Creation of new edges in a network may be used as an indication of a potential attack on the network. Historical data of a frequency with which nodes in a network create and receive new edges may be analyzed. Baseline models of behavior among the edges in the network may be established based on the analysis of the historical data. A new edge that deviates from a respective baseline model by more than a predetermined threshold during a time window may be detected. The new edge may be flagged as potentially anomalous when the deviation from the respective baseline model is detected. Probabilities for both new and existing edges may be obtained for all edges in a path or other subgraph. The probabilities may then be combined to obtain a score for the path or other subgraph. A threshold may be obtained by calculating an empirical distribution of the scores under historical conditions.

Various aspects described herein relate to hybrid automatic repeat/request (HARQ) communications in a wireless network. A first instance of a HARQ communication is transmitted or received over a first set of one or more links. Based on the transmitting or receiving the first instance of the HARQ communication, a scheduling grant can be received for a second instance of the HARQ communication over a second set of one or more links different from the first set of one or more links. The second instance of the HARQ communication can accordingly be transmitted or received over the second set of one or more links based at least in part on the scheduling grant.

A technique of mapping data, suitable for Peak to Average Power Ratio (PAPR) reduction while transmitting data portions via a communication channel limited by a peak power p.sub.peak. The mapping is performed by utilizing a Markovian symbol transition probability distribution with quantized probabilities and by selecting, for a specific data portion at a current channel state, such a binary symbol (called thinned label) which allows puncturing one or more bits in the thinned label's bit sequence before transmission.

Low latency transmission time interval (TTI) structures and feedback configurations allow for a downlink transmission, a feedback indication indicating successful or unsuccessful reception of the downlink transmission, and a retransmission of the downlink transmission, within a same subframe or 1 ms time period. A TTI structure may include a number of shortened TTIs (sTTIs) that are transmitted in a subframe, and timing for feedback transmissions may be identified based at least in part on the TTI structure. The TTI structure and configurations for feedback timing may be dynamically or semi-statically determined by a user equipment (UE). In some cases, the TTI structure may include an identified partial sTTI allocated to a UE that may span fewer than all of the resources of a sTTI and allow for faster processing and generation of feedback information, and for faster retransmissions of unsuccessfully received transmissions.

Creation of new edges in a network may be used as an indication of a potential attack on the network. Historical data of a frequency with which nodes in a network create and receive new edges may be analyzed. Baseline models of behavior among the edges in the network may be established based on the analysis of the historical data. A new edge that deviates from a respective baseline model by more than a predetermined threshold during a time window may be detected. The new edge may be flagged as potentially anomalous when the deviation from the respective baseline model is detected. Probabilities for both new and existing edges may be obtained for all edges in a path or other subgraph. The probabilities may then be combined to obtain a score for the path or other subgraph. A threshold may be obtained by calculating an empirical distribution of the scores under historical conditions.

A time domain multiplexed (TDM) routing schedule for a wireless mesh network can be generated using a Markov chain process. In particular, synchronized paths between access nodes and gateways in the mesh network can be added to, and removed from, the TDM routing schedule in an iterative fashion according to each individual state in a state progression of a Markov chain, with each state of the Markov chain mapping a different combination of synchronized paths to the TDM routing schedule. In some embodiments, transitioning between states of a Markov chain is performed according to a proportionally fair transition rate.

The present application discloses systems and methods for adjusting a back-off value for a rank. In some embodiment, the method includes the steps of: (a) determining whether the rank is underutilized and (b) in response to determining that the rank is underutilized, decreasing the back-off value as a function of time while the rank remains underutilized.

A system, apparatus, computer-readable medium, and computer-implemented method are provided for detecting anomalous behavior in a network. Historical parameters of the network are determined in order to determine normal activity levels. A plurality of paths in the network are enumerated as part of a graph representing the network, where each computing system in the network may be a node in the graph and the sequence of connections between two computing systems may be a directed edge in the graph. A statistical model is applied to the plurality of paths in the graph on a sliding window basis to detect anomalous behavior. Data collected by a Unified Host Collection Agent (UHCA) may also be used to detect anomalous behavior.

For random linear network encoded data transmission from user equipment, a method receives a Galois field and a resource allocation for transmission of data from the user equipment. The method further encodes a first set of k data packets from a first data ensemble into a first random linear network coded (RLNC) packet as a function of the Galois field. The first RLNC packet includes an ensemble number that identifies the first data ensemble, an encoding vector, and a sequence of RLNC sub-packets. In addition, the method generates a first protocol data unit (PDU) that comprises a number of RLNC sub-packets of the first RLNC packet. A size of the first PDU does not exceed the resource allocation.

A system, apparatus, computer-readable medium, and computer-implemented method are provided for detecting anomalous behavior in a network. Historical parameters of the network are determined in order to determine normal activity levels. A plurality of paths in the network are enumerated as part of a graph representing the network, where each computing system in the network may be a node in the graph and the sequence of connections between two computing systems may be a directed edge in the graph. A statistical model is applied to the plurality of paths in the graph on a sliding window basis to detect anomalous behavior. Data collected by a Unified Host Collection Agent (UHCA) may also be used to detect anomalous behavior.