Coherent measurement of quantum signals used for continuous-variable (CV)
quantum key distribution (QKD) across satellite-to-ground channels requires
compensation of phase wavefront distortions caused by atmospheric turbulence.
One compensation technique involves multiplexing classical reference pulses
(RPs) and the quantum signal, with direct phase measurements on the RPs then
used to modulate a real local oscillator (RLO) on the ground - a solution that
also removes some known attacks on CV-QKD. However, this is a cumbersome task
in practice - requiring substantial complexity in equipment requirements and
deployment. As an alternative to this traditional practice, here we introduce a
new method for estimating phase corrections for an RLO by using only intensity
measurements from RPs as input to a convolutional neural network, mitigating
completely the necessity to measure phase wavefronts directly. Conventional
wisdom dictates such an approach would likely be fruitless. However, we show
that the phase correction accuracy needed to provide for non-zero secure key
rates through satellite-to-ground channels is achieved by our intensity-only
measurements. Our work shows, for the first time, how artificial intelligence
algorithms can replace phase-measuring equipment in the context of CV-QKD
delivered from space, thereby delivering an alternate deployment paradigm for
this global quantum-communication application.

通过使用卷积神经网络的强度测量来估计实时局部振荡器中的相位信息，以实现对 CV-QKD 星地通信中由大气湍流引起的相位失真的补偿，为全球量子通信应用提供了一种新的部署范式。

深度学习在卫星 - 地面 CV-QKD 中的相位校正

Phase Correction using Deep Learning for Satellite-to-Ground CV-QKD

In recent years, a great deal of attention has been paid to the Transformer
network for speech recognition tasks due to its excellent model performance.
However, the Transformer network always involves heavy computation and large
number of parameters, causing serious deployment problems in devices with
limited computation sources or storage memory. In this paper, a new lightweight
model called Sim-T has been proposed to expand the generality of the
Transformer model. Under the help of the newly developed multiplexing
technique, the Sim-T can efficiently compress the model with negligible
sacrifice on its performance. To be more precise, the proposed technique
includes two parts, that are, module weight multiplexing and attention score
multiplexing. Moreover, a novel decoder structure has been proposed to
facilitate the attention score multiplexing. Extensive experiments have been
conducted to validate the effectiveness of Sim-T. In Aishell-1 dataset, when
the proposed Sim-T is 48% parameter less than the baseline Transformer, 0.4%
CER improvement can be obtained. Alternatively, 69% parameter reduction can be
achieved if the Sim-T gives the same performance as the baseline Transformer.
With regard to the HKUST and WSJ eval92 datasets, CER and WER will be improved
by 0.3% and 0.2%, respectively, when parameters in Sim-T are 40% less than the
baseline Transformer.

本文提出了一种新型轻量级模型 Sim-T，通过分别运用模块权重多路复用和注意力分数多路复用两种技术，实现了对 Transformer 模型的有效压缩，并验证了其在语音识别任务上的高效性和优越性。

使用复用技术简化变形金刚网络以用于语音识别的 Sim-T

Sim-T: Simplify the Transformer Network by Multiplexing Technique for  Speech Recognition

A Cell-Free Massive MIMO (multiple-input multiple-output) system comprises a
very large number of distributed access points (APs)which simultaneously serve
a much smaller number of users over the same time/frequency resources based on
directly measured channel characteristics. The APs and users have only one
antenna each. The APs acquire channel state information through time-division
duplex operation and the reception of uplink pilot signals transmitted by the
users. The APs perform multiplexing/de-multiplexing through conjugate
beamforming on the downlink and matched filtering on the uplink. Closed-form
expressions for individual user uplink and downlink throughputs lead to max-min
power control algorithms. Max-min power control ensures uniformly good service
throughout the area of coverage. A pilot assignment algorithm helps to mitigate
the effects of pilot contamination, but power control is far more important in
that regard.
Cell-Free Massive MIMO has considerably improved performance with respect to
a conventional small-cell scheme, whereby each user is served by a dedicated
AP, in terms of both 95%-likely per-user throughput and immunity to shadow
fading spatial correlation. Under uncorrelated shadow fading conditions, the
cell-free scheme provides nearly 5-fold improvement in 95%-likely per-user
throughput over the small-cell scheme, and 10-fold improvement when shadow
fading is correlated.

探讨了利用大规模分布式基站和最小功耗控制算法，在时分双工操作中以无线信道直接测量为基础，通过共轭波束成形目的进行复用 / 分复用，对用户进行服务。对单个用户上下行吞吐量的封闭式表征，便于最小功耗控制。在与小型基站方案相比较中，无小区 Cell-Free Massive MIMO 表现出优异的性能表现，尤其是在阴影衰落条件下，可以提供近 10 倍的 95％- 可能性每用户吞吐量提升。