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Energy Efficiency Optimization for Robust Covert ISAC Systems
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DOI：10.1109/JSAC.2025.3610566.png)
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Abstract
En 中文
Energy efficiency is of paramount importance for covert integrated sensing and communication (ISAC) networks to ensure sustained operation. In light of the imperfect channel state information (CSI) encountered in practical scenarios, we investigate the energy efficiency of these networks. Taking into account a variety of CSI estimation errors, our algorithm optimizes both sensing and information beamforming design while ensuring a low detection probability by multiple untrusted wardens. The energy-efficient beamforming design is formulated as a non-convex fractional programming problem. First, we establish that the covariance matrices of communication beamforming vectors are rank-one. Subsequently, we exploit this property to transform the original problem into a semi-definite relaxed version. For Gaussian CSI estimation errors, we adopt Bernstein-type inequalities to handle the probability constraints of interception and exploit Dinkelbach’s algorithm to address the nonlinear fractional objective function. For bounded CSI estimation errors, we employ an S-procedure to tackle the non-convex constraints associated with covert communications, followed by a successive convex optimization algorithm to provide an effective solution to the original problem. Extensive simulations confirm the superiority of our proposed algorithms, demonstrating a remarkable performance gain compared with baseline schemes adopting existing approaches. Specifically, deploying a larger number of antenna elements can enhance the energy efficiency of covert ISAC networks, while simultaneously reducing the system’s total power consumption. Furthermore, the sensing beam power threshold and the outage probability of covertness serve as important trade-off parameters in covert ISAC networks.
Keywords:
Covert communications
energy efficiency
imperfect channel state information (CSI)
integrated sensing and communications (ISAC)
fractional programming
Journal
IF:
17.2
Papers:
6.4K
Citations:
3.1W
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