QoS-Aware Precoder Optimization for Radar Sensing and Multiuser Communications Under Per-Antenna Power Constraints

Wang, Chao; Li, Zan; Al-Dhahir, Naofal; Kim, Kyeong Jin; Wong, Kai‐Kit

doi:10.1109/tsp.2023.3279580

Cited by 9 publications

(1 citation statement)

References 61 publications

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“…To design an efficient algorithm to handle problem (19), we propose a distance-majorization induced iterative algorithm. In particular, we employ the Courant's penalty method [60] to incorporate constraints (19b)-(19d) into the objective function, and then, a distance-majorization induced low-complexity algorithm is proposed to handle the problem with the modified objective function [61], [62]. To facilitate the algorithm design, we first convert the complex-valued formulation of problem (19) into its real-valued counterpart by exploiting the following identity…”

Section: A Distance-majorization Algorithm For Optimizing the Signal ...mentioning

confidence: 99%

STAR-RIS-Enabled Secure Dual-Functional Radar-Communications: Joint Waveform and Reflective Beamforming Optimization

Wang

et al. 2023

IEEE Trans.Inform.Forensic Secur.

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Considering a simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS)-aided dual-functional radar-communications (DFRC) system, this paper proposes a symbol-level precoding-based scheme for concurrent securing confidential information transmission and performing target sensing, where the public signals intended for multiple unclassified users are exploited to deceive the multiple potential malicious radar targets. Specifically, the STAR-RIS-aided DFRC system design is formulated as a joint optimization problem that determines the transmission waveform signal, the transmission and reflection coefficients of STAR-RIS. The objective is to maximize the average received radar sensing power subject to the quality-of-service constraints for multiple communication users, the security constraint for multiple potential eavesdroppers, as well as various practical waveform design restrictions. However, the formulated problem is challenging to handle due to its

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Section: A Distance-majorization Algorithm For Optimizing the Signal ...mentioning

confidence: 99%

STAR-RIS-Enabled Secure Dual-Functional Radar-Communications: Joint Waveform and Reflective Beamforming Optimization

Wang

et al. 2023

IEEE Trans.Inform.Forensic Secur.

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QACDes: QoS-aware context-sensitive design of cyber-physical systems

Sidhanta,

Chokwitthaya,

Zhu

et al. 2024

Sci Rep

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There is a lot of confusion and ambiguity regarding the quantification of the Quality of Service (QoS) of a system, especially for cyber-physical systems (CPS) involved in automating or controlling the operations in built environments and critical urban infrastructures, such as office buildings, factories, transportation systems, smart cities, etc. In these cases, the QoS, as experienced by human users, depends on the context in which they (i.e., humans) interact with these systems. Traditionally, the QoS of a CPS has been defined in terms of absolute metrics. Such measures are unable to take into account the variations in performance due to contextual factors arising out of different kinds of human interactions. Further, the QoS of a CPS has typically been evaluated by comparing the performance of the actual, fully realized system with the given QoS constraints only after the actual system has been completely developed. In the case of faults in the design exposed by observed deviations from the QoS constraints due to unpredicted variations in the contextual factors, the system needs to be re-designed and re-developed from scratch. Due to the above-mentioned reason, the validation approach associated with the traditional QoS makes the design of CPS systems prohibitively expensive, impractical, as well as infeasible in numerous application areas, such as civil and engineering works, since it may not be possible to modify the system once developed beyond a certain extent. To that end, we propose a context-aware definition of QoS of a CPS which facilitates the design of robust systems as elaborated below. In this paper, we define QoS as a function of contextual factors. A CPS designed according to our QoS specifications would always satisfy the QoS irrespective of any possible changes in contextual factors resulting from many different human interactions that may occur during operation of the system. We also present QACDes - a novel framework that provides a formal mechanism for validating the design of a CPS with respect to the specified QoS constraints at the design phase as well as after the realization of the actual system. QACDes can validate any given CPS, irrespective of its application domain, against a QoS guarantee: (A) as early as even before the design phase by comparing the proposed model with a baseline model, or (B) after the realization of the actual system based on logs collected from running the actual system. We consider a lighting control system that manages the light switches - switching it on/off depending on contextual factors, such as the presence of occupants and time of the day. Using the lighting control system in a building as a use case, we analyze and demonstrate the effectiveness of our QoS definition as well as the QACDes framework against the performance metric measured in an actual fully-realized CPS.

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SigMixer: Lightweight Automatic Modulation Classification via Multi -Layer Perceptrons Neural Network

Wang,

Zhang

et al. 2023

GLOBECOM 2023 - 2023 IEEE Global Communications Conference

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QoS-Aware Precoder Optimization for Radar Sensing and Multiuser Communications Under Per-Antenna Power Constraints

Cited by 9 publications

References 61 publications

STAR-RIS-Enabled Secure Dual-Functional Radar-Communications: Joint Waveform and Reflective Beamforming Optimization

STAR-RIS-Enabled Secure Dual-Functional Radar-Communications: Joint Waveform and Reflective Beamforming Optimization

QACDes: QoS-aware context-sensitive design of cyber-physical systems

SigMixer: Lightweight Automatic Modulation Classification via Multi -Layer Perceptrons Neural Network

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