Two-Dimensional Direction-of-Arrival Estimation Using Stacked Intelligent Metasurfaces
CoRR(2024)
摘要
Stacked intelligent metasurfaces (SIM) are capable of emulating
reconfigurable physical neural networks by relying on electromagnetic (EM)
waves as carriers. They can also perform various complex computational and
signal processing tasks. A SIM is fabricated by densely integrating multiple
metasurface layers, each consisting of a large number of small meta-atoms that
can control the EM waves passing through it. In this paper, we harness a SIM
for two-dimensional (2D) direction-of-arrival (DOA) estimation. In contrast to
the conventional designs, an advanced SIM in front of the receiver array
automatically carries out the 2D discrete Fourier transform (DFT) as the
incident waves propagate through it. As a result, the receiver array directly
observes the angular spectrum of the incoming signal. In this context, the DOA
estimates can be readily obtained by using probes to detect the energy
distribution on the receiver array. This avoids the need for power-thirsty
radio frequency (RF) chains. To enable SIM to perform the 2D DFT, we formulate
the optimization problem of minimizing the fitting error between the SIM's EM
response and the 2D DFT matrix. Furthermore, a gradient descent algorithm is
customized for iteratively updating the phase shift of each meta-atom in SIM.
To further improve the DOA estimation accuracy, we configure the phase shift
pattern in the zeroth layer of the SIM to generate a set of 2D DFT matrices
associated with orthogonal spatial frequency bins. Additionally, we
analytically evaluate the performance of the proposed SIM-based DOA estimator
by deriving a tight upper bound for the mean square error (MSE). Our numerical
simulations verify the capability of a well-trained SIM to perform DOA
estimation and corroborate our theoretical analysis. It is demonstrated that a
SIM having an optical computational speed achieves an MSE of 10^-4 for DOA
estimation.
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