Deep Learning-based Design of Uplink Integrated Sensing and Communication
IEEE Transactions on Wireless Communications(2024)
摘要
In this paper, we investigate the issue of uplink integrated sensing and
communication (ISAC) in 6G wireless networks where the sensing echo signal and
the communication signal are received simultaneously at the base station (BS).
To effectively mitigate the mutual interference between sensing and
communication caused by the sharing of spectrum and hardware resources, we
provide a joint sensing transmit waveform and communication receive beamforming
design with the objective of maximizing the weighted sum of normalized sensing
rate and normalized communication rate. It is formulated as a computationally
complicated non-convex optimization problem, which is quite difficult to be
solved by conventional optimization methods. To this end, we first make a
series of equivalent transformation on the optimization problem to reduce the
design complexity, and then develop a deep learning (DL)-based scheme to
enhance the overall performance of ISAC. Both theoretical analysis and
simulation results confirm the effectiveness and robustness of the proposed
DL-based scheme for ISAC in 6G wireless networks.
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关键词
6G,integrated sensing and communication,deep learning,waveform and beamforming design
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