The present disclosure relates 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). The present disclosure may be applied to intelligent services based on the 5G communication technology and the IoT-related technology, such as smart home, smart building, smart city, smart car, connected car, health care, digital education, smart retail, security and safety services. A communication environment analysis method according to the present invention comprises the steps of: receiving satellite information and image information of a certain area; identifying area information of an object that is not contained in the image information, on the basis of the satellite information; determining characteristic information of the object; and analyzing a communication environment of the certain area on the basis of the characteristic information, wherein the object is one that causes signal attenuation due to at least one of signal scattering and signal absorption.

The present disclosure relates 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). The present disclosure may be applied to intelligent services based on the 5G communication technology and the IoT-related technology, such as smart home, smart building, smart city, smart car, connected car, health care, digital education, smart retail, security and safety services. A communication environment analysis method according to the present invention comprises the steps of: receiving satellite information and image information of a certain area; identifying area information of an object that is not contained in the image information, on the basis of the satellite information; determining characteristic information of the object; and analyzing a communication environment of the certain area on the basis of the characteristic information, wherein the object is one that causes signal attenuation due to at least one of signal scattering and signal absorption.

A system for load-testing a cloud radio access network (C-RAN) is provided. The system includes at least one radio point (RP), each being configured to exchange radio frequency (RF) signals with at least one user equipment (UE). The system also includes a baseband controller communicatively coupled to the at least one RP via a front-haul ETHERNET network. The front-haul ETHERNET network includes at least one switch; and a testing device that is time-synchronized to the baseband controller and the at least one RP. The testing device is configured to receive at least one packet from each of the at least one RP. The testing device is also configured to replicate each of at least some of the received packets to produce a respective replicated packet. The testing device is also configured to transmit at least one replicated packet to the baseband controller.

During operation, an electronic device may emulate client functionality associated with a virtual client in a wireless network, where emulating the client functionality includes generating a first frame that is compatible with a wireless communication protocol and is associated with fictious wireless communication with the virtual client. Then, the electronic device may provide, to a computer, a second frame that includes at least a portion of the first frame, where the second frame is compatible with a wired communication protocol. Next, the electronic device may receive, from the computer, a response message based at least in part on the first frame, where the response message includes information associated with a service provided by the computer. Moreover, the electronic device may assess the service based at least in part on the information and may selectively perform the remedial action based at least in part on the assessment.

During operation, an electronic device may emulate client functionality associated with a virtual client in a wireless network, where emulating the client functionality includes generating a first frame that is compatible with a wireless communication protocol and is associated with fictious wireless communication with the virtual client. Then, the electronic device may provide, to a computer, a second frame that includes at least a portion of the first frame, where the second frame is compatible with a wired communication protocol. Next, the electronic device may receive, from the computer, a response message based at least in part on the first frame, where the response message includes information associated with a service provided by the computer. Moreover, the electronic device may assess the service based at least in part on the information and may selectively perform the remedial action based at least in part on the assessment.

A method for wireless communication by a receiving device, includes receiving, from a transmitting device, a latent representation of a channel sequence for a wireless signal. A decoder applies a physical propagation channel model to the latent representation to reconstruct the channel sequence for the wireless signal.

A method for wireless communication by a receiving device, includes receiving, from a transmitting device, a latent representation of a channel sequence for a wireless signal. A decoder applies a physical propagation channel model to the latent representation to reconstruct the channel sequence for the wireless signal.

One or more computing devices, systems, and/or methods for system parameter identification and network component configuration are provided. A state comprising a system parameter combination, a traffic model, and a channel assignment may be generated. A network traffic scenario is executed through a virtualized testbed using the state. A reward for the system parameter combination may be generated based upon key performance indicators output by the network traffic scenario. A reward policy and rewards generated for system parameter combinations are used to select a system parameter combination that is used to configure a network component of a communication network.

An example method for estimating the angle-of-arrival (AoA) and other parameters of radio frequency (RF) signals that are received by an antenna array comprises: receiving a plurality of radio frequency (RF) signal power measurements by a plurality of antenna elements at a plurality of RF channels; computing, by applying a machine learning model to the plurality of RF signal power measurements, an estimated RF signal parameter value; and outputting the RF signal parameter value.

Implementations relate to selection of a physics-specific model for determination of characteristics of radio frequency signal propagation. In some implementations, a method includes receiving a plurality of first propagation characteristics of a radio frequency (RF) signal, determining a feature vector based on the first propagation characteristics, inputting the feature vector to a machine-learning meta-model, and executing the machine learning meta-model to select a particular physics-specific model from multiple physics-specific models, where each of the physics-specific models is for a different RF signal propagation environment. The feature vector is input to the particular physics-specific model, and the particular physics-specific model is executed to output an estimate of one or more second propagation characteristics of the RF signal based on the feature vector.