Abstract: Techniques for determining a location of a client device using recursive phase vector subspace estimation are described. One technique includes receiving a plurality of angle-of-arrival (AoA) measurements from a plurality of access points (APs). Each AoA measurement includes a plurality of entries for phase values measured from a signal received from a client device at the plurality of APs. At least one AoA measurement of the plurality of AoA measurements that includes at least one of: (i) one or more entries with missing phase values and (ii) one or more entries with erroneous phase values is identified, based on a recursive phase estimation. The plurality of AoA measurements are updated based on the identified at least one AoA measurement. The location of the client device is determined, based on the updated plurality of AoA measurements.

Abstract: Embodiments herein describe performing AoA resolving to identify a plurality of AoAs corresponding to a multipath signal and then using AP voting to identify a location of the client device. AoA resolving enables an AP to identify the different angles at which a multipath signal reaches the AP. That is, due to reflections, a wireless signal transmitted by a single client device may reach the AP using multiple paths that each has their own AoA. The AP can perform AoA resolving to identify the AoAs for the different paths in a multipath signal. In one embodiment, the AoAs for two APs (or a subset of the APs) can be used to identify cross points or intersection points that represent candidate locations of the client device. A voting module can determine whether those cross points corresponds to AoAs identified by the remaining APs.

Abstract: Aspects described herein include a method comprising predicting, based on one or more transmission characteristics, error values for a sequence of bit positions used for modulating data within a packet. The method further comprises generating a bitmap that maps one or more payload bits and one or more padding bits of the packet to respective bit positions of the sequence. The one or more padding bits are preferentially mapped to respective bit positions having relatively greater error values. The method further comprises modulating the sequence according to the bitmap.

Abstract: A wireless node in a wireless communication network. The wireless node includes one or more interfaces configured to receive wireless transmissions, a memory comprising instructions, and a hardware processor. The wireless node samples a received wireless transmission into a plurality of time-based subdivisions for each subdivision of the wireless transmission the wireless node determines a cross-correlation between the subdivision and a local syncword. The local syncword is constructed to correlate to any primary synchronization signal, PSS, of a plurality of PSSs defined for synchronization in the wireless network. The wireless node, based on the cross-correlation, determines whether one PSS of the plurality of PSSs is present in the subdivision of the wireless transmission.

Abstract: Aspects described herein provide for hardening an RF signature by dynamically utilizing a sending device carrier frequency offset (CFO) as part of the RF signature. The CFO and the CFO varying pattern of wireless devices observed. A radio frequency signature at a sending device is paired to a frequency offset estimation algorithm at a receiving device, the final CFO estimation error may be bounded to a small range for various applications and communication protocols, and utilized to properly identify the sending device at the receiving device.

Abstract: Techniques for determining a location of a client device using recursive phase vector subspace estimation are described. One technique includes receiving a plurality of angle-of-arrival (AoA) measurements from a plurality of access points (APs). Each AoA measurement includes a plurality of entries for phase values measured from a signal received from a client device at the plurality of APs. At least one AoA measurement of the plurality of AoA measurements that includes at least one of: (i) one or more entries with missing phase values and (ii) one or more entries with erroneous phase values is identified, based on a recursive phase estimation. The plurality of AoA measurements are updated based on the identified at least one AoA measurement. The location of the client device is determined, based on the updated plurality of AoA measurements.

Abstract: Aspects described herein include a method comprising predicting, based on one or more transmission characteristics, error values for a sequence of bit positions used for modulating data within a packet. The method further comprises generating a bitmap that maps one or more payload bits and one or more padding bits of the packet to respective bit positions of the sequence. The one or more padding bits are preferentially mapped to respective bit positions having relatively greater error values. The method further comprises modulating the sequence according to the bitmap.

Abstract: Embodiments herein describe performing AoA resolving to identify a plurality of AoAs corresponding to a multipath signal and then using AP voting to identify a location of the client device. AoA resolving enables an AP to identify the different angles at which a multipath signal reaches the AP. That is, due to reflections, a wireless signal transmitted by a single client device may reach the AP using multiple paths that each has their own AoA. The AP can perform AoA resolving to identify the AoAs for the different paths in a multipath signal. In one embodiment, the AoAs for two APs (or a subset of the APs) can be used to identify cross points or intersection points that represent candidate locations of the client device. A voting module can determine whether those cross points corresponds to AoAs identified by the remaining APs.

Abstract: In some embodiments, a method obtains raw data from one or more packets received over a wireless channel via an antenna. The raw data comprises raw amplitude values and raw phase values. Each raw amplitude value and raw phase value corresponds to a respective OFDM symbol and subcarrier of a respective packet. The method further comprises processing the raw data according to an interference mitigation process and using the resulting calibrated amplitude values and calibrated phase values to determine weighted phase values. Each weighted phase value corresponds to a respective subcarrier. The method determines a phase variance for the antenna based on comparing the plurality of weighted phase values across the plurality of subcarriers. The method determines whether the wireless channel experiences line-of-sight propagation or non-line-of-sight propagation based at least in part on the phase variance.

Abstract: Techniques for identification and isolation of Internet-of-Things devices in an enterprise network are described. In one embodiment, a method includes detecting a plurality of devices having a first network interface to connect to a wireless wide area network and a second network interface to connect to an enterprise network. The method also includes identifying a first subset of the plurality of devices as Internet-of-Things (IoT) devices based on at least a detected repetition rate on a physical random access channel of a transmission made by a device of the plurality of devices. The method includes assigning the IoT devices to a separate network segment within the enterprise network.

Abstract: In one embodiment, a method includes identifying a number of configured proactive repetitions in downlink transmissions from the base station, selecting k antenna states for receiving repetitive downlink transmissions among the number of antenna states, where k equals the number of configured proactive repetitions, and where each of the k antenna states corresponds to each of the repetitive downlink transmissions, transmitting a CSI report for each of the k antenna states to the base station, where a CSI report for an antenna state is used by the base station to adjust configurations for the corresponding downlink transmission, receiving signals for each of the k repetitive downlink transmissions from the base station using each of the k antenna states, and decoding the downlink transmission based on k sets of received signals, each of the k sets being received using each of the k selected antenna states.

Abstract: A wireless node in a wireless communication network. The wireless node includes one or more interfaces configured to receive wireless transmissions, a memory comprising instructions, and a hardware processor. The wireless node samples a received wireless transmission into a plurality of time-based subdivisions. for each subdivision of the wireless transmission the wireless node determines a cross-correlation between the subdivision and a local syncword. The local syncword is constructed to correlate to any primary synchronization signal, PSS, of a plurality of PSSs defined for synchronization in the wireless network. The wireless node, based on the cross-correlation, determines whether one PSS of the plurality of PSSs is present in the subdivision of the wireless transmission.

Abstract: In some embodiments, a method obtains raw data from one or more packets received over a wireless channel via an antenna. The raw data comprises raw amplitude values and raw phase values. Each raw amplitude value and raw phase value corresponds to a respective OFDM symbol and subcarrier of a respective packet. The method further comprises processing the raw data according to an interference mitigation process and using the resulting calibrated amplitude values and calibrated phase values to determine weighted phase values. Each weighted phase value corresponds to a respective subcarrier. The method determines a phase variance for the antenna based on comparing the plurality of weighted phase values across the plurality of subcarriers. The method determines whether the wireless channel experiences line-of-sight propagation or non-line-of-sight propagation based at least in part on the phase variance.

Abstract: In one embodiment, a method includes identifying a number of configured proactive repetitions in downlink transmissions from the base station, selecting k antenna states for receiving repetitive downlink transmissions among the number of antenna states, where k equals the number of configured proactive repetitions, and where each of the k antenna states corresponds to each of the repetitive downlink transmissions, transmitting a CSI report for each of the k antenna states to the base station, where a CSI report for an antenna state is used by the base station to adjust configurations for the corresponding downlink transmission, receiving signals for each of the k repetitive downlink transmissions from the base station using each of the k antenna states, and decoding the downlink transmission based on k sets of received signals, each of the k sets being received using each of the k selected antenna states.

Abstract: Techniques for identification and isolation of Internet-of-Things devices in an enterprise network are described. In one embodiment, a method includes detecting a plurality of devices having a first network interface to connect to a wireless wide area network and a second network interface to connect to an enterprise network. The method also includes identifying a first subset of the plurality of devices as Internet-of-Things (IoT) devices based on at least a detected repetition rate on a physical random access channel of a transmission made by a device of the plurality of devices. The method includes assigning the IoT devices to a separate network segment within the enterprise network.

Abstract: In one embodiment, a method is performed. A classifier may receive a signal. The classifier may determine whether the signal passes a time-frequency filter. If the signal passes a time-frequency filter, the classifier may perform a search for at least one of a primary synchronization signal (PSS), a secondary synchronization signal (SSS), and/or a demodulation reference signal (DMRS) in the signal. The classifier may classify the signal based on a result of the search.