Space-based gravitational wave (GW) detectors will be able to observe signals
from sources that are otherwise nearly impossible from current ground-based
detection. Consequently, the well established signal detection method, matched
filtering, will require a complex template bank, leading to a computational
cost that is too expensive in practice. Here, we develop a high-accuracy GW
signal detection and extraction method for all space-based GW sources. As a
proof of concept, we show that a science-driven and uniform multi-stage deep
neural network can identify synthetic signals that are submerged in Gaussian
noise. Our method has more than 99% accuracy for signal detection of various
sources while obtaining at least 95% similarity compared with target signals.
We further demonstrate the interpretability and strong generalization behavior
for several extended scenarios.

本文提出了一种适用于所有空间引力波信号的高精度 GW 信号检测和提取方法，使用多阶段深度神经网络可以在高斯噪声中检测到具有超过 99％的准确性的合成信号。此方法在多种场景下有很强的解释性和泛化能力。

基于深度神经网络的空间引力波信号探测与提取

Space-based gravitational wave signal detection and extraction with deep neural network

We present a first proof-of-principle study for using deep neural networks
(DNNs) as a novel search method for continuous gravitational waves (CWs) from
unknown spinning neutron stars. The sensitivity of current wide-parameter-space
CW searches is limited by the available computing power, which makes neural
networks an interesting alternative to investigate, as they are extremely fast
once trained and have recently been shown to rival the sensitivity of matched
filtering for black-hole merger signals. We train a convolutional neural
network with residual (short-cut) connections and compare its detection power
to that of a fully-coherent matched-filtering search using the WEAVE pipeline.
As test benchmarks we consider two types of all-sky searches over the frequency
range from $20\,\mathrm{Hz}$ to $1000\,\mathrm{Hz}$: an `easy' search using
$T=10^5\,\mathrm{s}$ of data, and a `harder' search using $T=10^6\,\mathrm{s}$.
Detection probability $p_\mathrm{det}$ is measured on a signal population for
which matched filtering achieves $p_\mathrm{det}=90\%$ in Gaussian noise. In
the easiest test case ($T=10^5\,\mathrm{s}$ at $20\,\mathrm{Hz}$) the DNN
achieves $p_\mathrm{det}\sim88\%$, corresponding to a loss in sensitivity depth
of $\sim5\%$ versus coherent matched filtering. However, at higher-frequencies
and longer observation time the DNN detection power decreases, until
$p_\mathrm{det}\sim13\%$ and a loss of $\sim 66\%$ in sensitivity depth in the
hardest case ($T=10^6\,\mathrm{s}$ at $1000\,\mathrm{Hz}$). We study the DNN
generalization ability by testing on signals of different frequencies,
spindowns and signal strengths than they were trained on. We observe excellent
generalization: only five networks, each trained at a different frequency,
would be able to cover the whole frequency range of the search.

利用深度神经网络进行全天文搜寻频率范围为 20 Hz 到 1000 Hz 的连续重力波信号，结果显示相对于完全匹配滤波搜寻，神经网络在容易的情况下性能可以与之匹敌，在困难的情况下性能下降，通过测试不同的信号频率和强度，发现该方法具有良好的泛化能力，可以用于搜寻全部的频率范围。

深度学习连续引力波

Deep-Learning continuous gravitational waves

In massive MIMO base stations, power consumption and cost of the low-noise
amplifiers (LNAs) can be substantial because of the many antennas. We
investigate the feasibility of inexpensive, power efficient LNAs, which
inherently are less linear. A polynomial model is used to characterize the
nonlinear LNAs and to derive the second-order statistics and spatial
correlation of the distortion. We show that, with spatial matched filtering
(maximum-ratio combining) at the receiver, some distortion terms combine
coherently, and that the SINR of the symbol estimates therefore is limited by
the linearity of the LNAs. Furthermore, it is studied how the power from a
blocker in the adjacent frequency band leaks into the main band and creates
distortion. The distortion term that scales cubically with the power received
from the blocker has a spatial correlation that can be filtered out by spatial
processing and only the coherent term that scales quadratically with the power
remains. When the blocker is in free-space line-of-sight and the LNAs are
identical, this quadratic term has the same spatial direction as the desired
signal, and hence cannot be removed by linear receiver processing.

研究使用经济高效的低噪放大器（LNAs）在 massive MIMO 基站中实现空间匹配滤波以消除 LNA 的线性畸变和相邻频段泄漏带来的干扰。

空间滤波对 Massive MIMO 基站低噪声放大器畸变的影响

Impact of Spatial Filtering on Distortion from Low-Noise Amplifiers in  Massive MIMO Base Stations

In this paper, we consider the potential of data-transmission in a system
with a massive number of radiating and sensing elements, thought of as a
contiguous surface of electromagnetically active material. We refer to this as
a large intelligent surface (LIS). The "LIS" is a newly proposed concept, which
conceptually goes beyond contemporary massive MIMO technology, that arises from
our vision of a future where man-made structures are electronically active with
integrated electronics and wireless communication making the entire environment
"intelligent".
We consider capacities of single-antenna autonomous terminals communicating
to the LIS where the entire surface is used as a receiving antenna array. Under
the condition that the surface-area is sufficiently large, the received signal
after a matched-filtering (MF) operation can be closely approximated by a
sinc-function-like intersymbol interference (ISI) channel. We analyze the
capacity per square meter (m^2) deployed surface, \hat{C}, that is achievable
for a fixed transmit power per volume-unit, \hat{P}. Moreover, we also show
that the number of independent signal dimensions per m deployed surface is
2/\lambda for one-dimensional terminal-deployment, and \pi/\lambda^2 per m^2
for two and three dimensional terminal-deployments. Lastly, we consider
implementations of the LIS in the form of a grid of conventional antenna
elements and show that, the sampling lattice that minimizes the surface-area of
the LIS and simultaneously obtains one signal space dimension for every spent
antenna is the hexagonal lattice. We extensively discuss the design of the
state-of-the-art low-complexity channel shortening (CS) demodulator for
data-transmission with the LIS.

本文提出了一种大面积智能面（LIS）的概念，通过匹配滤波操作，研究单天线终端与 LIS 的容量和最佳抽样格点。同时，设计了用于数据传输的低复杂度通道缩短解调器。