  Integrated super-resolution sensing and communication (ISSAC) is a promising
technology to achieve extremely high sensing performance for critical
parameters, such as the angles of the wireless channels. In this paper, we
propose an ISSAC-based channel estimation method, which requires little or even
no pilot, yet still achieves accurate channel state information (CSI)
estimation. The key idea is to exploit the fact that subspace-based
super-resolution algorithms such as multiple signal classification (MUSIC) do
not require a priori known pilots for accurate parameter estimation. Therefore,
in the proposed method, the angles of the multi-path channel components are
first estimated in a pilot-free manner while communication data symbols are
sent. After that, the multi-path channel coefficients are estimated, where very
little pilots are needed. The reasons are two folds. First, compared to the
conventional channel estimation methods purely relying on channel training,
much fewer parameters need to be estimated once the multi-path angles are
accurately estimated. Besides, with angles obtained, the beamforming gain is
also enjoyed when pilots are sent to estimate the channel path gains. To
rigorously study the performance of the proposed method, we first consider the
basic line-of-sight (LoS) channel. By analyzing the minimum mean square error
(MMSE) of channel estimation and the resulting beamforming gains, we show that
our proposed method significantly outperforms the conventional methods purely
based on channel training. We then extend the study to the more general
multipath channels. Simulation results are provided to demonstrate our
theoretical results.
