AI-Based Message Interpretation and Fault Mitigation in 5G and 6G

    公开(公告)号:US20240356672A1

    公开(公告)日:2024-10-24

    申请号:US18759843

    申请日:2024-06-29

    CPC classification number: H04L1/0046 H04L1/0061 H04L1/0064

    Abstract: Message faulting is expected to be a major challenge in 5G-Advanced and especially 6G, due to increased pathloss and phase noise at FR2 frequencies, and exponential crowding of networks. Legacy methods for forward-correction or automatic retransmissions are unsuitable to the fast-paced demands of next-generation users. Therefore, disclosed herein is an AI-based receiver that interprets a corrupted message to determine the most likely meaning or intent, and thereby provides one or more candidate corrected messages along with a likelihood that each of the candidate corrected messages is indeed correct. The AI model may also be provided with data on the context or current activity of the receiver, data on the waveform of each message element, and other data available to the receiver, so that the AI model can further refine the likelihood values. By recovering corrupted messages in the receiver, a costly retransmission may be avoided, saving time and resource usage.

    Data decoding method and device in communication and broadcast system

    公开(公告)号:US12074704B2

    公开(公告)日:2024-08-27

    申请号:US18080202

    申请日:2022-12-13

    CPC classification number: H04L1/0047 H04L1/0046 H04L1/0061

    Abstract: The present disclosure relates to a communication method and system for converging a 5th-Generation (5G) communication system for supporting higher data rates beyond a 4th-Generation (4G) system with a technology for Internet of Things (IoT). The present disclosure may be applied to intelligent services based on the 5G communication technology and the IoT-related technology, such as smart home, smart building, smart city, smart car, connected car, health care, digital education, smart retail, security and safety services. Further, the present disclosure relates to decoding of a turbo code in a communication system including long term evolution (LTE), and to efficiently implement a method, procedure, and device for receiving and decoding a signal transmitted in a mobile communication system.

    RESOURCE CONFIGURATION FOR EPDCCH
    6.
    发明公开

    公开(公告)号:US20240215041A1

    公开(公告)日:2024-06-27

    申请号:US18594708

    申请日:2024-03-04

    CPC classification number: H04W72/23 H04L1/0046 H04W28/26 H04W72/00

    Abstract: A system and method for providing both localized and distributed transmission modes for EPDCCH is disclosed, where one EPDCCH comprises of one or multiple CCEs. Localized versus distributed transmission may be defined in terms of the EPDCCH to CCE resource mapping. In a localized transmission CCEs are restricted to be contained within one PRB. In a distributed transmission a CCE spans over multiple PRBs. A UE can be configured to either receive the EPDCCH only in localized or only in distributed transmissions. A UE can also be configured to expect EPDCCH transmissions in both localized and distributed transmissions. In each PRB configured by the higher layer as an EPDCCH resource, 24 REs that may be used for any DMRS transmission are always reserved and not used for EPDCCH transmission.

    AI Model with Error-Detection Code for Fault Correction in 5G/6G

    公开(公告)号:US20240063942A1

    公开(公告)日:2024-02-22

    申请号:US18502054

    申请日:2023-11-05

    CPC classification number: H04L1/0046 H04L1/0061 H04L1/0064

    Abstract: Message faulting is a critical unsolved problem for 5G and 6G. Disclosed herein is a method for combining an AI-based analysis of the waveform data of each message element, plus the constraint of an associated error-detection code (such as a CRC or parity construct of the correct message) to localize and, in many cases, correct a limited number of faults per message, without a retransmission. For example, the waveform data may include a deviation of the amplitude or phase of a particular message element, relative to an average of the amplitudes or phases of the other message elements that have the same demodulation value. The outliers are thereby exposed as the most likely faulted message elements. In addition, using the error-detection code, the AI model can determine the most likely corrected message, thereby avoiding retransmission delays and power usage and other costs.

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