A Survey on Fundamental Limits of Integrated Sensing and Communication

Liu, An; Huang, Zhe; Li, Min; Wan, Yubo; Li, Wenrui; Han, Tony Xiao; Liu, Chenchen; Du, Rui; Pin, Danny Tan Kai; Lu, Jiang; Yuan, Songliu; Colone, Fabiola; Chetty, Kevin

doi:10.48550/arxiv.2104.09954

Cited by 9 publications

(15 citation statements)

References 84 publications

(173 reference statements)

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“…The optimal tradeoff is an optimization problem that maximizes the mutual information over a constrained input distribution P X as in (1). For a practical system, the channel input is usually further subject to additional cost constraint such as power constraint.…”

Section: A Problem Formulationmentioning

confidence: 99%

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Information-Theoretic Limits of Integrated Sensing and Communication with Correlated Sensing and Channel States for Vehicular Networks

Liu¹,

Li²,

Liu³

et al. 2021

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Integrated sensing and communication (ISAC) emerges as a new design paradigm that combines both sensing and communication systems to jointly utilize their resources and to pursue mutual benefits for future B5G and 6G networks. In ISAC, the hardware and spectrum co-sharing leads to a fundamental tradeoff between sensing and communication performance, which is not well understood except for very simple cases with the same sensing and channel states, and perfect channel state information at the receiver (CSIR). In this paper, a more general point-to-point ISAC model is proposed to account for the scenarios that the sensing state is different from but correlated with the channel state, and the CSIR is not necessarily perfect. For the model considered, the optimal tradeoff is characterized by a capacity-distortion function that quantifies the best communication rate for a given sensing distortion constraint requirement. An iterative algorithm is proposed to compute such tradeoff, and a few non-trivial examples are constructed to demonstrate the benefits of ISAC as compared to the separation-based approach.Index Terms-Integrated sensing and communication, correlated sensing and channel states, imperfect channel state information at the receiver, capacity-distortion tradeoff. I. INTRODUCTIONWith the integration of massive MIMO and millimeter-wave communications into mobile networks, communication signals tend to have high resolution in both time and angular domain, which provides an unprecedented opportunity to employ mobile network for high-accuracy sensing. Integrated sensing and communication (ISAC) through co-sharing the same hardware and spectrum for both sensing and communication systems, is envisioned as an important new cost-efficient technology in future B5G and 6G networks [1], [2].Substantial studies have been conducted to investigate the key techniques for ISAC in different scenarios and system architectures, such as the state-of-art in the levels of coexistence, collaboration, and co-design [3], the representative methodologies in vehicular networks [4], the framework for large-scale mobile networks [5], joint waveform design [6], and spatial signal processing [7]. While these studies demonstrate the advantages of joint design of sensing and communication, it is unclear whether these schemes have reached the optimal performance for the resources given. This motivates one to investigate the fundamental performance limits of ISAC and to quantify the optimal tradeoff between sensing accuracy and communication rate for a given ISAC scenario.

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Section: A Problem Formulationmentioning

confidence: 99%

“…x Initialization: µ, the penalty parameter; σ1, σ2, convergence parameter; P (0) 1) , ( 5), and (6).…”

Section: A Problem Formulationmentioning

confidence: 99%

Information-Theoretic Limits of Integrated Sensing and Communication with Correlated Sensing and Channel States for Vehicular Networks

Liu¹,

Li²,

Liu³

et al. 2021

Preprint

Self Cite

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“…As such, the notion of integrated sensing and communication (ISAC) [2], [3], in which sensing and communication systems are co-designed to share the same frequency band and hardware, has been proposed as an enabling technology for beyond fifth-generation (5G) and sixth-generation (6G) wireless systems to further improve the spectral efficiency and to reduce the hardware-cost [4]. It is envisioned that the ISAC can support various essential applications [2], ranging from indoor localization, extended reality, to unmanned aerial vehicle (UAV) sensing and communication [5], [6]. Among various emerging network architectures, vehicle-to-everything (V2X) networks [7] play an important role to unlock the potential of upcoming next-generation wireless communication, which promise a low-latency data transmission in a high user mobility scenario.…”

Section: Introductionmentioning

confidence: 99%

“…Generally, according to whether the sensing targets can independently transmit sensing signals or not, ISAC can be divided into two types, i.e., (a) device-based ISAC and (b) device-free ISAC [2]. For (a), since sensing targets are equipped with some devices to transmit sensing signals, wireless-based localization methods can be adopted for ISAC.…”

Section: Introductionmentioning

confidence: 99%

Learning-based Predictive Beamforming for Integrated Sensing and Communication in Vehicular Networks

Liu¹,

Yuan²,

Li³

et al. 2021

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This paper investigates the integrated sensing and communication (ISAC) in vehicle-to-infrastructure (V2I) networks. To realize ISAC, an effective beamforming design is essential which however, highly depends on the availability of accurate channel tracking requiring large training overhead and computational complexity. Motivated by this, we adopt a deep learning (DL) approach to implicitly learn the features of historical channels and directly predict the beamforming matrix to be adopted for the next time slot to maximize the average achievable sum-rate of an ISAC system. The proposed method can bypass the need of explicit channel tracking process and reduce the signaling overhead significantly. To this end, a general sum-rate maximization problem with Cramer-Rao lower bounds (CRLBs)-based sensing constraints is first formulated for the considered ISAC system. Then, by exploiting the penalty method, a versatile unsupervised DL-based predictive beamforming design framework is developed to address the formulated design problem. As a realization of the developed framework, a historical channelsbased convolutional long short-term memory (LSTM) network (HCL-Net) is devised for predictive beamforming in the ISAC-based V2I network. Specifically, the convolution and LSTM modules are successively adopted in the proposed HCL-Net to exploit the spatial and temporal dependencies of communication channels to further improve the learning performance. Finally, simulation results show that the proposed predictive method not only guarantees the required sensing performance, but also achieves a satisfactory sum-rate that can approach the upper bound obtained by the genie-aided scheme with the perfect instantaneous channel state information.

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“…Integrated sensing and communication (ISAC) has been identified as a promising technology for the sixth-generation (6G) mobile communications [1]- [3], where the joint design of sensing and communication systems becomes necessary to meet the increasing demand for communication and sensing services [4], [5]. High-precision localization, as a critical issue in sensing, shows great potential needs in the emerging 6G application scenarios such as internet of vehicles (IoV), augmented reality (AR), virtual reality (VR), unmanned aerial vehicle (UAV) communications, etc [6]- [8].…”

Section: Introductionmentioning

confidence: 99%

Bayesian User Localization and Tracking for Reconfigurable Intelligent Surface Aided MIMO Systems

Teng,

Yuan,

Wang

et al. 2021

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In this paper, we study the user localization and tracking problem in the reconfigurable intelligent surface (RIS) aided multiple-input multiple-output (MIMO) system, where a multi-antenna base station (BS) and multiple RISs are deployed to assist the localization and tracking of a multi-antenna user. By establishing a probability transition model for user mobility, we develop a message-passing algorithm, termed the Bayesian user localization and tracking (BULT) algorithm, to estimate and track the user position and the angle-of-arrival (AoAs) at the user in an online fashion. We also derive Bayesian Cramér Rao bound (BCRB) to characterize the fundamental performance limit of the considered tracking problem. To improve the tracking performance, we optimize the beamforming design at the BS and the RISs to minimize the derived BCRB. Simulation results show that our BULT algorithm can perform close to the derived BCRB, and significantly outperforms the counterpart algorithms without exploiting the temporal correlation of the user location.

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A Survey on Fundamental Limits of Integrated Sensing and Communication

Cited by 9 publications

References 84 publications

Information-Theoretic Limits of Integrated Sensing and Communication with Correlated Sensing and Channel States for Vehicular Networks

Information-Theoretic Limits of Integrated Sensing and Communication with Correlated Sensing and Channel States for Vehicular Networks

Learning-based Predictive Beamforming for Integrated Sensing and Communication in Vehicular Networks

Bayesian User Localization and Tracking for Reconfigurable Intelligent Surface Aided MIMO Systems

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