Abstract: An artificial neural network (ANN) is configured to convert input data bits to a corresponding discrete-time Orthogonal Frequency Division Multiplexing (OFDM) sequence. The ANN combines multiple baseband signal-processing operations, including bits-to-symbol mapping and OFDM modulation. Training tunes the ANN to provide the discrete-time OFDM signal with at least one of a low multiple input multiple output (MIMO) condition number, a predetermined number of eigenvalues above a threshold value, low peak-to-average-power ratio (PAPR), a low bit error probability, or a high bandwidth efficiency.

Abstract: Systems, methods, and apparatuses for efficiently transmitting wireless communication signals are provided. An artificial neural network (ANN) is configured to take in an input data symbol sequence and generate a corresponding discrete-time Orthogonal Frequency Division Multiplexing (OFDM) sequence therefrom. The ANN combines multiple baseband signal-processing operations, one of which includes OFDM modulation. Training data tunes the ANN to provide the discrete-time OFDM signal with at least one of a low multiple input multiple output (MIMO) condition number, a predetermined number of eigenvalues above a threshold value, low peak-to-average-power ratio (PAPR), a low bit error probability, or a high bandwidth efficiency.

Abstract: An unmanned aerial vehicle (UAV) uses a first baseband processor to establish a first communication link with a ground station of a wireless network and a second baseband processor that establishes a second communication link with a user device. Communication data is routed between the user device and the core network through the first communication link and the second communication link. The UAV receives control commands from a ground-based UAV controller that direct the UAV to travel according to a flight trajectory that is proximate to the user device.

Abstract: An unmanned aerial vehicle (UAV) uses a first baseband processor to establish a first communication link with a ground station of a wireless network and a second baseband processor that establishes a second communication link with a user device. Communication data is routed between the user device and the core network through the first communication link and the second communication link. The UAV receives control commands from a ground-based UAV controller that direct the UAV to travel according to a flight trajectory that is proximate to the user device.

Abstract: A computer-implementable method employs network signal metadata to train a cognitive learning and inference system to produce an inferred function, wherein the metadata comprises a syntactic structure of at least one network communication protocol. The inferred function is used to map metadata of a detected network signal to a cognitive profile of a transmitter of the detected network signal. The mapping effects intelligent discrimination of the transmitter from at least one other transmitter through corroborative or negating evidentiary observation of properties associated with the metadata of the detected network signal. Information content in the network signal can be determined from the inferred function or the cognitive profile without demodulating the network signal.

Abstract: A transmitter in a wireless communication network includes a bits-to-symbol mapper that produces a plurality of data symbols; and a waveform modulator that receives a first discrete-time waveform and at least a second discrete-time waveform that comprises a cyclic shift of the first discrete-time waveform; modulates a first one of the plurality of data symbols onto the first discrete-time waveform and modulates a second one of the plurality of second data symbol onto the second discrete-time waveform, to produce a plurality of modulated discrete-time waveforms; and sums the plurality of modulated discrete-time waveforms to produce a modulated discrete-time signal to be transmitted in the network. The first discrete-time waveform and at least the second discrete-time waveform are multicarrier signals.

Abstract: A Multiple Input Multiple Output (MIMO) system in a radio access network (RAN) comprises a virtualized baseband unit, an antenna array; and a fronthaul network that connects the virtualized baseband unit to the antenna array. For a first set of antennas in the antenna array, the virtualized baseband unit generates a first MIMO matrix from channel state information (CSI); computes a sparse update matrix corresponding to an update to the first set of antennas; sums the sparse update matrix with the first MIMO matrix to produce a second MIMO matrix corresponding to a second set of antennas in the antenna array; determines which of the MIMO matrices results in a better CSI, MIMO condition number, peak-to-average-power ratio, or quality metric of spatial subchannels; and based on the determination, selects the first set of antennas or the second set of antennas to transmit or receive signals in the RAN.

Abstract: A wireless user equipment (UE) device is configured to communicate with another UE via device-to-device (D2D) communications. The UE transmits a communication to the other UE, wherein the communication indicates scheduling of a shared spectrum resource. The shared spectrum resource is shared by a first network management operator (NMO) and a second NMO, wherein both the UE and the other UE are associated with the first NMO. The first NMO employs a first set of spatial channels in the shared spectrum resource, and a second set of spatial channels in the shared spectrum resource is made available for use by the second NMO, the second set being different from the first set. The UE communicates with the other UE over the first set of spatial channels.

Abstract: Systems, methods, and apparatuses for analyzing and synthesizing wireless communication signals are provided. A receiver might transform a received signal into a basis in which the transformed signal is sparse, which can reduce the complexity of joint detection by facilitating message passing algorithm (MPA) decoding. A transmitter might employ dense codewords in a first basis, which may facilitate certain signal-processing operations and may provide a transmission with a low peak-to-average-power ratio. The codewords can be designed to be sparse when transformed to a second basis. The codewords may be configured for non-orthogonal multiple access.

Abstract: A base station or content delivery server in a wireless network is configured to receive wireless network topology information from each of a plurality of wireless user devices. The wireless network topology information indicates which wireless user devices are within radio communication range of each other. The base station or content delivery server is configured to respond to a request for content from a first wireless user device by using the wireless network topology information for selecting a second wireless user device that has the content and is within radio communication range of the first wireless user device. The base station or content delivery server can be configured to forward the request to the second wireless user device, send a resource identifier to the first wireless user device that identifies the second wireless user device, and/or transmit a radio resource allocation to the first wireless user device to create a peer-to-peer link with the second wireless user device.

Abstract: An Orthogonal Frequency Division Multiplexing (OFDM) transmitter generates OFDM multiple-access signals with low Peak-to-Average-Power Ratio (PAPR). A code-division multiplexer arranges original data symbols from different data streams inside each length-N symbol block, which is spread by a Discrete Fourier Transform (DFT) spreader. The arrangement of the original data symbols configures the DFT spreader to spread each original data symbol into a predetermined spread-DFT code division multiple access channel. The resulting DFT-spread data symbols are mapped to OFDM subcarriers, and an inverse discrete Fourier transform (IDFT) operates on the DFT-spread data symbols to generate an OFDM transmission signal having low PAPR.

Abstract: A computer-implementable method employs network signal metadata to train a cognitive learning and inference system to produce an inferred function, wherein the metadata comprises a syntactic structure of at least one network communication protocol. The inferred function is used to map metadata of a detected network signal to a cognitive profile of a transmitter of the detected network signal. The mapping effects intelligent discrimination of the transmitter from at least one other transmitter through corroborative or negating evidentiary observation of properties associated with the metadata of the detected network signal. Information content in the network signal can be determined from the inferred function or the cognitive profile without demodulating the network signal.

Abstract: An unmanned aerial vehicle (UAV) uses a first baseband processor to establish a first communication link with a ground station of a wireless network and a second baseband processor that establishes a second communication link with a user device. Communication data is routed between the user device and the core network through the first communication link and the second communication link. The UAV receives control commands from a ground-based UAV controller that direct the UAV to travel according to a flight trajectory that is proximate to the user device.

Abstract: Systems, methods, computer program products, and devices provide for computing an eigensystem from a first data set; computing updated eigenvalues that approximate an eigensystem of at least a second data set based on the eigensystem of the first data set; and evaluating a plurality of features in each of the first and at least second data sets using a cost function. The cost function uses fewer than the total number of eigenvalues and can include a condition number. The cost function can perform a coarse approximation of the eigenvalues to de-select at least one of the data sets. This can be useful for learning and/or online processing in an artificial neural network.

Abstract: A Multiple Input Multiple Output (MIMO) system in a radio access network (RAN) comprises a virtualized baseband unit, an antenna array; and a fronthaul network that connects the virtualized baseband unit to the antenna array. For a first set of antennas in the antenna array, the virtualized baseband unit generates a first MIMO matrix from channel state information (CSI); computes a sparse update matrix corresponding to an update to the first set of antennas; sums the sparse update matrix with the first MIMO matrix to produce a second MIMO matrix corresponding to a second set of antennas in the antenna array; determines which of the MIMO matrices results in a better CSI, MIMO condition number, peak-to-average-power ratio, or quality metric of spatial subchannels; and based on the determination, selects the first set of antennas or the second set of antennas to transmit or receive signals in the RAN.

Abstract: Certain aspects of the present disclosure generally relate to wireless communications. In some aspects, a wireless device reduces a peak-to-average power ratio (PAPR) of a discrete-time orthogonal frequency division multiplexing (OFDM) transmission by selecting a signal with low PAPR from a set of candidate discrete-time OFDM signals. The wireless device may generate a partial-update discrete-time OFDM signal by performing a sparse transform operation on a base data symbol sequence, and then linearly combine the partial-update discrete-time OFDM signal with a base discrete-time OFDM signal to produce an updated discrete-time OFDM signal, which is added to the set of candidate discrete-time OFDM signals. Numerous other aspects are provided.

Abstract: An artificial neural network (ANN) generates a base expanded matrix that represents an output of a layer of the ANN, such as the output layer. Values in each row are grouped with respect to a set of network parameters in a previous layer, and a sum of the values in each row produces an output vector of activations. The ANN updates the values in at least one column of the expanded matrix according to parameter updates, which results in an updated expanded matrix or an update expanded matrix. An error or a total cost can be computed from the updated expanded matrix or the update expanded matrix. Nonlinear activation functions can be modeled as piecewise linear functions, and a change in an activation function's slope can be modeled as a linear update to an expanded matrix. Parameter updates can be constrained to a restricted value set in order to simplify update operations performed on the expanded matrices.

Abstract: Systems, methods, and apparatuses for analyzing a wireless communication signal are provided. A set of linear operations is performed on a received signal vector, which comprises values of a transmitted signal received by a receiver. The set of linear operations is configured to produce an expanded matrix having multiple rows and multiple columns. The column values in each row of the base expanded matrix are summed to produce a processed signal vector. At least one signal parameter of the processed signal vector is measured to produce at least one signal parameter measurement, and based on the at least one signal parameter measurement, at least one column in the expanded matrix is updated to produce an updated expanded matrix.

Abstract: A computer-implementable method employs radio signal metadata to train a cognitive learning and inference system to produce an inferred function, wherein the metadata comprises a syntactic structure of at least one radio communication protocol. The inferred function is used to map metadata of a detected radio signal to a cognitive profile of a transmitter of the detected radio signal. The mapping effects intelligent discrimination of the transmitter from at least one other transmitter through corroborative or negating evidentiary observation of properties associated with the metadata of the detected radio signal. A response to the transmitter is based upon the mapping.

Abstract: An unmanned aerial vehicle (UAV) uses a first baseband processor to establish a first communication link with a ground station of a wireless network and a second baseband processor that establishes a second communication link with a user device. The second baseband processor for processing a radio transmission from a user equipment. The second baseband processor is communicatively coupled to the first baseband processor such that the radio transmission is communicated to the ground station via the first communication link. Flight-control hardware steers the UAV along a flight trajectory that is determined by a ground-based UAV controller based at least on the radio transmission, such that the UAV or the ground station can locate or track the user equipment.