Channel capacity and average error rates in generalised-K fading channels

2010 ◽  
Vol 4 (11) ◽  
pp. 1364 ◽  
Author(s):  
G.P. Efthymoglou ◽  
N.Y. Ermolova ◽  
V.A. Aalo
Keyword(s):  
Fading Channels ◽  
Channel Capacity ◽  
Error Rates ◽  
Average Error

scholarly journals Unraveling city-specific signature and identifying sample origin locations for the data from CAMDA MetaSUB challenge

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Runzhi Zhang ◽  
Alejandro R. Walker ◽  
Susmita Datta
Keyword(s):  
Machine Learning ◽  
Error Rates ◽  
Average Error ◽  
Importance Score ◽  
Learning Methods ◽  
Principal Coordinates Analysis ◽  
Principal Coordinates ◽  
Total Variability ◽  
The Common ◽  
Feature Selecting

Abstract Background Composition of microbial communities can be location-specific, and the different abundance of taxon within location could help us to unravel city-specific signature and predict the sample origin locations accurately. In this study, the whole genome shotgun (WGS) metagenomics data from samples across 16 cities around the world and samples from another 8 cities were provided as the main and mystery datasets respectively as the part of the CAMDA 2019 MetaSUB “Forensic Challenge”. The feature selecting, normalization, three methods of machine learning, PCoA (Principal Coordinates Analysis) and ANCOM (Analysis of composition of microbiomes) were conducted for both the main and mystery datasets. Results Features selecting, combined with the machines learning methods, revealed that the combination of the common features was effective for predicting the origin of the samples. The average error rates of 11.93 and 30.37% of three machine learning methods were obtained for main and mystery datasets respectively. Using the samples from main dataset to predict the labels of samples from mystery dataset, nearly 89.98% of the test samples could be correctly labeled as “mystery” samples. PCoA showed that nearly 60% of the total variability of the data could be explained by the first two PCoA axes. Although many cities overlapped, the separation of some cities was found in PCoA. The results of ANCOM, combined with importance score from the Random Forest, indicated that the common “family”, “order” of the main-dataset and the common “order” of the mystery dataset provided the most efficient information for prediction respectively. Conclusions The results of the classification suggested that the composition of the microbiomes was distinctive across the cities, which could be used to identify the sample origins. This was also supported by the results from ANCOM and importance score from the RF. In addition, the accuracy of the prediction could be improved by more samples and better sequencing depth.

scholarly journals Channel capacity and performance evaluation of precoded MIMO-OFDM system with large-size constellation

2019 ◽  
Vol 9 (6) ◽  
pp. 5024
Author(s):  
Hussein A. Leftah ◽  
Huda N. Alminshid
Keyword(s):  
Fading Channels ◽  
Channel Capacity ◽  
Orthogonal Frequency Division Multiplexing ◽  
Multiple Input Multiple Output ◽  
Ofdm System ◽  
Large Size ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Mimo Ofdm ◽  
And Performance

<p>Multiple input-multiple output (MIMO) is a multipath diversity exploring approach which is emerged with orthogonal frequency division multiplexing (OFDM) to produce MIMO-OFDM that is widely used in wireless communications. This paper presents a discrete Hart-ley transform (DHT) precoded MIMO-OFDM system over multipath frequency-selective fading channel with large-size quadrature amplitude modulation (16-QAM, 64-QAM and 256-QAM). A mathematical models for the BER and channel capacity over mutlipath fading channels are also derived in this paper. Average Bit-error-rate (BER) and channel capacity of the presented system is considered and compared with that of the traditional MIMO-OFDM. Simulation results shows that the transmission performance and channel capacity of the proposed schemes is better than that of the traditional MIMO-OFDM without a pre-coder.</p>

scholarly journals Unraveling city-specific signature and identifying sample origin locations for the data from CAMDA MetaSUB challenge

2020 ◽  
Author(s):  
Runzhi Zhang ◽  
Alejandro R. Walker ◽  
Susmita Datta
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Error Rates ◽  
Average Error ◽  
Importance Score ◽  
Learning Methods ◽  
Principal Coordinates Analysis ◽  
Principal Coordinates ◽  
Total Variability ◽  
The Common

Abstract BackgroundComposition of microbial communities can be location specific, and the different abundance of taxon within location could help us to unravel city-specific signature and predict the sample origin locations accurately. In this study, the whole genome shotgun (WGS) metagenomics data from samples across 16 cities around the world and samples from another 8 cities were provided as the main and mystery datasets respectively as the part of the CAMDA 2019 MetaSUB “Forensic Challenge”. The feature selection, normalization, three methods of machine learning, PCoA (Principal Coordinates Analysis) and ANCOM (Analysis of composition of microbiomes) were conducted for both the main and mystery datasets.ResultsFeature selection, combined with the machines learning methods, revealed that the combination of the common features was effective for predicting the origin of the samples. The average error rates of 11.6% and 30.0% of three machine learning methods were obtained for main and mystery datasets respectively. Using the samples from main dataset to predict the labels of samples from mystery dataset, nearly 89.98% of the test samples could be correctly labeled as “mystery” samples. PCoA showed that nearly 60% of the total variability of the data could be explained by the first two PCoA axes. Although many cities overlapped, the separation of some cities was found in PCoA. The results of ANCOM, combined with importance score from the Random Forest, indicated that the common “family”, “order” of the main-dataset and the common “order” of the mystery dataset provided the most efficient information for prediction respectively.ConclusionsThe results of the classification suggested that the composition of the microbiomes was distinctive across the cities, which was also supported by the results from ANCOM and importance score from the RF. The analysis utilized in this study can be of great help in field of forensic science to efficiently predict the origin of the samples. And the accurate of the prediction could be improved by more samples and better sequencing depth.

scholarly journals Dual-hop Relaying Networks for Short-Packet URLLCs: Performance analysis and Optimization

2021 ◽  
Author(s):  
Nguyen Thi Yen Linh ◽  
Tu Ngo Hoang ◽  
Pham Ngoc Son ◽  
Vo Nguyen Quoc Bao
Keyword(s):  
Fading Channels ◽  
Optimization Problems ◽  
Signal To Noise Ratio ◽  
Error Rates ◽  
Decode And Forward ◽  
Location Optimization ◽  
Maximum Ratio Combining ◽  
System A ◽  
Qualitative Conclusion ◽  
Short Packet

<div>This paper investigates short-packet communications for the dual-hop decode-and-forward relaying system to facilitate ultra-reliable and low-latency communications. In this system, a selected relay having the highest signal-to-noise ratio (SNR) serves as a forwarder to support the unavailable direct link between the source and destination, whereas a maximum ratio combining technique is leveraged at the destination to achieve the highest diversity gain. Approximated expressions of end-to-end (e2e) block error rates (BLERs) are derived over quasi-static Rayleigh fading channels and the finite-blocklength regime. To gain more insights about the performance behavior in the high-SNR regime, we provide the asymptotic analysis with two approaches, from which the qualitative conclusion based on the diversity order is made. Furthermore, the power allocation and relay location optimization problems are also considered to minimize the asymptotic e2e BLER under the configuration constraints. Our analysis is verified through Monte-Carlo simulations, which yield the system parameters' impact on the system performance.</div>

Sign in / Sign up

Export Citation Format

Share Document