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Deep Learning Enabled Precoding in Secure Integrated Sensing and Communication Systems
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DOI：10.1109/LCOMM.2024.3481032.png)
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Abstract
En 中文
This letter investigates the physical layer security of integrated sensing and communication (ISAC) systems, in which a base station (BS) communicates with users and sense targets simultaneously while a eavesdropper attempts to intercept confidential information. We develop an optimization problem for precoding to minimize the maximum eavesdropping signal-to-interference-plus-noise ratio (SINR) while ensuring quality of service (QoS) requirements of communication and sensing. To find its solution, we propose a learning-based precoding scheme that obtains precoding results from uplink pilots and echoes through a neural network without prior channel information. In particular, a newly developed loss function is designed based upon the first-order optimality conditions to take into account the intricate constraints of the ISAC system. Finally, simulation results indicate that the proposed approach effectively reduce the SINR of eavesdroppers while ensuring a satisfactory QoS for communication and sensing performance.
Keywords:
Precoding
Signal to noise ratio
Interference
Radar
Wireless sensor networks
Wireless communication
Integrated sensing and communication
Vectors
Optimization
Eavesdropping
Integrated sensing and communications (ISAC)
transmit precoding
physical layer security
Journal
IF:
4.4
Papers:
1.3W
Citations:
2.2W
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Cited Papers
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