100 research outputs found
Spectrum Sharing between Cooperative Relay and Ad-hoc Networks: Dynamic Transmissions under Computation and Signaling Limitations
This paper studies a spectrum sharing scenario between a cooperative relay
network (CRN) and a nearby ad-hoc network. In particular, we consider a dynamic
spectrum access and resource allocation problem of the CRN. Based on sensing
and predicting the ad-hoc transmission behaviors, the ergodic traffic collision
time between the CRN and ad-hoc network is minimized subject to an ergodic
uplink throughput requirement for the CRN. We focus on real-time implementation
of spectrum sharing policy under practical computation and signaling
limitations. In our spectrum sharing policy, most computation tasks are
accomplished off-line. Hence, little real-time calculation is required which
fits the requirement of practical applications. Moreover, the signaling
procedure and computation process are designed carefully to reduce the time
delay between spectrum sensing and data transmission, which is crucial for
enhancing the accuracy of traffic prediction and improving the performance of
interference mitigation. The benefits of spectrum sensing and cooperative relay
techniques are demonstrated by our numerical experiments.Comment: 5 pages, 3 figures, to appear in IEEE International Conference on
Communications (ICC 2011
Exploring Global and Local Information for Anomaly Detection with Normal Samples
Anomaly detection aims to detect data that do not conform to regular
patterns, and such data is also called outliers. The anomalies to be detected
are often tiny in proportion, containing crucial information, and are suitable
for application scenes like intrusion detection, fraud detection, fault
diagnosis, e-commerce platforms, et al. However, in many realistic scenarios,
only the samples following normal behavior are observed, while we can hardly
obtain any anomaly information. To address such problem, we propose an anomaly
detection method GALDetector which is combined of global and local information
based on observed normal samples. The proposed method can be divided into a
three-stage method. Firstly, the global similar normal scores and the local
sparsity scores of unlabeled samples are computed separately. Secondly,
potential anomaly samples are separated from the unlabeled samples
corresponding to these two scores and corresponding weights are assigned to the
selected samples. Finally, a weighted anomaly detector is trained by loads of
samples, then the detector is utilized to identify else anomalies. To evaluate
the effectiveness of the proposed method, we conducted experiments on three
categories of real-world datasets from diverse domains, and experimental
results show that our method achieves better performance when compared with
other state-of-the-art methods.Comment: 6 pages, 1 figure
A Unified Approach to Optimal Opportunistic Spectrum Access under Collision Probability Constraint in Cognitive Radio Systems
We consider a cognitive radio system with one primary channel and one secondary user, and then we introduce a channel-usage pattern model and a fundamental access scheme in this system. Based on this model and fundamental access scheme, we study optimal opportunistic spectrum access problem and formulate it as an optimization problem that the secondary user maximizes spectrum holes utilization under the constraint of collision tolerable level. And then we propose a unified approach to solve this optimization problem. According to the solution of the optimization problem, we analyze and present optimal opportunistic spectrum access algorithms in several cases that the idle period follows uniform distribution, exponential distribution, and Pareto or generalized Pareto distribution. Theoretical analysis and simulation results both show that the optimal opportunistic spectrum access algorithms can maximize spectrum holes utilization under the constraint that the collision probability is bounded below collision tolerable level. The impact of sensing error is also analyzed by simulation
Few-shot Message-Enhanced Contrastive Learning for Graph Anomaly Detection
Graph anomaly detection plays a crucial role in identifying exceptional
instances in graph data that deviate significantly from the majority. It has
gained substantial attention in various domains of information security,
including network intrusion, financial fraud, and malicious comments, et al.
Existing methods are primarily developed in an unsupervised manner due to the
challenge in obtaining labeled data. For lack of guidance from prior knowledge
in unsupervised manner, the identified anomalies may prove to be data noise or
individual data instances. In real-world scenarios, a limited batch of labeled
anomalies can be captured, making it crucial to investigate the few-shot
problem in graph anomaly detection. Taking advantage of this potential, we
propose a novel few-shot Graph Anomaly Detection model called FMGAD (Few-shot
Message-Enhanced Contrastive-based Graph Anomaly Detector). FMGAD leverages a
self-supervised contrastive learning strategy within and across views to
capture intrinsic and transferable structural representations. Furthermore, we
propose the Deep-GNN message-enhanced reconstruction module, which extensively
exploits the few-shot label information and enables long-range propagation to
disseminate supervision signals to deeper unlabeled nodes. This module in turn
assists in the training of self-supervised contrastive learning. Comprehensive
experimental results on six real-world datasets demonstrate that FMGAD can
achieve better performance than other state-of-the-art methods, regardless of
artificially injected anomalies or domain-organic anomalies
Limited Feedback Precoding for Massive MIMO
The large-scale array antenna system with numerous low-power antennas deployed at the base station, also known as massive multiple-input multiple-output (MIMO), can provide a plethora of advantages over the classical array antenna system. Precoding is important to exploit massive MIMO performance, and codebook design is crucial due to the limited feedback channel. In this paper, we propose a new avenue of codebook design based on a Kronecker-type approximation of the array correlation structure for the uniform rectangular antenna array, which is preferable for the antenna deployment of massive MIMO. Although the feedback overhead is quite limited, the codebook design can provide an effective solution to support multiple users in different scenarios. Simulation results demonstrate that our proposed codebook outperforms the previously known codebooks remarkably
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