Integrated Sensing and Communication with Massive MIMO: A Unified Tensor Approach for Channel and Target Parameter Estimation
IEEE Transactions on Wireless Communications(2024)
摘要
Benefitting from the vast spatial degrees of freedom, the amalgamation of
integrated sensing and communication (ISAC) and massive multiple-input
multiple-output (MIMO) is expected to simultaneously improve spectral and
energy efficiencies as well as the sensing capability. However, a large number
of antennas deployed in massive MIMO-ISAC raises critical challenges in
acquiring both accurate channel state information and target parameter
information. To overcome these two challenges with a unified framework, we
first analyze their underlying system models and then propose a novel
tensor-based approach that addresses both the channel estimation and target
sensing problems. Specifically, by parameterizing the high-dimensional
communication channel exploiting a small number of physical parameters, we
associate the channel state information with the sensing parameters of targets
in terms of angular, delay, and Doppler dimensions. Then, we propose a shared
training pattern adopting the same time-frequency resources such that both the
channel estimation and target parameter estimation can be formulated as a
canonical polyadic decomposition problem with a similar mathematical
expression. On this basis, we first investigate the uniqueness condition of the
tensor factorization and the maximum number of resolvable targets by utilizing
the specific Vandermonde
更多查看译文
关键词
Integrated sensing and communication,massive MIMO,channel estimation,target parameter estimation,tensor decomposition
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要