Massive MIMO systems with wireless fronthaul

    公开(公告)号:US11742902B2

    公开(公告)日:2023-08-29

    申请号:US17547675

    申请日:2021-12-10

    CPC classification number: H04B7/0452

    Abstract: A communications network system is disclosed. The system may include a central processing unit (CPU) in data communication with a first access point (AP) configured to enable a data communication between the CPU and a first user equipment (UE). The CPU may include a processor configured to select a first group of APs including the first AP, establish a first data communications link over a first frequency band between the CPU and the first AP, cause the first AP to establish a second data communications link over a second frequency band between the first AP and the first UE, and transmit a portion of data to the first AP over the first data communications link. The first data communications link may be a wireless data communications link. The first frequency band may include higher frequency levels than those of the second frequency band.

    MASSIVE MIMO SYSTEMS WITH WIRELESS FRONTHAUL

    公开(公告)号:US20230299817A1

    公开(公告)日:2023-09-21

    申请号:US18324589

    申请日:2023-05-26

    CPC classification number: H04B7/0452

    Abstract: A communications network system is disclosed. The system may include a central processing unit (CPU) in data communication with a first access point (AP) configured to enable a data communication between the CPU and a first user equipment (UE). The CPU may include a processor configured to select a first group of APs including the first AP, establish a first data communications link over a first frequency band between the CPU- and the first AP, cause the first AP to establish a second data communications link over a second frequency band between the first AP and the first UE, and transmit a portion of data to the first AP over the first data communications link. The first data communications link may be a wireless data communications link. The first frequency band may include higher frequency levels than those of the second frequency band.

    WIRELESS TRANSMITTER IDENTIFICATION IN VISUAL SCENES

    公开(公告)号:US20230031124A1

    公开(公告)日:2023-02-02

    申请号:US17863567

    申请日:2022-07-13

    Abstract: Wireless transmitter identification in visual scenes is provided. This technology enables important wireless communications and sensing applications such as (i) fast beam/blockage prediction in fifth generation (5G)/sixth generation (6G) systems using camera data, (ii) identifying cars and people in a surveillance camera feed using joint visual and wireless data processing, and (iii) enabling efficient autonomous vehicle communication relying on both the camera and wireless data. This is done by developing multimodal machine learning based frameworks that use the sensory data obtained by visual and wireless sensors. More specifically, given some visual data, an algorithm needs to perform the following: (i) predict whether an object responsible for a received radio signal is present or not, (ii) if it is present, detect which object it is out of the candidate transmitters, and (iii) predict what type of signal the detected object is transmitting.

    MASSIVE MIMO SYSTEMS WITH WIRELESS FRONTHAUL

    公开(公告)号:US20240396597A1

    公开(公告)日:2024-11-28

    申请号:US18783840

    申请日:2024-07-25

    Abstract: A communications network system including antenna elements and a processor coupled with the antenna elements, the processor executing instructions to perform operations including establishing a first data communication link over a first frequency band between the CPU and the first AP of a first group of APs, causing the first AP to establish a second data communications link over a second frequency band between the first AP and a first UE, transmitting, via beamforming by the antenna elements, data to the first AP over the first data communications link, the data configured to be relayed via the first AP to the first UE over the second data communications link, obtaining an end-to-end data rate of the data communication between the CPU and the first UE, and achieving a higher end-to-end data rate than the obtained end-to-end data rate by adjusting beamforming vectors.

    Massive MIMO systems with wireless fronthaul

    公开(公告)号:US12068813B2

    公开(公告)日:2024-08-20

    申请号:US18324589

    申请日:2023-05-26

    CPC classification number: H04B7/0452

    Abstract: A communications network system is disclosed. The system may include a central processing unit (CPU) in data communication with a first access point (AP) configured to enable a data communication between the CPU and a first user equipment (UE). The CPU may include a processor configured to select a first group of APs including the first AP, establish a first data communications link over a first frequency band between the CPU- and the first AP, cause the first AP to establish a second data communications link over a second frequency band between the first AP and the first UE, and transmit a portion of data to the first AP over the first data communications link. The first data communications link may be a wireless data communications link. The first frequency band may include higher frequency levels than those of the second frequency band.

    Large intelligent surfaces with sparse channel sensors

    公开(公告)号:US11728571B2

    公开(公告)日:2023-08-15

    申请号:US16901899

    申请日:2020-06-15

    CPC classification number: H01Q15/148

    Abstract: Large intelligent surfaces (LISs) with sparse channel sensors are provided. Embodiments described herein provide efficient solutions for these problems by leveraging tools from compressive sensing and deep learning. Consequently, an LIS architecture based on sparse channel sensors is provided where all LIS elements are passive reconfigurable elements except for a few elements that are active (e.g., connected to baseband). Two solutions are developed that design LIS reflection matrices with negligible training overhead. First, compressive sensing tools are leveraged to construct channels at all the LIS elements from the channels seen only at the active elements. These full channels can then be used to design the LIS reflection matrices with no training overhead. Second, a deep learning-based solution is deployed where the LIS learns how to optimally interact with the incident signal given the channels at the active elements, which represent the current state of the environment and transmitter/receiver locations.

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