Disclosed in some examples are systems, methods, devices, and machine-readable mediums for improved communications between a software-defined radio front-end device and a network-based computing device. Rather than packetize samples together, same bit positions from multiple ADC samples may be packetized together. If a Quality of Service (QoS) metric of the network connection between the RF front-end device and the network-based processing computing drops below a threshold, the RF front-end device may prioritize sending packets with the more significant bits over packets with less significant bits. In other examples, the RF front-end device may prioritize samples corresponding to certain data types over other data types.

Various embodiments provide for deep learning-based architectures and design methodologies for an orthogonal frequency division multiplexing (OFDM) receiver under the constraint of one-bit complex quantization. Single bit quantization greatly reduces complexity and power consumption in the receivers, but makes accurate channel estimation and data detection difficult. This is particularly true for OFDM waveforms, which have high peak-to average (signal power) ratio in the time domain and fragile subcarrier orthogonality in the frequency domain. The severe distortion for one-bit quantization typically results in an error floor even at moderately low signal-to-noise-ratio (SNR) such as 5 dB. For channel estimation (using pilots), various embodiments use novel generative supervised deep neural networks (DNNs) that can be trained with a reasonable number of pilots. After channel estimation, a neural network-based receiver specifically, an autoencoder jointly learns a precoder and decoder for data symbol detection.

Examples described herein include systems and methods which include wireless devices and systems with examples of mixing input data with coefficient data specific to a processing mode selection. For example, a computing system with processing units may mix the input data for a transmission in a radio frequency (RF) wireless domain with the coefficient data to generate output data that is representative of the transmission being processed according to a specific processing mode selection. The input data is mixed with coefficient data at layers of multiplication/accumulation processing units (MAC units). The processing mode selection may be associated with an aspect of a wireless protocol. Examples of systems and methods described herein may facilitate the processing of data for 5G wireless communications in a power-efficient and time-efficient manner.

Systems and method are provided for canceling unwanted transmitter-to-receiver leakage in a coherent wireless system using a feedforward waveform that overcomes the limitations of purely analog or purely digital cancelation systems and methods. Systems and methods in accordance with embodiments of the present disclosure generate a software-defined waveform that, when fed forward into the receiver, effectively cancels the leakage. Embodiments of the present disclosure can use a defined cancelation waveform (e.g., a software-defined cancelation waveform) that can cancel multiple leakage paths at the same time.

Provided is a radar and communications enhanced sail for a sailboat, sail ship, or sail drone. The sail includes a first sail section comprising an active communication system, a second sail section comprising a passive communication system, or a combination thereof. The active communication system includes an antenna array (transceiver) and a software-defined radio (SDR), while the passive communication system comprises a reflective panel or sections and/or array of reflector panels or sections. The active system utilizes its SDR and transceiver to communicate back and forth with an onshore SDR and transceiver to provide information as necessary. The passive system receives a radar signal via the reflective material on the sail and reflects the signal back at the radar, which produces a radar cross section indicating that there is an object (in this case the sailboat) in the ocean.

A radio telescope array is provided for tracking radio sources that are essentially infinitely stable and resilient transmitters. The radio telescope array may be implemented with just a few antennas in different applications, such as an ionospheric density gradiometer or an imaging scintillometer. Data received at the radio telescope array may be utilized for various purposes, for example, to analyze ionospheric variations, study bursts of radio emission or monitor cosmic objects.

A method and system of transmitting data. The method comprises receiving data, in a memory of the server computing device, from an asset tracker device; determining, in a processor of the server computing device, one or more weighting factors representing a respective priority associated with one or more of a latency, a cost, a power utilization and a throughput associated with transmission of the data for each of a plurality of communication channels; and selecting, from the plurality of communication channels, a communication channel for transmission of the data based at least in part on the one or more weighting factors and a transmission schedule for the data.

Embodiments of this application disclose a radio frequency unit, an antenna, and a signal processing method. The method includes: receiving an uplink signal in a downlink slot, where the uplink signal includes signals of N frequency bands; filtering and amplifying the signals of the N frequency bands; converting the uplink signal into a digital intermediate frequency signal; and then processing the digital intermediate frequency signal.

A receiver in a wireless communications network demodulates and decodes received Carrier Interferometry (CI) signals. A demodulator demodulates a received multicarrier signal into demodulated symbols, wherein the plurality of demodulated symbols are CI-coded data symbols. A CI decoder decodes the CI-coded data symbols to produce data symbol estimates. The CI decoder can employ an inverse of a transform, such as a Fourier transform, used by a transmitter to provide the multicarrier signal with a low peak-to-average-power ratio.

Various embodiments provide for deep learning-based architectures and design methodologies for an orthogonal frequency division multiplexing (OFDM) receiver under the constraint of one-bit complex quantization. Single bit quantization greatly reduces complexity and power consumption in the receivers, but makes accurate channel estimation and data detection difficult. This is particularly true for OFDM waveforms, which have high peak-to average (signal power) ratio in the time domain and fragile subcarrier orthogonality in the frequency domain. The severe distortion for one-bit quantization typically results in an error floor even at moderately low signal-to-noise-ratio (SNR) such as 5 dB. For channel estimation (using pilots), various embodiments use novel generative supervised deep neural networks (DNNs) that can be trained with a reasonable number of pilots. After channel estimation, a neural network-based receiver specifically, an autoencoder jointly learns a precoder and decoder for data symbol detection.