AI Empowered Channel Semantic Acquisition for 6G Integrated Sensing and Communication Networks
IEEE Network(2024)
摘要
Motivated by the need for increased spectral efficiency and the proliferation
of intelligent applications, the sixth-generation (6G) mobile network is
anticipated to integrate the dual-functions of communication and sensing (C S).
Although the millimeter wave (mmWave) communication and mmWave radar share
similar multiple-input multiple-output (MIMO) architecture for integration, the
full potential of dual-function synergy remains to be exploited. In this paper,
we commence by overviewing state-of-the-art schemes from the aspects of
waveform design and signal processing. Nevertheless, these approaches face the
dilemma of mutual compromise between C S performance. To this end, we reveal
and exploit the synergy between C S. In the proposed framework, we introduce a
two-stage frame structure and resort artificial intelligence (AI) to achieve
the synergistic gain by designing a joint C S channel semantic extraction and
reconstruction network (JCASCasterNet). With just a cost-effective and
energy-efficient single sensing antenna, the proposed scheme achieves enhanced
overall performance while requiring only limited pilot and feedback signaling
overhead. In the end, we outline the challenges that lie ahead in the future
development of integrated sensing and communication networks, along with
promising directions for further research.
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