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

We consider adversarial machine learning based attacks on power allocation
where the base station (BS) allocates its transmit power to multiple orthogonal
subcarriers by using a deep neural network (DNN) to serve multiple user
equipments (UEs). The DNN that corresponds to a regression model is trained
with channel gains as the input and returns transmit powers as the output.
While the BS allocates the transmit powers to the UEs to maximize rates for all
UEs, there is an adversary that aims to minimize these rates. The adversary may
be an external transmitter that aims to manipulate the inputs to the DNN by
interfering with the pilot signals that are transmitted to measure the channel
gain. Alternatively, the adversary may be a rogue UE that transmits fabricated
channel estimates to the BS. In both cases, the adversary carefully crafts
adversarial perturbations to manipulate the inputs to the DNN of the BS subject
to an upper bound on the strengths of these perturbations. We consider the
attacks targeted on a single UE or all UEs. We compare these attacks with a
benchmark, where the adversary scales down the input to the DNN. We show that
the adversarial attacks are much more effective than the benchmark attack in
terms of reducing the rate of communications. We also show that adversarial
attacks are robust to the uncertainty at the adversary including the erroneous
knowledge of channel gains and the potential errors in exercising the attacks
exactly as specified.

本论文研究基于对手机器学习的对功率分配的攻击，其中基站通过使用深度神经网络（DNN）将其传输功率分配给多个正交子载波来为多个用户设备（UE）提供服务。 训练相应于回归模型的 DNN 以通道增益为输入并返回传输功率作为输出。研究了对单个 UE 或所有 UE 进行的攻击，并与基准进行比较，表明对手攻击在减少通信速率方面比基准攻击更有效。