Liberty Latin America has been granted a patent for innovative physical layer processing methods that enhance wireless network discovery and synchronization. The methods utilize spatial processing algorithms, including spatial matched filters and adaptive beamformers, to improve performance in challenging radio frequency environments. GlobalData’s report on Liberty Latin America gives a 360-degree view of the company including its patenting strategy. Buy the report here.

According to GlobalData’s company profile on Liberty Latin America, was a key innovation area identified from patents. Liberty Latin America's grant share as of June 2024 was 75%. Grant share is based on the ratio of number of grants to total number of patents.

Wireless network discovery and synchronization using spatial processing

Source: United States Patent and Trademark Office (USPTO). Credit: Liberty Latin America Ltd

The patent US12040921B1 outlines a method and system for detecting synchronization signals in a communication system using advanced signal processing techniques. The method involves searching for a synchronization signal by applying a spatial matched filter, which incorporates a spatial whitener, to signals received by multiple antennas. This process is complemented by the use of multiple adaptive beamformers that detect various signal parameters, including carrier frequency and timing offsets, from the synchronization signal. The method can be executed at a remote node within the communication system, which receives synchronization signals transmitted from a base station.

The system described in the patent consists of a plurality of antennas connected to a spatial matched filter and multiple adaptive beamformers. The spatial matched filter is designed to identify synchronization signals and includes a matched filter bank for enhanced processing. Each adaptive beamformer is configured to detect specific parameters of the synchronization signal, such as primary and secondary identifiers, by utilizing respective inverse spatial covariance matrices and cross-correlation vectors. The system is capable of generating training sequences to calculate detection statistics, which are then used to determine the signal parameters effectively. This innovative approach aims to improve the reliability and efficiency of synchronization in communication networks.

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