Techniques for establishing communication channels between user devices experiencing network connectivity issues and remote communication systems are described herein. The techniques include the use of a secondary device to act as a proxy, or a “middle man,” to facilitate the communications with the user device. A user device may detect lack of network connectivity, and begin broadcasting advertisement messages that indicate the lack of connectivity. A secondary device may detect the advertisement message, and send a discovery message to a connectivity system indicating that it detected the advertisement message. The connectivity system can provide this information to a remote communication system, and the remote communication system can establish a connection with the secondary device and instruct the secondary device to establish a connection with the user device. The remote communication system then has a communication channel with the user device, using the secondary device, to troubleshoot the user device.

A computer-implemented method for establishing and controlling a mobile perimeter and for determining a geographic location of an RF emitting source at or within the mobile perimeter includes receiving from RF sensors in a network, processed RF emissions from the source collected at RF sensors. The RF emissions follow a wireless protocol and include frames encoding RF emitting source identification information. The method further includes extracting RF emitting source identification information from the frames, processing the source identification information to identify the RF emitting source, and classifying the RF emitting source by one or more of UAS type, UAS capabilities, and UAS model. The method also includes receiving from the RF sensors, a geographic location of each RF sensor and a time of arrival (TOA) of the RF emissions at the RF sensor; and executing a multilateration process to estimate a geographic location of the RF emitting source.

A computer-implemented method for establishing and controlling a mobile perimeter and for determining a geographic location of an RF emitting source at or within the mobile perimeter includes receiving from RF sensors in a network, processed RF emissions from the source collected at RF sensors. The RF emissions follow a wireless protocol and include frames encoding RF emitting source identification information. The method further includes extracting RF emitting source identification information from the frames, processing the source identification information to identify the RF emitting source, and classifying the RF emitting source by one or more of UAS type, UAS capabilities, and UAS model. The method also includes receiving from the RF sensors, a geographic location of each RF sensor and a time of arrival (TOA) of the RF emissions at the RF sensor; and executing a multilateration process to estimate a geographic location of the RF emitting source.

A method for discovering devices in a mesh network. After receiving a plurality of request frames sent by a large number of first devices, a second device sends a response frame via broadcast form times, at an interval of n; and each first device that receives the response frame establishes a mutual discovery relationship with the second device. In the event that an extremely large number of devices are present in the mesh network, the present invention may greatly improve the probability of discovery between devices, and effectively suppress network storming, thereby enabling the mesh network to maintain a good network performance.

Embodiments are disclosed that allow encrypted data to be sent between a Bluetooth enabled device and a virtual device associated with a corresponding physical device. In particular, a Bluetooth implementation on the physical device may include one or more raw interfaces to facilitate endpoint to endpoint secure Bluetooth cryptography. Using these raw interfaces, an encrypted Bluetooth channel may be established directly between the virtual device and the Bluetooth enabled device using the radio of the physical device, where data may be encrypted and decrypted at an endpoint of the Bluetooth communication channel (such as at the virtual device or the Bluetooth enabled device) and passed through a Bluetooth implementation on the physical device without any additional encryption or decryption being performed on that data.

Technologies for establishing and utilizing a decentralized cloud infrastructure using a plurality of mobile computing devices include broadcasting for the formation of the decentralized cloud computing and storage infrastructure and establishing wireless communications between the plurality of mobile computing devices. The plurality of mobile computing devices self-organize and cooperate with one another to establish a structured decentralized cloud infrastructure to expose and sharing resources, services, and/or applications for ad hoc or socially-driven decentralized, cloud computing purposes.

This disclosure generally relates to systems, devices, apparatuses, products, and methods for wireless communication. For example, a communication system may include a repeater that relays communications between communication devices. The repeater determines a downlink gain value to use for one or more downlink initial access messages received at the repeater. The repeater determines an uplink gain value to use for one or more downlink initial access messages received at the repeater. The uplink gain value is based on the downlink gain value and a noise level related to a channel between the communication device and the repeater. The repeater receives a downlink initial access message, and applies the downlink gain value to the downlink initial access message. The repeater receives an uplink initial access message, and applies the uplink gain value to the uplink initial access message.

A network control and rate optimization solution for multiuser multiple input multiple output (MU-MIMO) communications in wireless networks. This solution is decentralized and includes scheduling and routing of the MU-MIMO communication links that adapt to dynamic channel, interference, and traffic conditions. The ergodic sum rates of MIMO multiple access channel (MAC) and interference channel (IC) configurations are analyzed by considering the error, and overhead effects due to channel estimation (training) and quantization (feedback). By taking practical considerations such as channel estimation, quantization error and in-network interference into account, the rate gain is shown with an increasing number of antennas compared with single-input single-output (SISO) systems. A distributed channel access protocol to select and activate MU-MIMO configurations is presented with the maximum achievable sum rates using local information on channel, interference, and traffic conditions. The scheduling algorithm is extended to routing via a cross-layer solution based on a decentralized version of the backpressure algorithm. After accounting for the control message overhead, it is shown that the proposed MU-MIMO scheduling and routing solution improves the stable throughput over the minimum distance routing based on frequency of encounters and single user MIMO communications in a mobile ad hoc network (MANET) setting.

A network control and rate optimization solution for multiuser multiple input multiple output (MU-MIMO) communications in wireless networks. This solution is decentralized and includes scheduling and routing of the MU-MIMO communication links that adapt to dynamic channel, interference, and traffic conditions. The ergodic sum rates of MIMO multiple access channel (MAC) and interference channel (IC) configurations are analyzed by considering the error, and overhead effects due to channel estimation (training) and quantization (feedback). By taking practical considerations such as channel estimation, quantization error and in-network interference into account, the rate gain is shown with an increasing number of antennas compared with single-input single-output (SISO) systems. A distributed channel access protocol to select and activate MU-MIMO configurations is presented with the maximum achievable sum rates using local information on channel, interference, and traffic conditions. The scheduling algorithm is extended to routing via a cross-layer solution based on a decentralized version of the backpressure algorithm. After accounting for the control message overhead, it is shown that the proposed MU-MIMO scheduling and routing solution improves the stable throughput over the minimum distance routing based on frequency of encounters and single user MIMO communications in a mobile ad hoc network (MANET) setting.

A method and apparatus are disclosed herein for controlling transmit power in a station (STA) of a wireless local area network (WLAN). A STA may transmit, to another STA, transmit power step size information that indicates a maximum transmission power for an operational bandwidth that the STA supports. The STA may receive, from another STA, a signal in one of the plurality of operational bandwidths that the STA supports. The received signal may include a transmission power based on the transmitted adjustment step size setting.