The Experts below are selected from a list of 26751 Experts worldwide ranked by ideXlab platform
David C Tscharke - One of the best experts on this subject based on the ideXlab platform.
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altered cd8 t cell immunodominance after vaccinia virus infection and the naive repertoire in inbred and f1 mice
Journal of Immunology, 2010Co-Authors: Inge E. A. Flesch, Nicole L La Gruta, Vijay Panchanathan, Tania Cukalac, Yik Chun Wong, David C TscharkeAbstract:Previous studies of CD8 + T cell immunodominance after primary virus infection of F 1 mice compared with their inbred parents have generally concluded that no dramatic changes occur. In this study, we revisit this issue using vaccinia virus (VACV), which has a large genome, a recently defined immunodominance hierarchy in mice, and is a Candidate Vector for vaccines. We found that immunogenicity of VACV peptides defined using inbred mice was highly variable in F 1 progeny: some peptides were equally immunogenic in F 1 and inbred, whereas others elicited responses that were reduced by >90% in F 1 mice. Furthermore, the dominance of a peptide in the relevant inbred parent did not predict whether it would be poorly immunogenic in F 1 mice. This result held using F 1 hybrids of MHC-congenic mice, suggesting that MHC differences alone were responsible. It was also extended to foreign epitopes expressed by an rVACV vaccine. F 1 mice were less able to mount responses to the poorly immunogenic peptides when used as a sole immunogen, ruling out immunodomination. In addition, conserved TCR Vβ usage between inbred and F 1 mice did not always correlate with strong responses in F 1 mice. However, direct estimation of naive precursor numbers showed that these were reduced in F 1 compared with inbred mice for specificities that were poorly immunogenic in the hybrids. These data have implications for our understanding of the extent to which MHC diversity alters the range of epitopes that are immunogenic in outbred populations.
Divyang Rawal - One of the best experts on this subject based on the ideXlab platform.
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Mitigating Empty Vector Set Using Enlarged QRLRL-M Soft SM-MIMO Detector
Wireless Personal Communications, 2015Co-Authors: Divyang Rawal, Seungjae Bahng, Youn Ok Park, Vijay Kumar Chakka, Hyeong Sook ParkAbstract:An enlargement of the Candidate Vector set of QR-least reliable layer (QR-LRL) based MIMO detector for efficient soft output generation is proposed. Previous work (Bahng et al. in IEICE Trans Commun, E89---B(10):2956---2960, 2008) shows that the QR-LRL based MIMO detector approaches hard decision output ML performance, but does not match soft output ML performance due to empty Candidate Vector set problem. Performance degradation is more severe when modulation order is low. Some of the previous methods have provided solutions to empty Vector set (EVS) problem (Kawai et al. in IEICE Trans Commun, E88---B(1):47---57, 2005; Bahng et al. in IEICE Trans Commun, E89---B(10):2956---2960, 2008; Kim et al. in IEICE Trans Commun, E92---B(11):3512---3515, 2009), but are not efficient in terms of performance or computation complexity. In this paper, we enlarge the Candidate Vector set of QR-LRL detector by applying every constellation point at each layer. The proposed detector thus effectively solves the EVS problem and achieves soft ML performance while keeping the computation complexity low, especially at low modulation order.
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Enlarged QR-LRL based SM-MIMO detector for efficient soft output generation
TENCON 2014 - 2014 IEEE Region 10 Conference, 2014Co-Authors: Divyang Rawal, Seungjae Bahng, Youn Ok Park, Vijay Kumar Chakka, Hyeong Sook ParkAbstract:An enlargement of Candidate Vector set of QR-LRL (QR - Least Reliable Layer) based MIMO detector for efficient soft output generation is proposed. Previous work [8] shows that QR-LRL based MIMO detector approaches hard decision output ML performance, but does not match the soft output ML performance due to empty Candidate Vector set problem. Performance degradation is more severe when modulation order is low. Some of the previous methods have provided solutions to mitigate Empty Vector Set (EVS) problem [4] [8] [9], but are not efficient in terms of performance or computation complexity. In this paper, we enlarged Candidate Vector set of QR-LRL detector by applying every constellation point at each layer. The proposed detector thus effectively removes EVS problem and achieves soft ML performance while keeping the computation complexity low, especially at low modulation order.
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ICTC - Efficiently using extrinsic gain for Candidate Vectors selection in QR-LRL based IDD MIMO receiver
2012 International Conference on ICT Convergence (ICTC), 2012Co-Authors: Divyang Rawal, Seungjae Bahng, Youn Ok Park, Hoon Lee, Choi-jung Pil, Vijay Kumar ChakkaAbstract:Various OSIC MIMO detectors are considered suboptimal in terms of performance and complexity, however suffers from error propagation. QR-LRL is considered to be most effective to mitigate error performance as it detects the least reliable layer(LRL) symbol first. It achieves hard ML performance, but suffers from empty Vector set(EVS) problem for soft output generation. Some of the previous work mitigates this problem effectively and achieves soft ML performance at the cost of complexity. QR-LRL based IDD(Iterative Detection and Decoding) is an alternative solution, which exchanges the extrinsic information between detector and decoder. Based on feed back knowledge from decoder, decision is made to update Candidate Vector set for soft output generation. Simulation results shows that significant performance improvement is achieved while keeping the receiver design simple.
Vijay Kumar Chakka - One of the best experts on this subject based on the ideXlab platform.
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Mitigating Empty Vector Set Using Enlarged QRLRL-M Soft SM-MIMO Detector
Wireless Personal Communications, 2015Co-Authors: Divyang Rawal, Seungjae Bahng, Youn Ok Park, Vijay Kumar Chakka, Hyeong Sook ParkAbstract:An enlargement of the Candidate Vector set of QR-least reliable layer (QR-LRL) based MIMO detector for efficient soft output generation is proposed. Previous work (Bahng et al. in IEICE Trans Commun, E89---B(10):2956---2960, 2008) shows that the QR-LRL based MIMO detector approaches hard decision output ML performance, but does not match soft output ML performance due to empty Candidate Vector set problem. Performance degradation is more severe when modulation order is low. Some of the previous methods have provided solutions to empty Vector set (EVS) problem (Kawai et al. in IEICE Trans Commun, E88---B(1):47---57, 2005; Bahng et al. in IEICE Trans Commun, E89---B(10):2956---2960, 2008; Kim et al. in IEICE Trans Commun, E92---B(11):3512---3515, 2009), but are not efficient in terms of performance or computation complexity. In this paper, we enlarge the Candidate Vector set of QR-LRL detector by applying every constellation point at each layer. The proposed detector thus effectively solves the EVS problem and achieves soft ML performance while keeping the computation complexity low, especially at low modulation order.
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Enlarged QR-LRL based SM-MIMO detector for efficient soft output generation
TENCON 2014 - 2014 IEEE Region 10 Conference, 2014Co-Authors: Divyang Rawal, Seungjae Bahng, Youn Ok Park, Vijay Kumar Chakka, Hyeong Sook ParkAbstract:An enlargement of Candidate Vector set of QR-LRL (QR - Least Reliable Layer) based MIMO detector for efficient soft output generation is proposed. Previous work [8] shows that QR-LRL based MIMO detector approaches hard decision output ML performance, but does not match the soft output ML performance due to empty Candidate Vector set problem. Performance degradation is more severe when modulation order is low. Some of the previous methods have provided solutions to mitigate Empty Vector Set (EVS) problem [4] [8] [9], but are not efficient in terms of performance or computation complexity. In this paper, we enlarged Candidate Vector set of QR-LRL detector by applying every constellation point at each layer. The proposed detector thus effectively removes EVS problem and achieves soft ML performance while keeping the computation complexity low, especially at low modulation order.
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ICTC - Efficiently using extrinsic gain for Candidate Vectors selection in QR-LRL based IDD MIMO receiver
2012 International Conference on ICT Convergence (ICTC), 2012Co-Authors: Divyang Rawal, Seungjae Bahng, Youn Ok Park, Hoon Lee, Choi-jung Pil, Vijay Kumar ChakkaAbstract:Various OSIC MIMO detectors are considered suboptimal in terms of performance and complexity, however suffers from error propagation. QR-LRL is considered to be most effective to mitigate error performance as it detects the least reliable layer(LRL) symbol first. It achieves hard ML performance, but suffers from empty Vector set(EVS) problem for soft output generation. Some of the previous work mitigates this problem effectively and achieves soft ML performance at the cost of complexity. QR-LRL based IDD(Iterative Detection and Decoding) is an alternative solution, which exchanges the extrinsic information between detector and decoder. Based on feed back knowledge from decoder, decision is made to update Candidate Vector set for soft output generation. Simulation results shows that significant performance improvement is achieved while keeping the receiver design simple.
Hyeong Sook Park - One of the best experts on this subject based on the ideXlab platform.
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Mitigating Empty Vector Set Using Enlarged QRLRL-M Soft SM-MIMO Detector
Wireless Personal Communications, 2015Co-Authors: Divyang Rawal, Seungjae Bahng, Youn Ok Park, Vijay Kumar Chakka, Hyeong Sook ParkAbstract:An enlargement of the Candidate Vector set of QR-least reliable layer (QR-LRL) based MIMO detector for efficient soft output generation is proposed. Previous work (Bahng et al. in IEICE Trans Commun, E89---B(10):2956---2960, 2008) shows that the QR-LRL based MIMO detector approaches hard decision output ML performance, but does not match soft output ML performance due to empty Candidate Vector set problem. Performance degradation is more severe when modulation order is low. Some of the previous methods have provided solutions to empty Vector set (EVS) problem (Kawai et al. in IEICE Trans Commun, E88---B(1):47---57, 2005; Bahng et al. in IEICE Trans Commun, E89---B(10):2956---2960, 2008; Kim et al. in IEICE Trans Commun, E92---B(11):3512---3515, 2009), but are not efficient in terms of performance or computation complexity. In this paper, we enlarge the Candidate Vector set of QR-LRL detector by applying every constellation point at each layer. The proposed detector thus effectively solves the EVS problem and achieves soft ML performance while keeping the computation complexity low, especially at low modulation order.
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Enlarged QR-LRL based SM-MIMO detector for efficient soft output generation
TENCON 2014 - 2014 IEEE Region 10 Conference, 2014Co-Authors: Divyang Rawal, Seungjae Bahng, Youn Ok Park, Vijay Kumar Chakka, Hyeong Sook ParkAbstract:An enlargement of Candidate Vector set of QR-LRL (QR - Least Reliable Layer) based MIMO detector for efficient soft output generation is proposed. Previous work [8] shows that QR-LRL based MIMO detector approaches hard decision output ML performance, but does not match the soft output ML performance due to empty Candidate Vector set problem. Performance degradation is more severe when modulation order is low. Some of the previous methods have provided solutions to mitigate Empty Vector Set (EVS) problem [4] [8] [9], but are not efficient in terms of performance or computation complexity. In this paper, we enlarged Candidate Vector set of QR-LRL detector by applying every constellation point at each layer. The proposed detector thus effectively removes EVS problem and achieves soft ML performance while keeping the computation complexity low, especially at low modulation order.
Inge E. A. Flesch - One of the best experts on this subject based on the ideXlab platform.
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altered cd8 t cell immunodominance after vaccinia virus infection and the naive repertoire in inbred and f1 mice
Journal of Immunology, 2010Co-Authors: Inge E. A. Flesch, Nicole L La Gruta, Vijay Panchanathan, Tania Cukalac, Yik Chun Wong, David C TscharkeAbstract:Previous studies of CD8 + T cell immunodominance after primary virus infection of F 1 mice compared with their inbred parents have generally concluded that no dramatic changes occur. In this study, we revisit this issue using vaccinia virus (VACV), which has a large genome, a recently defined immunodominance hierarchy in mice, and is a Candidate Vector for vaccines. We found that immunogenicity of VACV peptides defined using inbred mice was highly variable in F 1 progeny: some peptides were equally immunogenic in F 1 and inbred, whereas others elicited responses that were reduced by >90% in F 1 mice. Furthermore, the dominance of a peptide in the relevant inbred parent did not predict whether it would be poorly immunogenic in F 1 mice. This result held using F 1 hybrids of MHC-congenic mice, suggesting that MHC differences alone were responsible. It was also extended to foreign epitopes expressed by an rVACV vaccine. F 1 mice were less able to mount responses to the poorly immunogenic peptides when used as a sole immunogen, ruling out immunodomination. In addition, conserved TCR Vβ usage between inbred and F 1 mice did not always correlate with strong responses in F 1 mice. However, direct estimation of naive precursor numbers showed that these were reduced in F 1 compared with inbred mice for specificities that were poorly immunogenic in the hybrids. These data have implications for our understanding of the extent to which MHC diversity alters the range of epitopes that are immunogenic in outbred populations.
