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公开(公告)号：US20240264299A1
公开(公告)日：2024-08-08
申请号：US18417036
申请日：2024-01-19
Inventor： Ahmed ALKHATEEB , Shuaifeng JIANG
CPC classification number: G01S13/584 , G01S7/354 , G01S7/356
Abstract: A computer system is disclosed that is configured to perform a method that includes receiving one or more radar data frames from one or more antennas of a base station or a user equipment device in an environment; processing the one or more radar data frames to identify one or more attributes of one or more static objects and one or more dynamic objects in the environment; and estimating one or more channels for the user equipment device and the base station based on the one or more attributes of the one or more static objects and the one or more dynamic objects.
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Patent Agency Ranking
公开(公告)号：US20250125852A1
公开(公告)日：2025-04-17
申请号：US18912922
申请日：2024-10-11
Inventor： Ahmed ALKHATEEB , Yu ZHANG
Abstract: A system and method for training the machine learning models of the multiple-input multiple-output (MIMO) communication systems. The system and method cluster the training data based on the device position. Then, separate models are trained based on the data for each position. The system and method can be applied to the design of machine learning-based beamforming, precoding, channel compression, channel estimation, and codebook design among other applications. The system uses the implicit or explicit user position information to select the right zone-specific model and its parameters.
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公开(公告)号：US20230352847A1
公开(公告)日：2023-11-02
申请号：US18342181
申请日：2023-06-27
Inventor： Ahmed ALKHATEEB , Abdelrahman TAHA , Muhammed ALRABEIAH
IPC: H01Q15/14
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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公开(公告)号：US20230123472A1
公开(公告)日：2023-04-20
申请号：US17906198
申请日：2021-03-15
Inventor： Ahmed ALKHATEEB , Muhammed ALRABEIAH , Andrew HREDZAK
Abstract: Vision-aided wireless communications systems are provided. Embodiments disclosed herein leverage visual data sensors (such as red-blue-green (RGB)/depth cameras) to adapt communications (e.g., predict beamforming directions) in large-scale antenna arrays, such as used in millimeter wave (mmWave) and massive multiple-input multiple-output (MIMO) systems. These systems face two important challenges: (i) a large training overhead associated with selecting an optimal beam and (ii) a reliability challenge due to high sensitivity of mmWave and similar signals to link blockages. Interestingly, most devices that employ mmWave antenna arrays, such as 5G phones, self-driving vehicles, and virtual/augmented reality headsets, will likely also use cameras. Therefore, an efficient olution is presented which uses cameras at base stations and/or handsets to help overcome the beam selection and blockage prediction challenges.
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公开(公告)号：US20250007579A1
公开(公告)日：2025-01-02
申请号：US18578896
申请日：2022-07-12
Inventor： Ahmed ALKHATEEB , Yu ZHANG , Muhammad ALRABEIAH
IPC: H04B7/06
Abstract: Reinforcement learning of beam codebooks for millimeter wave and terahertz multiple-input-multiple-output (MIMO) systems is provided. Millimeter wave (mmWave) and terahertz (THz) MIMO systems rely on predefined beamforming codebooks for both initial access and data transmission. These predefined codebooks, however, are commonly not optimized for specific environments, user distributions, and/or possible hardware impairments. To overcome these limitations, this disclosure develops a deep reinforcement learning framework that learns how to optimize the codebook beam patterns relying only on receive power measurements. The developed model learns how to adapt the beam patterns based on the surrounding environment, user distribution, hardware impairments, and array geometry. Further, this approach does not require any knowledge about the channel, radio frequency (RF) hardware, or user positions.
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公开(公告)号：US20230198605A1
公开(公告)日：2023-06-22
申请号：US17996264
申请日：2021-06-11
Inventor： Ahmed ALKHATEEB , Umut DEMIRHAN , Xiaoyan YING
CPC classification number: H04B7/15507 , H04B7/0617 , H01Q15/14 , H01Q19/104 , H01Q19/18
Abstract: Relay-aided intelligent reconfigurable surfaces (IRSs) are provided. A novel relay-aided intelligent surface architecture is described herein that has the potential of achieving the promising gains of IRSs with a much smaller number of elements, opening the door for realizing these surfaces in practice. A half-duplex or full-duplex relay is connected to one or more IRSs. This merges the gains of relays and reconfigurable surfaces and splits the required signal-to-noise ratio (SNR) gain between them. This architecture can hen significantly reduce the required number of reconfigurable elements in the IRS(s) while achieving the same spectral efficiencies. Consequently, the proposed relay-aided intelligent surface architecture needs far less channel estimation/beam training overhead and provides enhanced robustness compared to traditional IRS solutions.
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