503 research outputs found
Second-Order Coding Rate of Quasi-Static Rayleigh-Product MIMO Channels
With the development of innovative applications that require high reliability
and low latency, ultra-reliable and low latency communications become critical
for wireless networks. In this paper, the second-order coding rate of the
coherent quasi-static Rayleigh-product MIMO channel is investigated. We
consider the coding rate within O(1/\sqrt(Mn)) of the capacity, where M and n
denote the number of transmit antennas and the blocklength, respectively, and
derive the closed-form upper and lower bounds for the optimal average error
probability. This analysis is achieved by setting up a central limit theorem
(CLT) for the mutual information density (MID) with the assumption that the
block-length, the number of the scatterers, and the number of the antennas go
to infinity with the same pace. To obtain more physical insights, the high and
low SNR approximations for the upper and lower bounds are also given. One
interesting observation is that rank-deficiency degrades the performance of
MIMO systems with FBL and the fundamental limits of the Rayleigh-product
channel approaches those of the single Rayleigh case when the number of
scatterers approaches infinity. Finally, the fitness of the CLT and the gap
between the derived bounds and the performance of practical LDPC coding are
illustrated by simulations
ProbPS: A new model for peak selection based on quantifying the dependence of the existence of derivative peaks on primary ion intensity
<p>Abstract</p> <p>Background</p> <p>The analysis of mass spectra suggests that the existence of derivative peaks is strongly dependent on the intensity of the primary peaks. Peak selection from tandem mass spectrum is used to filter out noise and contaminant peaks. It is widely accepted that a valid primary peak tends to have high intensity and is accompanied by derivative peaks, including isotopic peaks, neutral loss peaks, and complementary peaks. Existing models for peak selection ignore the dependence between the existence of the derivative peaks and the intensity of the primary peaks. Simple models for peak selection assume that these two attributes are independent; however, this assumption is contrary to real data and prone to error.</p> <p>Results</p> <p>In this paper, we present a statistical model to quantitatively measure the dependence of the derivative peak's existence on the primary peak's intensity. Here, we propose a statistical model, named ProbPS, to capture the dependence in a quantitative manner and describe a statistical model for peak selection. Our results show that the quantitative understanding can successfully guide the peak selection process. By comparing ProbPS with AuDeNS we demonstrate the advantages of our method in both filtering out noise peaks and in improving <it>de novo </it>identification. In addition, we present a tag identification approach based on our peak selection method. Our results, using a test data set, suggest that our tag identification method (876 correct tags in 1000 spectra) outperforms PepNovoTag (790 correct tags in 1000 spectra).</p> <p>Conclusions</p> <p>We have shown that ProbPS improves the accuracy of peak selection which further enhances the performance of de novo sequencing and tag identification. Thus, our model saves valuable computation time and improving the accuracy of the results.</p
Logging practices in software engineering : A systematic mapping study
Background: Logging practices provide the ability to record valuable runtime information of software systems to support operations tasks such as service monitoring and troubleshooting. However, current logging practices face common challenges. On the one hand, although the importance of logging practices has been broadly recognized, most of them are still conducted in an arbitrary or ad-hoc manner, ending up with questionable or inadequate support to perform these tasks. On the other hand, considerable research effort has been carried out on logging practices, however, few of the proposed techniques or methods have been widely adopted in industry. Objective: This study aims to establish a comprehensive understanding of the research state of logging practices, with a focus on unveiling possible problems and gaps which further shed light on the potential future research directions. Method: We carried out a systematic mapping study on logging practices with 56 primary studies. Results: This study provides a holistic report of the existing research on logging practices by systematically synthesizing and analyzing the focus and inter-relationship of the existing research in terms of issues, research topics and solution approaches. Using 3W1H ā Why to log , Where to log , What to log and How well is the logging āas the categorization standard, we find that: (1) the best known issues in logging practices have been repeatedly investigated; (2) the issues are often studied separately without considering their intricate relationships; (3) the Where and What questions have attracted the majority of research attention while little research effort has been made on the Why and How well questions; and (4) the relationships between issues, research topics, and approaches regarding logging practices appear many-to-many, which indicates a lack of profound understanding of the issues in practice and how they should be appropriately tackled. Conclusions: This study indicates a need to advance the state of research on logging practices. For example, more research effort should be invested on why to log to set the anchor of logging practices as well as on how well is the logging to close the loop. In addition, a holistic process perspective should be taken into account in both the research and the adoption related to logging practices
High-dimensional Clustering onto Hamiltonian Cycle
Clustering aims to group unlabelled samples based on their similarities. It
has become a significant tool for the analysis of high-dimensional data.
However, most of the clustering methods merely generate pseudo labels and thus
are unable to simultaneously present the similarities between different
clusters and outliers. This paper proposes a new framework called
High-dimensional Clustering onto Hamiltonian Cycle (HCHC) to solve the above
problems. First, HCHC combines global structure with local structure in one
objective function for deep clustering, improving the labels as relative
probabilities, to mine the similarities between different clusters while
keeping the local structure in each cluster. Then, the anchors of different
clusters are sorted on the optimal Hamiltonian cycle generated by the cluster
similarities and mapped on the circumference of a circle. Finally, a sample
with a higher probability of a cluster will be mapped closer to the
corresponding anchor. In this way, our framework allows us to appreciate three
aspects visually and simultaneously - clusters (formed by samples with high
probabilities), cluster similarities (represented as circular distances), and
outliers (recognized as dots far away from all clusters). The experiments
illustrate the superiority of HCHC
Message Passing Meets Graph Neural Networks: A New Paradigm for Massive MIMO Systems
As one of the core technologies for 5G systems, massive multiple-input
multiple-output (MIMO) introduces dramatic capacity improvements along with
very high beamforming and spatial multiplexing gains. When developing efficient
physical layer algorithms for massive MIMO systems, message passing is one
promising candidate owing to the superior performance. However, as their
computational complexity increases dramatically with the problem size, the
state-of-the-art message passing algorithms cannot be directly applied to
future 6G systems, where an exceedingly large number of antennas are expected
to be deployed. To address this issue, we propose a model-driven deep learning
(DL) framework, namely the AMP-GNN for massive MIMO transceiver design, by
considering the low complexity of the AMP algorithm and adaptability of GNNs.
Specifically, the structure of the AMP-GNN network is customized by unfolding
the approximate message passing (AMP) algorithm and introducing a graph neural
network (GNN) module into it. The permutation equivariance property of AMP-GNN
is proved, which enables the AMP-GNN to learn more efficiently and to adapt to
different numbers of users. We also reveal the underlying reason why GNNs
improve the AMP algorithm from the perspective of expectation propagation,
which motivates us to amalgamate various GNNs with different message passing
algorithms. In the simulation, we take the massive MIMO detection to exemplify
that the proposed AMP-GNN significantly improves the performance of the AMP
detector, achieves comparable performance as the state-of-the-art DL-based MIMO
detectors, and presents strong robustness to various mismatches.Comment: 30 Pages, 7 Figures, and 4 Tables. This paper has been submitted to
the IEEE for possible publication. arXiv admin note: text overlap with
arXiv:2205.1062
- ā¦