Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Joint Training of the Superimposed Direct and Reflected Links in
  Reconfigurable Intelligent Surface Assisted Multiuser Communications   In Reconfigurable intelligent surface (RIS)-assisted systems the acquisition
of CSI and the optimization of the reflecting coefficients constitute a pair of
salient design issues. In this paper, a novel channel training protocol is
proposed, which is capable of achieving a flexible performance vs. signalling
and pilot overhead as well as implementation complexity trade-off. More
specifically, first of all, we conceive a holistic channel estimation protocol,
which integrates the existing channel estimation techniques and passive
beamforming design. Secondly, we propose a new channel training framework. In
contrast to the conventional channel estimation arrangements, our new framework
divides the training phase into several periods, where the superimposed
end-to-end channel is estimated instead of separately estimating the direct
BS-user channel and cascaded reflected BS-RIS-user channels. As a result, the
reflecting coefficients of the RIS are optimized by comparing the objective
function values over multiple training periods. Moreover, the theoretical
performance of our channel training protocol is analyzed and compared to that
under the optimal reflecting coefficients. In addition, the potential benefits
of our channel training protocol in reducing the complexity, pilot overhead as
well as signalling overhead are also detailed. Thirdly, we derive the
theoretical performance of channel estimation protocols and our channel
training protocol in the presence of noise for a SISO scenario, which provides
useful insights into the impact of the noise on the overall RIS performance.
Finally, our numerical simulations characterize the performance of the proposed
protocols and verify our theoretical analysis. In particular, the simulation
results demonstrate that our channel training protocol is more competitive than
the channel estimation protocol at low signal-to-noise ratios.

27
The categories of this paper are: comp-gas
13
The categories of this paper are: quant-ph
26
The categories of this paper are: bayes-an
16
The categories of this paper are: stat
23
The categories of this paper are: solv-int
33
The categories of this paper are: plasm-ph
8
The categories of this paper are: hep-ex
28
The categories of this paper are: alg-geom
17
The categories of this paper are: q-fin
6
The categories of this paper are: astro-ph
3
The categories of this paper are: physics
20
The categories of this paper are: acc-phys
31
The categories of this paper are: ao-sci
22
The categories of this paper are: chao-dyn
12
The categories of this paper are: nucl-th
21
The categories of this paper are: adap-org