609 research outputs found
Blind recognition of analog modulation schemes for software defined radio
With the emergence of software defined radios (SDRs), an adaptive receiver is needed that can configure various parameters, such as carrier frequency, bandwidth, symbol timing, and signal to noise ratio (SNR), and automatically identify modulation schemes. In this dissertation research, several fundamental SDR tasks for analog modulations are investigated, since analog radios are often used by civil government agencies and some unconventional military forces. Hence, the detection and recognition of old technology analog modulations remain an important task both for civil and military electronic support systems and for notional cognitive radios.
In this dissertation, a Cyclostationarity-Based Decision Tree classifier is developed to separate between analog modulations and digital modulations, and classify signals into several subsets of modulation types. In order to further recognize the specific modulation type of analog signals, more effort and work are, however, needed. For this purpose, two general methods for automatic modulation classification (AMC), feature- based method and likelihood-based method, are investigated in this dissertation for analog modulation schemes. For feature-based method, a multi-class SVM-based AMC classifier is developed. After training, the developed classifier can achieve high classification accuracy in a wide range of SNR. While the likelihood-based methods for digital modulation types have been well developed, it is noted that the likelihood-based methods for analog modulation types are seldom explored in the literature. Average-Likelihood-Ratio-Testing based AMC algorithms have been developed to automatically classify AM, DSB and FM signals in both coherent and non-coherent situations In addition, the Non-Data-Aided SNR estimation algorithms are investigated, which can be used to estimate the signal power and noise power either before or after modulation classification
Querying Streaming System Monitoring Data for Enterprise System Anomaly Detection
The need for countering Advanced Persistent Threat (APT) attacks has led to
the solutions that ubiquitously monitor system activities in each enterprise
host, and perform timely abnormal system behavior detection over the stream of
monitoring data. However, existing stream-based solutions lack explicit
language constructs for expressing anomaly models that capture abnormal system
behaviors, thus facing challenges in incorporating expert knowledge to perform
timely anomaly detection over the large-scale monitoring data. To address these
limitations, we build SAQL, a novel stream-based query system that takes as
input, a real-time event feed aggregated from multiple hosts in an enterprise,
and provides an anomaly query engine that queries the event feed to identify
abnormal behaviors based on the specified anomaly models. SAQL provides a
domain-specific query language, Stream-based Anomaly Query Language (SAQL),
that uniquely integrates critical primitives for expressing major types of
anomaly models. In the demo, we aim to show the complete usage scenario of SAQL
by (1) performing an APT attack in a controlled environment, and (2) using SAQL
to detect the abnormal behaviors in real time by querying the collected stream
of system monitoring data that contains the attack traces. The audience will
have the option to interact with the system and detect the attack footprints in
real time via issuing queries and checking the query results through a
command-line UI.Comment: Accepted paper at ICDE 2020 demonstrations track. arXiv admin note:
text overlap with arXiv:1806.0933
Robustness of an energy storage system-based stabiliser to suppress inter-area oscillations in a multi-machine power system
Designing Enhanced Multi-dimensional Constellations for Code-Domain NOMA
This paper presents an enhanced design of multi-dimensional (MD)
constellations which play a pivotal role in many communication systems such as
code-domain non-orthogonal multiple access (CD-NOMA). MD constellations are
attractive as their structural properties, if properly designed, lead to signal
space diversity and hence improved error rate performance. Unlike the existing
works which mostly focus on MD constellations with large minimum Euclidean
distance (MED), we look for new MD constellations with additional feature that
the minimum product distance (MPD) is also large. To this end, a non-convex
optimization problem is formulated and then solved by the convex-concave
procedure (CCCP). Compared with the state-of-the-art literature, our proposed
MD constellations lead to significant error performance enhancement over
Rayleigh fading channels whilst maintaining almost the same performance over
the Gaussian channels. To demonstrate their application, we also show that
these MD constellations give rise to good codebooks in sparse code multiple
access systems. All the obtained MD constellations can be found in
https://github.com/Aureliano1/Multi-dimensional-constellation
Uncertainty-Aware Bootstrap Learning for Joint Extraction on Distantly-Supervised Data
Jointly extracting entity pairs and their relations is challenging when
working on distantly-supervised data with ambiguous or noisy labels. To
mitigate such impact, we propose uncertainty-aware bootstrap learning, which is
motivated by the intuition that the higher uncertainty of an instance, the more
likely the model confidence is inconsistent with the ground truths.
Specifically, we first explore instance-level data uncertainty to create an
initial high-confident examples. Such subset serves as filtering noisy
instances and facilitating the model to converge fast at the early stage.
During bootstrap learning, we propose self-ensembling as a regularizer to
alleviate inter-model uncertainty produced by noisy labels. We further define
probability variance of joint tagging probabilities to estimate inner-model
parametric uncertainty, which is used to select and build up new reliable
training instances for the next iteration. Experimental results on two large
datasets reveal that our approach outperforms existing strong baselines and
related methods.Comment: ACL 2023 main conference short pape
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