Holographic Metasurface Transceivers (HMTs) are emerging as cost-effective
substitutes to large antenna arrays for beamforming in Millimeter and TeraHertz
wave communication. However, to achieve desired channel gains through
beamforming in HMT, phase-shifts of a large number of elements need to be
appropriately set, which is challenging. Also, these optimal phase-shifts
depend on the location of the receivers, which could be unknown. In this work,
we develop a learning algorithm using a {\it fixed-budget multi-armed bandit
framework} to beamform and maximize received signal strength at the receiver
for far-field regions. Our algorithm, named \Algo exploits the parametric form
of channel gains of the beams, which can be expressed in terms of two {\it
phase-shifting parameters}. Even after parameterization, the problem is still
challenging as phase-shifting parameters take continuous values. To overcome
this, {\it\HB} works with the discrete values of phase-shifting parameters and
exploits their unimodal relations with channel gains to learn the optimal
values faster. We upper bound the probability of {\it\HB} incorrectly
identifying the (discrete) optimal phase-shift parameters in terms of the
number of pilots used in learning. We show that this probability decays
exponentially with the number of pilot signals. We demonstrate that {\it\HB}
outperforms state-of-the-art algorithms through extensive simulations.

使用固定预算多臂赌博框架开发了一种学习算法，通过初始值探索指定波束 forming 的相移参数及最大程度地增强接收器的接收信号强度，该算法在广泛的模拟中表现出优于现有算法的性能。

HoloBeam：远场全息介质表面收发器中的最佳波束形成学习

HoloBeam: Learning Optimal Beamforming in Far-Field Holographic  Metasurface Transceivers

Holographic metasurface transceivers (HMT) is an emerging technology for
enhancing the coverage and rate of wireless communication systems. However,
acquiring accurate channel state information in HMT-assisted wireless
communication systems is critical for achieving these goals. In this paper, we
propose an algorithm for learning the optimal phase-shifts at a HMT for the
far-field channel model. Our proposed algorithm exploits the structure of the
channel gains in the far-field regions and learns the optimal phase-shifts in
presence of noise in the received signals. We prove that the probability that
the optimal phase-shifts estimated by our proposed algorithm deviate from the
true values decays exponentially in the number of pilot signals. Extensive
numerical simulations validate the theoretical guarantees and also demonstrate
significant gains as compared to the state-of-the-art policies.

该论文提出了一种在远距离通道模型中学习 HMT 最佳相位移位的算法，通过利用远场区域的信道增益结构，在接收信号噪声的情况下学习最佳相位移位。通过大量的数值仿真，验证了理论保证，并证明了与最新策略相比的显着收益。