Systems and methods may use proximate communication to retrieve information pertaining to a target device. In one example, the method may include detecting the target device within a vicinity of a user device, receiving an information request response communication including information pertaining to the target device, and receiving an operation request response communication including information pertaining to a performed operation.

Provided is a transmitter identifying apparatus configured to receive signals from terminals, extract a feature from the received signal, learn a first feature of the signal that is known as a signal received from a terminal to be discriminated, generate a first classifier for discriminating the terminal to be discriminated, input to the first classifier a second feature of the signal that is unknown whether the signal is received from the terminal to be discriminated, calculate a likelihood distribution representing likelihood for each terminal, analyze the likelihood distribution, determine whether to apply a tentative label to the second feature, and apply the tentative label to the second feature, based on a result of the analysis.

Systems, methods, and apparatus for automatic signal detection in a radio-frequency (RF) environment are disclosed. At least one node device is in a fixed nodal network. The at least one node device is operable to measure and learn the RF environment in a predetermined period based on statistical learning techniques, thereby creating learning data. The at least one node device is operable to create a spectrum map based on the learning data. The at least one node device is operable to calculate a power distribution by frequency of the RF environment in real time or near real time, including a first derivative and a second derivative of fast Fourier transform (FFT) data of the RF environment. The at least one node device is operable to identify at least one signal based on the first derivative and the second derivative of FFT data.

Systems, methods, and apparatus for automatic signal detection in a radio-frequency (RF) environment are disclosed. At least one node device is in a fixed nodal network. The at least one node device is operable to measure and learn the RF environment in a predetermined period based on statistical learning techniques, thereby creating learning data. The at least one node device is operable to create a spectrum map based on the learning data. The at least one node device is operable to calculate a power distribution by frequency of the RF environment in real time or near real time, including a first derivative and a second derivative of fast Fourier transform (FFT) data of the RF environment. The at least one node device is operable to identify at least one signal based on the first derivative and the second derivative of FFT data.

Among network devices for use within a wireless communication network, a transmitting network device is configured to transmit to a receiving network device via a control channel one or more control signals for configuring the receiving network device for a characterization of at least one of the transmitting network device and the receiving network device and/or for a characterization of one or more communication channels between the transmitting network device and the receiving network device.

Systems, methods and apparatus are disclosed for automatic signal detection in an RF environment. An apparatus comprises at least one receiver and at least one processor coupled with at least one memory. The apparatus is at the edge of a communication network. The apparatus sweeps and learns the RF environment in a predetermined period based on statistical learning techniques, thereby creating learning data. The apparatus forms a knowledge map based on the learning data, scrubs a real-time spectral sweep against the knowledge map, and creates impressions on the RF environment based on a machine learning algorithm. The apparatus is operable to detect at least one signal in the RF environment.

Systems, methods and apparatus are disclosed for automatic signal detection in an RF environment. An apparatus comprises at least one receiver and at least one processor coupled with at least one memory. The apparatus is at the edge of a communication network. The apparatus sweeps and learns the RF environment in a predetermined period based on statistical learning techniques, thereby creating learning data. The apparatus forms a knowledge map based on the learning data, scrubs a real-time spectral sweep against the knowledge map, and creates impressions on the RF environment based on a machine learning algorithm. The apparatus is operable to detect at least one signal in the RF environment.

Systems, methods, and devices enable spectrum management by identifying, classifying, and cataloging signals of interest based on radio frequency measurements. In an embodiment, signals and the parameters of the signals may be identified and indications of available frequencies may be presented to a user. In another embodiment, the protocols of signals may also be identified. In a further embodiment, the modulation of signals, data types carried by the signals, and estimated signal origins may be identified.

Systems, methods, and devices enable spectrum management by identifying, classifying, and cataloging signals of interest based on radio frequency measurements. In an embodiment, signals and the parameters of the signals may be identified and indications of available frequencies may be presented to a user. In another embodiment, the protocols of signals may also be identified. In a further embodiment, the modulation of signals, data types carried by the signals, and estimated signal origins may be identified.

A method and system for performing Maximum Likelihood Detector (MLD) preprocessing in a Multiple-Input Multiple-Output (MIMO) communication system, the method including, obtaining a received signal Y a corresponding channel matrix H and a vector of noise samples n; calculating a whitening filter L.sup.−H; whitening a channel matrix H; selecting one of a first calculation or a second calculation, based on estimated complexity of the calculations; and performing preprocessing of the received signal using the selected calculation. The first calculation includes: whitening the received signal and performing a Cordic based QR decomposition to the whitened channel matrix {tilde over (H)} and the whitened received signal {tilde over (Y)} to obtain triangular matrix R and Y=Q.sup.HL.sup.−HY. The second calculation includes: performing a Cordic based QR decomposition to the whitened channel matrix {tilde over (H)} and the whitening filter L.sup.−H to obtain triangular matrix R and Q.sup.HL.sup.−H, and multiplying the received signal Y by Q.sup.HL.sup.−H to obtain Y=Q.sup.HL.sup.−HY.