Analysis of very high throughput (VHT) at MAC and PHY layers under MIMO channel in IEEE 802.11ac WLAN

2017 ◽  
Author(s):  
Gul Zameen Khan ◽  
Ruben Gonzalez ◽  
Eun-Chan Park ◽  
Xin-Wen Wu
Keyword(s):  
High Throughput ◽  
Mimo Channel ◽  
Ieee 802.11Ac ◽  
Very High

A novel low PAPR preamble for very high throughput WLAN IEEE 802.11ac 80MHz system

2020 ◽  
Author(s):  
Wahyul A. Syafei ◽  
Achmad Hidayatno ◽  
Ajub A. Zahra ◽  
S. Pramono
Keyword(s):  
High Throughput ◽  
Ieee 802.11Ac ◽  
Very High

IEEE 802.11ac: Enhancements for very high throughput WLANs

2011 ◽  
Author(s):  
Eng Hwee Ong ◽  
Jarkko Kneckt ◽  
Olli Alanen ◽  
Zheng Chang ◽  
Toni Huovinen ◽  
...  
Keyword(s):  
High Throughput ◽  
Ieee 802.11Ac ◽  
Very High

scholarly journals A high throughput architecture for a low complexity soft-output demapping algorithm

2015 ◽  
Vol 13 ◽  
pp. 73-80 ◽  
Author(s):  
I. Ali ◽  
U. Wasenmüller ◽  
N. Wehn
Keyword(s):  
Wireless Communication ◽  
High Throughput ◽  
Turbo Code ◽  
Low Complexity ◽  
Implementation Cost ◽  
Qam Modulation ◽  
Iterative Systems ◽  
Very High ◽  
Complexity Algorithm ◽  
Hardware Performance

Abstract. Iterative channel decoders such as Turbo-Code and LDPC decoders show exceptional performance and therefore they are a part of many wireless communication receivers nowadays. These decoders require a soft input, i.e., the logarithmic likelihood ratio (LLR) of the received bits with a typical quantization of 4 to 6 bits. For computing the LLR values from a received complex symbol, a soft demapper is employed in the receiver. The implementation cost of traditional soft-output demapping methods is relatively large in high order modulation systems, and therefore low complexity demapping algorithms are indispensable in low power receivers. In the presence of multiple wireless communication standards where each standard defines multiple modulation schemes, there is a need to have an efficient demapper architecture covering all the flexibility requirements of these standards. Another challenge associated with hardware implementation of the demapper is to achieve a very high throughput in double iterative systems, for instance, MIMO and Code-Aided Synchronization. In this paper, we present a comprehensive communication and hardware performance evaluation of low complexity soft-output demapping algorithms to select the best algorithm for implementation. The main goal of this work is to design a high throughput, flexible, and area efficient architecture. We describe architectures to execute the investigated algorithms. We implement these architectures on a FPGA device to evaluate their hardware performance. The work has resulted in a hardware architecture based on the figured out best low complexity algorithm delivering a high throughput of 166 Msymbols/second for Gray mapped 16-QAM modulation on Virtex-5. This efficient architecture occupies only 127 slice registers, 248 slice LUTs and 2 DSP48Es.

scholarly journals Performance of Multibeam Very High Throughput Satellite Systems Based on FSO Feeder Links with HPA Nonlinearity

2020 ◽  
Author(s):  
Emna Zedini ◽  
Abla Kammoun ◽  
Mohamed-Slim Alouini
Keyword(s):  
Outage Probability ◽  
High Throughput ◽  
Direct Detection ◽  
Satellite Communications ◽  
Ergodic Capacity ◽  
Heterodyne Detection ◽  
High Signal ◽  
Free Space Optical ◽  
Asymptotic Results ◽  
Very High

Due to recent advances in laser satellite communications technology, free-space optical (FSO) links are presented as an ideal alternative to the conventional radio frequency (RF) feeder links of the geostationary satellite for next generation very high throughput satellite (VHTS) systems. In this paper, we investigate the performance of multibeam VHTS systems that account for nonlinear high power amplifiers at the transparent fixed gain satellite transponder. Specifically, we consider the forward link of such systems, where the RF user link is assumed to follow the shadowed Rician model and the FSO feeder link is modeled by the Gamma-Gamma distribution in the presence of beam wander and pointing errors where it operates under either the intensity modulation with direct detection or the heterodyne detection. Moreover, zero-forcing precoder is employed to mitigate the effect of inter-beam interference caused by the aggressive frequency reuse in the user link. The performance of the system under study is evaluated in terms of the outage probability, the average bit-error rate (BER), and the ergodic capacity that are derived in exact closed-forms in terms of the bivariate Meijer's G function. Simple asymptotic results for the outage probability and the average BER are also obtained at high signal-to-noise ratio.

scholarly journals Machine Learning (ML) Can Successfully Support Microscopic Differential Counts of Peripheral Blood Smears in a High Throughput Hematology Laboratory

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 45-46
Author(s):  
Christian Pohlkamp ◽  
Kapil Jhalani ◽  
Niroshan Nadarajah ◽  
Inseok Heo ◽  
William Wetton ◽  
...  
Keyword(s):  
Machine Learning ◽  
Single Cell ◽  
High Throughput ◽  
Peripheral Blood ◽  
Cell Types ◽  
Cell Type ◽  
Hematological Neoplasms ◽  
Blood Smears ◽  
Peripheral Blood Smears ◽  
Very High

Background: Cytomorphology is the gold standard for quick assessment of peripheral blood and bone marrow samples in hematological neoplasms. It is a broadly-accepted method for orchestrating more specific diagnostics including immunophenotyping or genetics. Inter-/intra-observer-reproducibility of single cell classification is only 75 to 90%. Only a limited number of cells (100 - 500 cells/smear) is read in a time-consuming procedure. Machine learning (ML) is more reliable where human skills are limited, i.e. in handling large amounts of data or images. We here tested ML to differentiate peripheral blood leukocytes in a high throughput hematology laboratory. Aim: To establish an ML-based cell classifier capable of identifying healthy and pathologic cells in digitalized peripheral blood smear scans at an accuracy competitive with or outperforming human expert level. Methods: We selected >2,600 smears out of our unique archive of > 250,000 peripheral blood smears from hematological neoplasms. Depending on quality, we scanned up to 1,000 single cell images per smear. For image acquisition, a Metafer Scanning System (Zeiss Axio Imager.Z2 microscope, automatic slide feeder and automatic oiling device) from MetaSystems (Altlussheim, GER) was used. Areas of interest were defined by pre-scan in 10x magnification followed by high resolution scan in 40x to generate cell images for analysis. Average capture times for 300/500 cells were 3:43/4:37 min We set up a supervised ML-learning model using colour images (144x144 pixels) as input, outputting predicted probabilities of 21 predefined classes. We used ImageNet-pretrained Xception as our base model. We trained, evaluated and deployed the model using Amazon SageMaker on a subset of 82,974 images randomly selected from 514,183 cells captured and labelled for this study. 20 different cell types and one garbage class were classified. We included cell type categories referring to the critical importance of detecting rare leukemia subtypes (e.g. APL). Numbers of images from respective 21 classes ranged from 1,830 to 14,909 (median: 2,945). Minority classes were up-sampledto handle imbalances. Each picture was labelled by highly skilled technicians (median years practicing in this laboratory: 5) and two independent hematologists (median years at microscope: 20). Results: On a separate test set of 8,297 cells, our classifier was able to predict any of the five cell types occurring in the peripheral blood of healthy individuals (PMN, lymphocytes, monocytes, eosinophils, basophils) at very high median accuracy (97.0%) Median prediction accuracy of 15 rare or pathological cell types was 91.3%. For six critical pathological cell forms (myeloblasts, atypical/bilobulated promyelocytes in APL/APLv, hairy cells, lymphoma cells,plasma cells), median accuracy was 93.4% (sensitivity 93.8%). We saw a very high "T98 accuracy" for these cell types (98.5%) which is the accuracy of cell type predictions with prediction probability >0.98 (achieved in 2231/2417 cases), implicating that critical cells predicted with probability <0.98 should be flagged for human expert validation with priority. For all 21 classes median accuracy was 91.7%. Accuracy was lower for cells representing consecutive steps of maturation, e.g. promyelo-/myelo-/metamyelocytes, reproducing inconsistencies from the human-built phenotypic classification system (s.Fig.). Conclusions: We demonstrate an automated workflow using automatic microscopic cell capturing and ML-driven cell differentiation in samples of hematologic patients. Reproducibility, accuracy, sensitivity and specificity are above 90%, for many cell types above 98%. By flagging suspicious cells for humanvalidation, this tool can support even experienced hematology professionals, especially in detecting rare cell types. Given an appropriate scanning speed, it clearly outperforms human investigators in terms of examination time and number of differentiated cells. An ML-based intelligence can make its skills accessible to hematology laboratories on site or after upload of scanned cell images, independent of time/location. A cloud-based infrastructure is available. A prospective head to head challenge between ML-based classifier and human experts comparing sensitivity and accuracy for detection of all cell classes in peripheral blood will be tested to proof suitability for routine use (NCT 4466059). Figure Disclosures Heo: AWS: Current Employment. Wetton:AWS: Current Employment. Drescher:MetaSystems: Current Employment. Hänselmann:MetaSystems: Current Employment. Lörch:MetaSystems: Current equity holder in private company.

Research on Improvement of the Next Generation IEEE802.11ac Channel Model

2013 ◽  
Vol 712-715 ◽  
pp. 1741-1745
Author(s):  
Hao Cai ◽  
Dan Ao Han
Keyword(s):  
Wireless Local Area Network ◽  
Channel Model ◽  
Local Area Network ◽  
Local Area ◽  
Task Group ◽  
Mimo Channel ◽  
Area Network ◽  
Power Delay Profile ◽  
Ieee 802.11Ac ◽  
Set Up

Based on the special correlation of antennas and the power delay profile (PDP) of the cluster model, six models of A-F have been established by the TGn task-group in total. On the basis of the new broadband wireless local area network (WLAN) standard--IEEE 802.11ac with larger bandwidth and multi-user requirements drawn up by the TGac task-group, in this paper, the IEEE 802.11ac channel model is set up by means of improving and simulating the indoor MIMO channel.

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