FRAME-ERROR DETECTION METHOD AND DEVICE FOR ERROR MASKING, SPECIALLY IN GSM TRANSMISSIONS
    441.
    发明申请
    FRAME-ERROR DETECTION METHOD AND DEVICE FOR ERROR MASKING, SPECIALLY IN GSM TRANSMISSIONS 审中-公开
    方法和系统帧故障检测,以错误掩盖特别是在GSM变速箱

    公开(公告)号:WO98038764A1

    公开(公告)日:1998-09-03

    申请号:PCT/DE1998/000221

    申请日:1998-01-23

    CPC classification number: H04L1/201 H04L1/0061 H04L1/007 H04L1/20

    Abstract: The invention relates to a method for frame-error detection, wherein frames are defined as being wrong when a determined logical combination of several different comparison criteria is satisfied. The inventive method enables highly efficient frame-error detection to be carried out, specially when the parametric source coding technique is used.

    Abstract translation: 对于帧将解释用于帧错误检测的方法是错误的,当某个逻辑组合填充有几个不同的比较准则。 用这种方法,有可能提供一种帧的错误检测,并且特别是与使用参数源编码方法的具有更高的可靠性进行。

    A METHOD FOR FRAME QUALITY DETECTION AND A RECEIVER
    442.
    发明申请
    A METHOD FOR FRAME QUALITY DETECTION AND A RECEIVER 审中-公开
    一种用于框架质量检测的方法和接收器

    公开(公告)号:WO1997027686A1

    公开(公告)日:1997-07-31

    申请号:PCT/FI1997000032

    申请日:1997-01-23

    CPC classification number: H04L1/201 H04L1/20

    Abstract: The invention relates to a method for frame quality detection and a receiver for implementing the method. For frame quality detection, results A and B (27 and 28) are compared with each other and a predetermined threshold value (241) in the method. Result A (27) is formed either as the sum/product of soft bit decisions or as the bit error rate from bursts comprising bits from successive frames N and N + 1. Result B (28) is formed in the same way of frames N - 1 and N. When comparing results A and B (27 and 28) with each other, frame N is determined to be bad if either result (27 or 28) is essentially smaller than the other result (28 or 27). Frame N is also determined to be bad if both results (27 and 28) are smaller than the predetermined threshold value (241). The method improves detection of a bad frame and accordingly reduces the interference sound received by a receiver and caused by erroneously interpreted bad frames, especially during silence.

    Abstract translation: 本发明涉及一种用于帧质量检测的方法和一种用于实现该方法的接收机。 对于帧质量检测,结果A和B(27和28)在该方法中彼此比较和预定阈值(241)。 结果A(27)被形成为软比特决定的和/积或来自包括来自连续帧N和N + 1的比特的脉冲串的误码率。结果B(28)以与帧N相同的方式形成 当比较结果A和B(27和28)时,如果任一结果(27或28)基本上小于另一结果(28或27),则将帧N确定为差。 如果结果(27和28)都小于预定阈值(241),则帧N也被确定为坏。 该方法改善了对错误帧的检测,从而减少了接收机接收到的干扰声音,并由错误解读的坏帧引起,特别是在静音期间。

    SOFT ERROR CORRECTION IN A TDMA RADIO SYSTEM
    443.
    发明申请
    SOFT ERROR CORRECTION IN A TDMA RADIO SYSTEM 审中-公开
    TDMA无线电系统中的软错误校正

    公开(公告)号:WO1995016315A1

    公开(公告)日:1995-06-15

    申请号:PCT/SE1994001157

    申请日:1994-12-01

    CPC classification number: H04L1/201 G10L19/005 H04L1/20

    Abstract: In a TDMA cellular telephone system, an error concealment method provides parameter interpolation based on soft quality measures that will enhance the speech quality under bad channel conditions compared to prior methods of repeating previous voice data frames. Specifically, the soft technique of the present invention uses a weighted combination of previous and present frame parameters, where the weighting reflects the probability of error. The present invention will improve the masking of errors compared to hard actions based on a binary detection, especially when the binary detection fails or when the received frame is declared as being "good". The method can also utilize parameter based soft information without increasing the bitrate.

    Abstract translation: 在TDMA蜂窝式电话系统中,错误隐藏方法提供了基于软质量度量的参数插值,该方法将增强与不良信道条件下的语音质量相比,重复先前语音数据帧的现有方法。 具体地说,本发明的软技术使用先前和现在的帧参数的加权组合,其中加权反映错误概率。 本发明将改进与基于二进制检测的硬动作相比的错误掩蔽,特别是当二进制检测失败或者当接收到的帧被声明为“良好”时。 该方法还可以利用基于参数的软信息而不增加比特率。

    ERROR DETECTION SYSTEM
    444.
    发明申请
    ERROR DETECTION SYSTEM 审中-公开
    错误检测系统

    公开(公告)号:WO1993020620A1

    公开(公告)日:1993-10-14

    申请号:PCT/US1993002152

    申请日:1993-03-11

    Applicant: MOTOROLA INC.

    Abstract: An error detection system for discrete receiver. The error detection system indicates bad frames of binary information signal which contain distorted bits in excessive numbers so great as to cause a convolutional decoder to generate an incorrect, decoded signal. A signal decoded by a convolutional decoder (512) is re-encoded by an encoder (524), and the re-encoded signal (530) is compared with the signal received (506) by the receiver. When excessive numbers of re-encoded signal portions differ too greatly with corresponding portions of an actual, received signal, a bad frame indication is generated (590).

    Anti-fraud control system, monitoring device, and anti-fraud control method

    公开(公告)号:US11909748B2

    公开(公告)日:2024-02-20

    申请号:US17224883

    申请日:2021-04-07

    CPC classification number: H04L63/1416 H04L1/201 H04L12/44

    Abstract: In an anti-fraud control system, a first error monitoring device includes a first frame transmitting and receiving unit that receives a frame flowing on the on-board network; and a first error detector that causes transmission of an error notification frame for notifying of an occurrence of an error in the frame when detecting the occurrence of the error in the frame received by the first frame transmitting and receiving unit. Each of second error monitoring devices includes: a second frame transmitting and receiving unit that receives the error notification frame; and a second error detector that regards, as a frame to be invalidated, the frame subjected to the error and included in the received error notification frame, and shifts the second error monitoring device to an invalidation mode for invalidating reception of subsequent frames, if no error is detected in an own branch with respect to the frame.

    Artificial intelligence for fault localization and mitigation in 5G/6G

    公开(公告)号:US11784764B2

    公开(公告)日:2023-10-10

    申请号:US18309844

    申请日:2023-05-01

    CPC classification number: H04L1/201 H04L1/206

    Abstract: A key requirement for 5G and 6G networking is reliability. Message faults are inevitable, and therefore procedures are needed to identify each fault location in a message and, if possible, to rectify it. Disclosed herein are artificial intelligence AI models and procedures for mitigating faults in wireless messages by (a) evaluating the signal quality of each message element according to waveform features and modulation deviations, (b) evaluating the fault probability of each message element by seeking correlations, which may be subtle, among the various waveform measurements including polarization and frequency offset, and (c) correcting the faults according to the message type, apparent format, intent or meaning, typical previous messages of a similar type, correlations of bit patterns and symbol sequences, error-detection codes if present, and other content-based indicators uncovered during model development. Automatic, real-time fault localization and correction may save substantial time and resources while substantially enhancing messaging reliability.

    Artificial Intelligence for Fault Localization and Mitigation in 5G/6G

    公开(公告)号:US20230275710A1

    公开(公告)日:2023-08-31

    申请号:US18309844

    申请日:2023-05-01

    CPC classification number: H04L1/201 H04L1/206

    Abstract: A key requirement for 5G and 6G networking is reliability. Message faults are inevitable, and therefore procedures are needed to identify each fault location in a message and, if possible, to rectify it. Disclosed herein are artificial intelligence AI models and procedures for mitigating faults in wireless messages by (a) evaluating the signal quality of each message element according to waveform features and modulation deviations, (b) evaluating the fault probability of each message element by seeking correlations, which may be subtle, among the various waveform measurements including polarization and frequency offset, and (c) correcting the faults according to the message type, apparent format, intent or meaning, typical previous messages of a similar type, correlations of bit patterns and symbol sequences, error-detection codes if present, and other content-based indicators uncovered during model development. Automatic, real-time fault localization and correction may save substantial time and resources while substantially enhancing messaging reliability.

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