The Experts below are selected from a list of 51 Experts worldwide ranked by ideXlab platform
W Rupprecht - One of the best experts on this subject based on the ideXlab platform.
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soft Decision Output at sequential detection algorithms in digital mobile radio systems
Vehicular Technology Conference, 1994Co-Authors: G Zimmermann, W RupprechtAbstract:To improve the performance of the channel decoding in receivers for digital mobile radio systems the detector should be able to deliver reliability information about the symbol Decisions (soft-Decision). This paper is concerned with procedures which allow a soft-Decision Output at sequential detection algorithms. Simulation results obtained with these algorithms for the GSM system show the achievable gain over hard-Decision detectors and allow a comparison to the more complex Soft-Output Viterbi-algorithm. >
G Zimmermann - One of the best experts on this subject based on the ideXlab platform.
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soft Decision Output at sequential detection algorithms in digital mobile radio systems
Vehicular Technology Conference, 1994Co-Authors: G Zimmermann, W RupprechtAbstract:To improve the performance of the channel decoding in receivers for digital mobile radio systems the detector should be able to deliver reliability information about the symbol Decisions (soft-Decision). This paper is concerned with procedures which allow a soft-Decision Output at sequential detection algorithms. Simulation results obtained with these algorithms for the GSM system show the achievable gain over hard-Decision detectors and allow a comparison to the more complex Soft-Output Viterbi-algorithm. >
S.m. Berber - One of the best experts on this subject based on the ideXlab platform.
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A soft Decision Output convolutional decoder based on the application of neural networks
MILCOM 2005 - 2005 IEEE Military Communications Conference, 2005Co-Authors: S.m. BerberAbstract:The paper investigates BER characteristics of a new algorithm for decoding convolutional codes based on neural networks. The novelty of the algorithm is in its capability to generate soft Output estimates of the message bits encoded. It is shown that the defined noise energy function, which is traditionally used for the soft decoding algorithm of convolutional codes, can be related to the well known log likelihood function. The coding gain is calculated using a developed simulator of a coding communication system that uses a systematic 1/2-rate convolutional code
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Soft Decision Output decoding (SONNA) algorithm for convolutional codes based on artificial neural networks
2004 2nd International IEEE Conference on 'Intelligent Systems'. Proceedings (IEEE Cat. No.04EX791), 2004Co-Authors: S.m. BerberAbstract:The paper investigates new algorithm for decoding convolutions codes based on neural networks. The novelty of the algorithm is in its capability to generate soft Output estimates of the message bits encoded. The log likelihood function is derived, related to the noise energy function and then used as a criterion to decide which message bits are transmitted. The algorithm is demonstrated on a systematic 1/2-rate convolutional code for the assumed input message bits and the presence of the white Gaussian noise in the channel.
K L Su - One of the best experts on this subject based on the ideXlab platform.
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multilevel multisensor based intelligent recharging system for mobile robot
IEEE Transactions on Industrial Electronics, 2008Co-Authors: K L SuAbstract:Based on the sensor-based detection method, this paper presents an intelligent recharging system for a mobile robot. First, we design a flexible and reasonable intelligent recharging system for the mobile robot. It consists of a recharging station, a recharging device, and an intelligent power-detection module. The recharging station is designed to have 2 DOFs, such that it can move along the a;-axis and rotate about the z-axis. Meanwhile, a mechanism with two guarding poles is designed to provide a connection between the recharging station and the robot. In the recharging device, four power sensors are used to measure the power variety. Meanwhile, the adaptive fusion method is used to detect and diagnose the power-sensor status. Autoprotection circuits are also designed to prevent short and overload conditions during the recharging process. In the intelligent power-detection module, three power sensors are used to measure the power variety, and a redundant-management method is used to detect and diagnose the power-sensor status. The intelligent power-detection module can transmit a Decision Output to the main controller using an RS232 interface. Then, the main controller can decide an exact Output to control the recharging current using a rule-based method. Before practical implementation of the proposed method, computer simulation is performed, and the results show its feasibility. Then, based on the proposed method, different modules are implemented, and the experimental results also verify the feasibility of this method.
Taehyun Jeon - One of the best experts on this subject based on the ideXlab platform.
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An Low Complexity Hardware Implementation of MIMO Detector with Application to WLAN
2006 IEEE 63rd Vehicular Technology Conference, 2006Co-Authors: Chanho Yoon, Eunyoung Choi, Taehyun JeonAbstract:In this paper, we describe a FPGA implementation of MIMO detector for future wireless communication system with application to wireless LAN, targeted for upcoming 802.11n standard. The MIMO detector assumes 2 transmit and 3 receive antennas. In soft-Output demapper, we apply channel state information which effectively weights reliability information to soft-Decision Output bits for enhanced link-level performance. The implementation complexity is significantly reduced by avoiding repeated pseudo-inverse calculation for interference cancellation of every received symbol vector. Furthermore, the overall processing time and fabrication area it takes can be significantly reduced by applying bit reduction technique
