Use of high frequency waves, such a millimeter waves, in many circumstances is prevented due to their inability to diffract around common obstacles. Disclosed herein is a system and method for transforming an incident high frequency wave. The system includes meta-atom pairs that define a surface. The meta-atom pairs generate an electro-magnetic response by interacting with an incident wave. This electro-magnetic response can be modulated by applying voltage to the meta-atom pairs. The electro-magnetic response transforms the incident wave into an emitted wave based on its controlled properties. The system and method are able to, by changing the voltage applied, steer the emitted wave a full 360 degrees as wells as transmit it through the surface without significant power loss. Embodiments enable the transmission through or around many obstacles that would normally interfere with high frequency waves.

One or more processors control processing of radio frequency (RF) signals using a machine-learning network. The one or more processors receive as input, to a radio communications apparatus, a first representation of an RF signal, which is processed using one or more radio stages, providing a second representation of the RF signal. Observations about, and metrics of, the second representation of the RF signal are obtained. Past observations and metrics are accessed from storage. Using the observations, metrics and past observations and metrics, parameters of a machine-learning network, which implements policies to process RF signals, are adjusted by controlling the radio stages. In response to the adjustments, actions performed by one or more controllers of the radio stages are updated. A representation of a subsequent input RF signal is processed using the radio stages that are controlled based on actions including the updated one or more actions.

Systems, methods, and devices to reduce the channel estimation overhead by collecting data from many UEs and building a location-based mathematical model are disclosed. During building of the model, a reference signal is used to collect location- and signal-related data from connected UEs. Once the model is successfully built, it is then transmitted and/or downloaded to each new UE that connects to the base station. The UEs and/or the base stations then use this model to determine their own transmission parameter values. The UEs also report their location to the base stations, which use the model to estimate channel conditions and adapt transmission parameters for themselves.

One or more computing devices, systems, and/or methods are provided. In an example, a method includes instantiating a first user equipment (UE) module on a first computing device and instantiating a second UE module on a second computing device connected to the first computing device by a wired network. A first base station (BS) module associated with the first UE module is instantiated on a third computing device coupled to the wired network and a second BS module associated with the second UE module is instantiated on a fourth computing device coupled to the wired network. A first resource allocation map is sent from the first BS module to the second BS module. An interference metric is generated based on the first resource allocation map. A data transmission between the second UE module and the second BS module is modulated based on the interference metric.

A method for identifying radio signal transmission characteristics in a wireless communication system and an apparatus therefor are provided. The method may include identifying a signal transmission site, identifying a signal reception site, finding an area where a tree is present between the signal transmission site and the signal reception site, checking characteristics of the crown of the tree and characteristics of the trunk of the tree, and examining transmission characteristics of a radio signal sent from the signal transmission site to the signal reception site on the basis of the characteristics of the crown and the trunk. The method and apparatus relate to a communication method and system for converging a 5th-Generation (5G) communication system for supporting higher data rates beyond a 4th-Generation (4G) system with a technology for internet of things (IoT), and may be applied to intelligent services based on the 5G communication and the IoT-related technologies.

Systems, methods, and devices for wireless communication that support mechanisms signaling non-linearities to a victim user equipment (UE) for interference cancellation in a wireless communication system. A victim UE may experience interference from an uplink transmission by an aggressor UE to a base station. The victim UE may obtain a power amplifier model associated with the aggressor UE. The power amplifier may include a non-linearity model of a power amplifier of the aggressor UE causing the interference on the victim UE. The power amplifier model obtained by the victim UE may be dependent on various parameters, such as a transmit power used by the aggressor UE to transmit the uplink transmission causing the interference. Based on the aggressor UE transmit power, the victim UE may select parameters for power amplifier model and may estimate the interference caused by the uplink transmission for interference cancellation.

A method and/or system may receive a test application to be tested on a wireless communication network. The method and/or system may obtain a plurality test designs for the test application. The method and/or system may define a plurality of slices of the wireless communication network to test the test application. Each of the plurality of slices of the wireless communication network may be based on one of the plurality of test designs. The method and/or system may deploy a plurality of test wireless network services in the plurality of slices of the wireless communication network to test the test application in parallel. By testing a plurality of slices of the wireless communication network, the testing may be more comprehensive, as a diversity of network circumstances can be verified in an effective, efficient, and practical manner.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training and deploying machine-learned communication. One of the methods includes: receiving an RF signal at a signal processing system for training a machine-learning network; providing the RF signal through the machine-learning network; producing an output from the machine-learning network; measuring a distance metric between the signal processing model output and a reference model output; determining modifications to the machine-learning network to reduce the distance metric between the output and the reference model output; and in response to reducing the distance metric to a value that is less than or equal to a threshold value, determining a score of the trained machine-learning network using one or more other RF signals and one or more other corresponding reference model outputs, the score indicating an a performance metric of the trained machine-learning network to perform the desired RF function.

Systems and methods for rebroadcasting a target signal stream. A system includes a donor antenna circuit; a processor coupled to the donor antenna circuit. The processor may be configured to: receive a signal representing a request for rebroadcasting a target signal stream to a local region, the target signal stream associated with prioritization of a first signal type relative to at least one co-existing signal type; determining a target position to orient the donor antenna circuit relative to a target source providing the target signal stream, the target position based on a positioning model defined by a set of feature scores corresponding to one or more signal metrics, wherein the target position is determined based on an optimized combination of feature scores for a subset of signal metrics associated with prioritizing the first signal type; and transmitting a signal to orient the donor antenna circuit relative to the target source.

An air to ground (ATG) communication system testing platform may be configured to operably couple a base station to an aircraft base radio in a lab environment. The testing platform may include a position simulator and a channel simulator. The position simulator may be configured to generate simulated aircraft position information and communicate the simulated aircraft position information to an aircraft base radio and a base band unit of the base station. The channel simulator may operably couple a remote radio head of the base station to the aircraft base radio, and may be configured to emulate channel conditions with respect to transmission of signaling generated by the remote radio head for communication to the aircraft base radio based on the emulated channel conditions.