Fundamental CRB-Rate Tradeoff in Multi-antenna Multicast Channel with ISAC

Ren, Zixiang; Song, Xianxin; Yuan, Fang; Qiu, Ling; Xu, Jie

doi:10.1109/gcwkshps56602.2022.10008661

Cited by 14 publications

(10 citation statements)

References 19 publications

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“…The CRB matrix is the inverse matrix of the FIM [30]. The element in the q-th row and q-th column of F is [31] […”

Section: System Model and Problem Formulationmentioning

confidence: 99%

Bi-Level Deep Unfolding Based Robust Beamforming Design for IRS-Assisted ISAC System

Liu,

Xu,

et al. 2024

IEEE Access

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In this paper, an intelligent reflecting surface (IRS)-assisted integrated sensing and communication (ISAC) system is considered, where the ISAC base station (BS) serves the communication user equipments (UEs) and tracks sensing targets simultaneously. Unfortunately, it is challenging to obtain the perfect channel state information (CSI) of user equipments (UEs)-related links and target directions in practice. To address the imperfection, we investigate the robust beamforming design under the statistical CSI and bounded target direction uncertainty models. With these considerations, we formulate a Cramér-Rao bound (CRB) of sensing targets minimization problem, subject to the signal-to-interference-plus-noise ratio (SINR) outage probability constraint, the maximum transmit power limits and the unit-modulus constraint of each IRS element. In order to solve this non-convex problem, we formulate the robust beamforming design as a bi-level optimization (BLO) problem and then apply the double-loop deep unfolding (DU) approach to solve this problem. Specifically, we formulate the problem into a primal-dual problem by integrating the SINR outage probability constraint into the objective function which is further approximated by using the worst-case SINR. The projection method and Riemannian manifold optimization method are used to deal with the maximum transmit power constraint and the unit-modulus constraint, respectively. Therefore, the problem can be reformulated as the bi-level optimization (BLO) problem. The lower level (LL) problems aim to find the worst-case CSI and angle of sensing targets. The upper level (UL) problem is the primaldual problem solved by beamforming design and the Lagrange multipliers optimization with the certain CSI and angle. Then, we apply double-loop DU neural network which unfolds the iterative gradient descent into multi-layer structure, and each layer consists of UL loop and LL loop. In addition, the trainable step sizes and offset parameters are introduced as the network parameters, and the network ultimately outputs optimal optimization variables. Simulation results demonstrate the effectiveness of the proposed robust beamforming design algorithm.INDEX TERMS Integrated sensing and communication system, intelligent reflecting surface, robust beamforming, bi-level optimization, deep unfolding, worst-case CRB, SINR outage probability.

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“…The CRB matrix is the inverse matrix of the FIM [30]. The element in the q-th row and q-th column of F is [31] […”

Section: System Model and Problem Formulationmentioning

confidence: 99%

Bi-Level Deep Unfolding Based Robust Beamforming Design for IRS-Assisted ISAC System

Liu,

Xu,

et al. 2024

IEEE Access

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“…Simultaneously supporting multi-user communication and multi-target sensing in practical scenarios poses a critical challenge for MIMO-ISAC systems [14], [20]. Such scenarios are inherently complex due to the diverse performance requirements for both communication and sensing.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization of Land Use in the Beijing–Tianjin–Hebei Region of China Based on the GMOP-PLUS Coupling Model

2023

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The changeable patterns and contractions of land use have become increasingly significant in recent years as the economy and society have rapidly developed. Subsequently, land use change simulation has become a focal point in the study of land use patterns and change processes. Four development scenarios in 2030, including business-as-usual, ecological protection, economic development, and sustainable development scenarios, are proposed to realize the sustainable development of land use in Beijing–Tianjin–Hebei in the context of a low-carbon economy and ecological security. Then, a feasible multi-objective land use optimization scheme suitable for the region’s long-term development was identified through comparative analysis. The GMOP-PLUS model analyzed changes in ecological and economic benefits and carbon emissions by optimizing the quantitative structure and spatial layout of land use in different scenarios. The cultivated land area in the four scenarios decreased, while the construction land area increased for all scenarios other than the ecological protection and sustainable development scenarios. Moreover, the target development of the sustainable development scenario was the most balanced, with carbon emissions and economic benefits reduced by 49.77 million tons and CNY 0.73 billion compared with the business-as-usual scenario, respectively. Meanwhile, the ecological benefits increased by CNY 0.03 billion, and the economic benefits increased by 1.54 times compared with those in 2020. Therefore, the sustainable development scenario was more in line with the needs of Beijing, Tianjin, and Hebei for high-quality economic and ecological development, aiming towards a low-carbon goal. This work provides a theoretical basis for Beijing–Tianjin–Hebei territorial spatial planning and more perspectives for the study of sustainable land use through the obtained results.

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“…Secondly, mutual assistance between S&C may offer coordination gain and further boost the dual performance. Hence, ISAC is expected to benefit both communication and sensing functionality in the near future [7]- [10]. Due to its numerous advantages, ISAC is envisioned to be a key enabler for many future applications including intelligent connected vehicles, Internet of Things (IoT), and smart homes and cities [11]- [14].…”

Section: Introductionmentioning

confidence: 99%

“…While ISAC has achieved notable success in localization [3], tracking [10], imaging [15], and various other applications [7], [9], [11], [16], the realm of EM property sensing within ISAC remains unexplored to the best knowledge of the authors. Since the EM property of the target material is implicitly encoded into the channel state information (CSI) from the transmitter to the receiver, there are promising prospects to incorporate EM property sensing capabilities into the ISAC paradigm.…”

Section: Introductionmentioning

confidence: 99%

“…These parameters are crucial as they provide the spatial context needed to accurately engage with the target. The methods to acquire these parameters have been comprehensively discussed in conventional ISAC literature [5], [7], [9]- [13], [16]. Thirdly, we proceed to sense the EM property of the target, acquiring the material-specific information.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improve Sensing and Communication Performance of UAV via Integrated Sensing and Communication

Jiang

Wang

Wei

et al. 2021

2021 IEEE 21st International Conference on Communication Technology (ICCT)

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The unmanned aerial vehicle (UAV) needs to sense the environment to ensure safe flight, and the sensing accuracy and communication delay performance are two important indicators of safe flight. The strategy of using integrated sensing and communication (ISAC) technology to improve the sensing and communication performance is proposed in this paper. On the one hand, the extended kalman filter (EKF) algorithm is adopted to achieve the fusion of communication location information and sensing information to improve the accuracy of target sensing. On the other hand, a Identification Friend or Foe (IFF) method based on ISAC is proposed to reduce communication delay. Compared with the traditional IFF method, the integrated technology used for IFF can realize the radar sensing and communication interrogating functions in parallel, greatly shortening the sensing time. Simulation results show that using ISAC technology, the sensing performance of UAV has been greatly improved, the communication delay can be reduced by up to 50%, the accuracy of target sensing can be improved by 24.2% when communication location information and radar sensing information have the same sensing accuracy.

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Fundamental CRB-Rate Tradeoff in Multi-antenna Multicast Channel with ISAC

Cited by 14 publications

References 19 publications

Bi-Level Deep Unfolding Based Robust Beamforming Design for IRS-Assisted ISAC System

Bi-Level Deep Unfolding Based Robust Beamforming Design for IRS-Assisted ISAC System

Multi-Objective Optimization of Land Use in the Beijing–Tianjin–Hebei Region of China Based on the GMOP-PLUS Coupling Model

Improve Sensing and Communication Performance of UAV via Integrated Sensing and Communication

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