Abstract: Certain aspects of the present disclosure provide techniques and apparatus for identifying beamforming parameters to use in processing received signaling using a neural network. An example method generally includes receiving a first plurality of signals on a wireless communication channel. A first plurality of beamformed signals is generated by generating, for each respective signal of the first plurality of signals, a respective beamformed signal using beamforming parameters selected from a configured set of beamforming parameters. First predicted beamforming parameters are generated using a neural network and the first plurality of beamformed signals. A second signal is received on the wireless communication channel. The second signal is beamformed using beamforming parameters selected from a group of beamforming parameters including the configured set of beamforming parameters and the first predicted beamforming parameters.

Abstract: Certain aspects of the present disclosure provide techniques and apparatus for identifying beamforming parameters to use in processing received signaling using a neural network. An example method generally includes receiving a first plurality of signals on a wireless communication channel. A first plurality of beamformed signals is generated by generating, for each respective signal of the first plurality of signals, a respective beamformed signal using beamforming parameters selected from a configured set of beamforming parameters. First predicted beamforming parameters are generated using a neural network and the first plurality of beamformed signals. A second signal is received on the wireless communication channel. The second signal is beamformed using beamforming parameters selected from a group of beamforming parameters including the configured set of beamforming parameters and the first predicted beamforming parameters.

Abstract: Certain implementations of the disclosed technology may include methods and computing systems for memory-assisted compression of network packets using packet-level clustering. According to an example implementation, a method is provided. The method may include vectorizing a plurality of data packets stored in a memory, calculating respective distances between each of the respective vectorized data packets, clustering the plurality of data packets into a plurality of data packet clusters, obtaining a sample data packet to be compressed, identifying a training data packet cluster from among the plurality of data packet clusters, and compressing the sample data packet using a compression algorithm.

Abstract: Certain implementations of the disclosed technology may include methods and computing systems for memory-assisted compression of network packets using packet-level clustering. According to an example implementation, a method is provided. The method may include vectorizing a plurality of data packets stored in a memory, calculating respective distances between each of the respective vectorized data packets, clustering the plurality of data packets into a plurality of data packet clusters, obtaining a sample data packet to be compressed, identifying a training data packet cluster from among the plurality of data packet clusters, and compressing the sample data packet using a compression algorithm.