GNSS Driven Dynamic Partitioning of APs

    公开(公告)号:US20250093530A1

    公开(公告)日:2025-03-20

    申请号:US18467040

    申请日:2023-09-14

    Abstract: Described herein are devices, systems, methods, and processes for managing the computational complexity in geolocating a large number of network devices (e.g., access points (APs)) in indoor environments. A number of network devices may be partitioned into smaller groups or batches based on neighbor knowledge about the network devices. Each batch of network devices can include just devices located on a same floor, or may include devices located across different floors. Every batch may include at least one anchor network device. The geolocation of the network devices can be determined, batch-by-batch, based on fusing global navigation satellite system (GNSS) pseudorange measurements and inter-network device ranging measurements. The geolocation accuracy for each partition can be evaluated utilizing such metrics as the average residual error. If the error for a batch is greater than a threshold, remedial measures may be taken to reduce the error and improve the geolocation accuracy.

    SIGNAL TO INTERFERENCE AND NOISE RATIO ESTIMATION

    公开(公告)号:US20240275505A1

    公开(公告)日:2024-08-15

    申请号:US18168754

    申请日:2023-02-14

    CPC classification number: H04B17/336

    Abstract: Signal to Interference and Noise Ratio (SINR) estimation, and more specifically providing SINR estimation during Legacy Long Training Field (LLTF) accounting for Inter Symbol Interference (ISI) may be provided. SINR estimation may include receiving a Physical Layer Protocol Data Unit (PPDU) including a LLTF and extracting groups of transmission symbols from the LLTF. Next, groups of fragmented symbols may the groups of transmission symbols. One or more Sum of the Squared Magnitudes (SSM) may be determined, such as an SSM of the total signal, an SSM of the signal without ISI and/or noise, an SSM of ISI, an SSM of noise. Finally, SINR may be estimated using one or more SSMs (e.g., the SSM of noise, the SSM of signal, and the SSM of ISI).

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