Multi-Antenna Joint Radar and Communications: Precoder Optimization and Weighted Sum-Rate vs Probing Power Tradeoff

Xu, Chengcheng; Clerckx, Bruno; Zhang, Jianyun

doi:10.1109/access.2020.3025156

Cited by 23 publications

(16 citation statements)

References 24 publications

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“…In each iteration, the optimal v ⋆ is obtained in closed-form and the locally optimal x ⋆ is provided by the Hooke-Jeeves Pattern Search algorithm. Moreover, since the objective value is lower-bounded by 2 P tot /M µ , we can conclude that the solution to problem (19) in the inner loop converges. Therefore, the convergence of the PDD-based algorithm shown in Algorithm 1 can be guaranteed as analyzed in [35].…”

mentioning

confidence: 77%

“…Typical radar sensing metrics include the radar receiver's signal-to-interferenceplus-noise ratio (SINR) [11], the beampattern mean squared error (MSE) [12], the Cramér-Rao bound [13], and the similarity between the designed beamformer and that of the reference radar-only system [14]- [17]. Meanwhile, widelyused communication metrics include the achievable rate [18], [19], the communication user's SINR [14], [16], [20], and the multi-user interference (MUI) [12], [15]. The combination of radar sensing and communication metrics can provide a comprehensive criteria for evaluating the performance of DFRC systems [12]- [15].…”

Section: Introductionmentioning

confidence: 99%

“…Convergence Analysis: In the inner loop for solving the augmented Lagrangian problem (19), the objective value is non-increasing since each iteration of the MM method generates non-increasing sequences [36]. In each iteration, the optimal v ⋆ is obtained in closed-form and the locally optimal x ⋆ is provided by the Hooke-Jeeves Pattern Search algorithm.…”

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confidence: 99%

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Dual-Functional Radar-Communication Waveform Design: A Symbol-Level Precoding Approach

Liu,

Li,

Liu

et al. 2021

Preprint

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Dual-functional radar-communication (DFRC) systems can simultaneously perform both radar and communication functionalities using the same hardware platform and spectrum resource. In this paper, we consider multi-input multioutput (MIMO) DFRC systems and focus on transmit beamforming designs to provide both radar sensing and multiuser communications. Unlike conventional block-level precoding techniques, we propose to use the recently emerged symbollevel precoding approach in DFRC systems, which provides additional degrees of freedom (DoFs) that guarantee preferable instantaneous transmit beampatterns for radar sensing and achieve better communication performance. In particular, the squared error between the designed and desired beampatterns is minimized subject to the quality-of-service (QoS) requirements of the communication users and the constant-modulus power constraint. Two efficient algorithms are developed to solve this non-convex problem on both the Euclidean and Riemannian spaces. The first algorithm employs penalty dual decomposition (PDD), majorization-minimization (MM), and block coordinate descent (BCD) methods to convert the original optimization problem into two solvable sub-problems, and iteratively solves them using efficient algorithms. The second algorithm provides a much faster solution at the price of a slight performance loss, first transforming the original problem into Riemannian space, and then utilizing the augmented Lagrangian method (ALM) to obtain an unconstrained problem that is subsequently solved via a Riemannian Broyden-Fletcher-Goldfarb-Shanno (RBFGS) algorithm. Extensive simulations verify the distinct advantages of the proposed symbol-level precoding designs in both radar sensing and multi-user communications.

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confidence: 77%

Section: Introductionmentioning

confidence: 99%

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Dual-Functional Radar-Communication Waveform Design: A Symbol-Level Precoding Approach

Liu,

Li,

Liu

et al. 2021

Preprint

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“…Given the limited wireless resources and the shared use of the hardware platform, existing ISAC solutions seek to achieve a favorable performance tradeoff between both functionalities, where spatial multiplexing [2], [3] and time division [4], [5] are two typically employed approaches. Specifically, spatial multiplexing adopts the multiple-input multiple-output (MIMO) technology with sophisticatedly designed waveform and beamforming schemes so as to support dual-functional transmission with various modulation schemes.…”

Section: A Related Work On Integrated Sensing and Communicationmentioning

confidence: 99%

Integrated Sensing and Communication from Learning Perspective: An SDP3 Approach

Li¹,

Wang²,

Li³

et al. 2021

Preprint

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Integrated sensing and communication (ISAC) is a promising technology to improve the bandutilization efficiency via spectrum sharing or hardware sharing between radar and communication systems. Since a common radio resource budget is shared by both functionalities, there exists a tradeoff between the sensing and communication performance. However, this tradeoff curve is currently unknown in ISAC systems with human motion recognition tasks based on deep learning. To fill this gap, this paper formulates and solves a multi-objective optimization problem which simultaneously maximizes the recognition accuracy and the communication data rate. The key ingredient of this new formulation is a nonlinear recognition accuracy model with respect to the wireless resources, where the model is derived from power function regression of the system performance of the deep spectrogram network. To avoid cost-expensive data collection procedures, a primitive-based autoregressive hybrid (PBAH) channel model is developed, which facilitates efficient training and testing dataset generation for human motion recognition in a virtual environment. Extensive results demonstrate that the proposed wireless recognition accuracy and PBAH channel models match the actual experimental data very well. Moreover, it is found This work was supported by the SUSTech Huawei Cooperation Project under Grant OR1912016.

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“…Specifically, the precoders are designed using the Zero-Forcing (ZF) beamforming technique to form a desired radar beampattern and meet the SINR requirements for communication users. Based on the transmission structure of [12], the authors in [13] further investigate the tradeoff between Weighted Sum-Rate (WSR) and probing power at target for the multi-antenna DFRC, and show that a shared deployment with an integrated DFRC transmission outperforms the separated deployment (which is a special case of radar-communication coexistence). To the best of our knowledge, all existing works in the realm of multi-antenna DFRC consider the use of SDMA.…”

Section: A Background and Literature Reviewmentioning

confidence: 99%

Rate-Splitting Multiple Access for Multi-Antenna Joint Communication and Radar Transmissions

Clerckx

Chen

et al. 2020

2020 IEEE International Conference on Communications Workshops (ICC Workshops)

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Dual-Functional Radar-Communication (DFRC) system is an essential and promising technique to achieve Integrated Sensing and Communication (ISAC) for beyond 5G. In this work, we propose a powerful and unified multi-antenna DFRC transmission framework, where an additional radar sequence is transmitted apart from communication streams to enhance radar beampattern matching capability, and Rate-Splitting Multiple Access (RSMA) is adopted to better manage the interference. RSMA relies on multi-antenna Rate-Splitting (RS) with Successive Interference Cancellation (SIC) receivers, and the split and encoding of messages into common and private streams. We design the message split and the precoders of the radar sequence, common and private streams so as to jointly maximize the Weighted Sum Rate (WSR) and minimize the radar beampattern approximation Mean Square Error (MSE) subject to the per antenna power constraint. An iterative algorithm based on Alternating Direction Method of Multipliers (ADMM) is developed to solve the problem. Numerical results first show that RSMAassisted DFRC achieves a better tradeoff between WSR and beampattern approximation than Space-Division Multiple Access (SDMA)-assisted DFRC with or without radar sequence, and other simpler radar-communication strategies using orthogonal resources. We also show that the RSMA-assisted DFRC frameworks with and without radar sequence achieve the same tradeoff performance. This is because that the common stream is better exploited in the proposed framework. The common stream of RSMA fulfils the triple function of managing interference among communication users, managing interference between communication and radar, and beampattern approximation. Uniquely, the SIC receiver of RSMA is exploited for the dual purpose of managing interference among communication users as well as interference between communication and radar. Therefore, by enabling RSMA in DFRC, the system performance is enhanced while the system architecture is simplified since there is no need to use additional radar sequence and SIC. We conclude that RSMA is a more powerful multiple access for DFRC.

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Multi-Antenna Joint Radar and Communications: Precoder Optimization and Weighted Sum-Rate vs Probing Power Tradeoff

Cited by 23 publications

References 24 publications

Dual-Functional Radar-Communication Waveform Design: A Symbol-Level Precoding Approach

Dual-Functional Radar-Communication Waveform Design: A Symbol-Level Precoding Approach

Integrated Sensing and Communication from Learning Perspective: An SDP3 Approach

Rate-Splitting Multiple Access for Multi-Antenna Joint Communication and Radar Transmissions

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