Systems and methods provide solutions for testing an 8Rx capable UE using test cases for 4Rx capable UEs. An example method establishes a connection from a first Tx source and a second Tx source to each of 8Rx antenna ports. The connection duplicates a fading channel from both the first Tx source and the second Tx source to each of the eight Rx antenna ports, and adds independent noise for each of the 8Rx antenna ports. One test scenario uses 4Rx supported RF bands by connecting four of the Rx ports with data from a system simulator, and the other four Rx ports are connected with zero input. Same requirements specified with 4Rx capable UEs are applied. Another test scenario uses 8Rx supported RF bands and applies lower dB SNR requirements than those specified for 4Rx tests.

A method and system are herein disclosed. The method includes receiving sounding reference signals (SRS) at a massive multiple-input multiple-output (MIMO) base station (BS) from a user equipment (UE), determining, at the BS, beamformers for known channels of the SRS, determining, at the BS, beamformers for unknown channels of the SRS based on the determined beamformers for the known channels, and selecting a modulation and coding scheme (MCS) based on the determined beamformers.

Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for processing communications signals using a machine-learning network are disclosed. In some implementations, pilot and data information are generated for a data signal. The data signal is generated using a modulator for orthogonal frequency-division multiplexing (OFDM) systems. The data signal is transmitted through a communications channel to obtain modified pilot and data information. The modified pilot and data information are processed using a machine-learning network. A prediction corresponding to the data signal transmitted through the communications channel is obtained from the machine-learning network. The prediction is compared to a set of ground truths and updates, based on a corresponding error term, are applied to the machine-learning network.

Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for processing communications signals using a machine-learning network are disclosed. In some implementations, pilot and data information are generated for a data signal. The data signal is generated using a modulator for orthogonal frequency-division multiplexing (OFDM) systems. The data signal is transmitted through a communications channel to obtain modified pilot and data information. The modified pilot and data information are processed using a machine-learning network. A prediction corresponding to the data signal transmitted through the communications channel is obtained from the machine-learning network. The prediction is compared to a set of ground truths and updates, based on a corresponding error term, are applied to the machine-learning network.

Vortex radiometers (VRs) for cognitive antenna applications and fade prediction techniques are disclosed. A VR may act as an early warning system for communication antennas by of measuring: (1) when a fade causing interference with or complete loss of communication signal will occur; (2) how long the fade will persist; and/or (3) how intense the fade will be. This may be accomplished by measuring atmospheric noise temperature with concentric annular antenna beam patterns.

Systems and methods are disclosed for providing at least one report relating to a wireless communications spectrum. At least one device is operable for wideband scan; to detect and measure at least one signal transmitted from at least one signal emitting device autonomously, thereby creating signal data; to analyze the signal data in near real-time, thereby creating analyzed data; generate the at least one report in near real-time; and to communicate at least a portion of the at least one report over a network to at least one remote device.

A test system for testing an antenna of user equipment includes a channel emulator, an anechoic chamber, a base station emulator and a test control computer. The channel emulator supports bi-directional channel emulation and emulates wireless channel conditions with fading and without fading. The anechoic chamber is connected to the channel emulator and contains probes as well as user equipment as a device under test. The base station emulator is connected to the channel emulator and performs protocol testing with the device under test via the channel emulator without fading. The test control computer controls the other element of the test system along with a base station that is connected to the channel emulator and that performs performance testing with the device under test via the channel emulator with fading after the protocol testing is performed without fading.

The present invention discloses a base station test system and method based on 3D Massive MIMO and a storage medium, and relates to the technical field of multi-antenna beamforming test in a wireless communication system. Each channel in a phase shift amplitude modulation network can implement the amplitude control and phase control of the channel. The scheme provided by the present invention is to perform air interface channel simulation based on the phase shift amplitude modulation network to satisfy the Massive Multiple Input Multiple Output beamforming/beam tracking (phase synthesis) test verification of a wireless communication base station system.

In one embodiment, a method is provided. The method comprises determining a free space path loss distance at a frequency of a transmitter; determining a morphology class for a geographic location of the transmitter; determining a scaling factor P corresponding to the determined morphology class; determining a circular analysis region based upon the scaling factor P; and generating a contour.

The invention relates to a method and system for authenticating a user, wherein an application transmits a query comprising identification data to an authentication service, the authentication service determines the address of a mobile terminal linked to the user on the basis of the identification data and transmits a request comprising a transaction identifier to the mobile terminal, the mobile terminal performs an enquiry for input of a biometric security feature, grants access to a private key saved on the mobile terminal upon input of a valid security feature, signs the transaction identifier using the private key and transmits the signed transaction identifier back to the authentication service, and the authentication service verifies the signature of the signed transaction identifier and, in the case of the presence of an authentic signature, transmits a confirmation of the query back to the application.