlling the priority of S&C performance. We highlight here that by moving the CRB from (23a) to (23b), ( 23) may be equivalently recast as a constrained capacity characterization problem with two cost functions as in (13).…”

Section: Crb-rate Tradeoffmentioning

confidence: 99%

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

Xiong

Liu

Cui

et al. 2023

IEEE Trans. Inform. Theory

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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.

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Section: A Rate-distortion Regionmentioning

confidence: 94%

Section: B Computing the Capacity-distortion Boundarymentioning

confidence: 99%

“…The fundamental S&C tradeoff in ISAC has been exemplified (implicitly or explicitly) by the "torch metaphor" in some works [5], [13], as illustrated in Fig. 1.…”

Section: The Torch Metaphormentioning

confidence: 99%

Section: A Rate-distortion Regionmentioning

confidence: 99%

Section: Crb-rate Tradeoffmentioning

confidence: 99%

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