Abstract: Methods, systems, and computer storage media for providing an indication of an integrity of an object based on a non-invasive assessment of the integrity of the object using acoustic signature management engine in object integrity sensing system. In operation, an aggregate object-intermediate-medium sound of an object in an intermediate medium is detected (e.g., via sensors). An acoustic signature of the aggregate object-intermediate-medium sound is generated as a processed acoustic channel associated with statistical measurements. A reference acoustic signature of the object and intermediate medium is accessed. The reference acoustic signature is associated with an acoustic signature computation model, that generates reference acoustic signatures based on a mean and standard deviation measurements of input signals transmitted through the object and intermediate medium.

Abstract: Aspects include a machine learning based resource block scheduler configured to meet service level requirements of applications. Aspects include receiving a plurality of scheduling requests each associated with a respective application of a plurality of applications on a plurality of wireless devices, identifying a plurality of current channel state information each associated with one of the plurality of wireless devices, and identifying a plurality of different types of service level requirements each associated with one of the plurality of applications.

Abstract: In a 5G network, a slice controller operating in a radio access network (RAN) is arranged to make predictions of channel state information (CSI) for user equipment (UE) on the network using a predictive propagation model. The slice controller uses the predicted CSI to schedule subcarriers and time slots associated with physical radio resources for data transmission on slices of the 5G network between a 5G radio unit (RU) and the UE to maximize network throughput on a slice for the radio spectrum that is utilized for a given time period. In view of the CSI predictions, the slice controller controls operations of the MAC (Medium Access Control) layer functions based on PHY (physical) layer radio resource subsets to schedule the subcarrier and time slots for data transmissions on a slice over the 5G air interface from RU to UE.

Abstract: Slice control elements in a 5G slicing framework are instantiated in trusted hardware to provide for sealed data transmission in a trusted slice. In addition to sealing the data plane in the trusted slice, the control plane for the slice may be secured by the instantiation into the trusted hardware of layer 2 (medium access control—MAC) scheduling functions for radio resources (e.g., subcarriers and time slots). Layer 1 (physical—PHY) may also be configured to further enhance security of the trusted slice by isolating its PHY layer from that of other trusted and non-trusted slices. Such isolation may be implemented, for example, by using dedicated PHY resources, or by limiting resource time sharing to provide temporal isolation.

Abstract: A method for controlling transmission power from one or more radio units is provided including monitoring channel state feedback for a signal communicated between a first radio unit of the one or more radio units and a user device in a transmitted frequency range, wherein the channel state feedback is based at least in part on a metric of quality of the communicated radiofrequency signal, determining that the channel state feedback satisfies a channel state condition, wherein the channel state condition includes a metric to evaluate performance of the one or more radio units relative to the user device based at least on the metric of quality of the communicated signal, and transmitting an instruction to adjust a transmission power in the transmitted frequency range of at least one of the one or more radio units based at least on the satisfaction of the channel state condition.

Abstract: Aspects of the present disclosure relate to allocating workloads to vRANs via programmable switches at far-edge cloud datacenters. Traditionally, traffic allocation is handled by dedicated servers running load-balancing software. However, rerouting RAN traffic to such servers increases both energy and capital costs, degrades end-to-end performance, and requires additional physical space, all of which are undesirable or even infeasible for a RAN far-edge datacenter. Since switches are located in the path of data traffic, workflow policies can be designed to inspect packet headers of incoming traffic, evaluate real-time network information, determine available vRAN instances, and update the packet headers to steer the incoming traffic for processing. As network conditions change, the workflow policies enable the switch to dynamically redirect workloads to alternative vRANs for processing.

Abstract: Aspects of the present disclosure relate to allocating RAN resources among RAN slices using a machine learning model. In examples, the machine learning model may determine an optimal RAN resource configuration based on compute power needs. As a result, RAN resource allocation generation and compute power requirements may improve, even in instances with changing or unknown network conditions. In examples, a prediction engine may receive communication parameters and/or requirements associated with service-level agreements (SLAs) for applications executing at least partially at a device in communication with the RAN. The RAN may generate one or more RAN resource configuration for implementation among RAN slices. Upon a change in network conditions or SLA requirements, an optimal RAN configuration may be determined in terms of required compute power.

Abstract: Aspects of the present disclosure relate to allocating RAN resources among RAN slices according to reinforcement learning techniques. For example, a network slice controller (NSC) may generate a RAN resource allocation and associated expected slice characteristics may be determined for each slice based on the RAN resource allocation. Resources of the RAN may be allocated accordingly, such that resulting actual slice characteristics may be observed and compared to the expected slice characteristics. A reward may be generated for the resource allocation, for example based on a difference between the expected and observed slice characteristics. RAN resource allocation and slice characteristic forecasting may be adapted according to such rewards. As a result, RAN resource allocation generation may improve, even in instances with changing or unknown network conditions.

Abstract: In a 5G network, a profiler component of a network slice controller is arranged to dynamically observe behaviors of pre-defined types of network slices when handling current traffic. The profiler employs the observed behaviors to generate profiles of the pre-defined slice types in terms of throughput, reliability, or other suitable metrics. In response to a request from an application for admission to the 5G network for which an ID of an appropriate pre-defined network slice type is unknown, the application request and traffic is handled on a slice which is temporarily utilized while the profiler dynamically observes application behaviors to generate an application profile. The profiler identifies a pre-defined slice type having a profile that is the closest match to the generated application profile. The application may then be moved from the temporary slice to a slice of the identified pre-defined type so that optimal slice characteristics are provided for the application's traffic.

Abstract: In a 5G network, a slice controller is arranged to dynamically configure a radio access network (RAN) by allocating physical radio resources into RAN slices by making predictions of channel state information (CSI) for user equipment (UE) executing applications that make connectivity requests for admission to particular identified slices. The slice controller grants or denies admission requests based on the predicted CSI to ensure that applicable service level agreement (SLA) guarantees are satisfied for traffic across all the RAN slices. Each time new admission requests are received from applications, the slice controller determines whether a suitable RAN configuration exists that will enable SLA guarantees for the slices to continue to be satisfied for the current traffic while also meeting the SLA guarantees applicable to the new admission request.

Abstract: In a 5G network, a slice controller operating in a radio access network (RAN) is arranged to make predictions of channel state information (CSI) for user equipment (UE) on the network using a predictive propagation model. The slice controller uses the predicted CSI to schedule subcarriers and time slots associated with physical radio resources for data transmission on slices of the 5G network between a 5G radio unit (RU) and the UE to maximize network throughput on a slice for the radio spectrum that is utilized for a given time period. In view of the CSI predictions, the slice controller controls operations of the MAC (Medium Access Control) layer functions based on PHY (physical) layer radio resource subsets to schedule the subcarrier and time slots for data transmissions on a slice over the 5G air interface from RU to UE.

Abstract: Techniques for a motion tracing device using radio frequency signals are presented. The motion tracing device utilizes radio frequency signals, such as WiFi to identify moving objects and trace their motion. Methods and apparatus are defined that can measure multiple WiFi backscatter signals and identify the backscatter signals that correspond to moving objects. In addition, motion of a plurality of moving objects can be detected and traced for a predefined duration of time.

Abstract: A virtual reality system including a head mounted display (HMD), a server, and a beam steering apparatus utilizes optical data transmission from the server to the HMD. The data rates/bandwidths provided by optical data transmission allow the amount of compression needed for data transfer (and the latency associated with compression) to the HMD to be reduced. The efficient offloading of processing tasks from the HMD to the server, reduces HMD power consumption, and enables the delivery of video having high resolution, framerate, and quality. As the user of the HMD moves, and the pose (position and orientation) of the HMD changes, the pose is provided to the server and beam steering apparatus. Based on the pose, the server renders image frames for transmission to the HMD and the beam steering apparatus directs an optical beam to the HMD to enable transmission of the image frames from the server to the HMD.

Abstract: A virtual reality system including a head mounted display (HMD), a server, and a beam steering apparatus utilizes optical data transmission from the server to the HMD. The data rates/bandwidths provided by optical data transmission allow the amount of compression needed for data transfer (and the latency associated with compression) to the HMD to be reduced. The efficient offloading of processing tasks from the HMD to the server, reduces HMD power consumption, and enables the delivery of video having high resolution, framerate, and quality. As the user of the HMD moves, and the pose (position and orientation) of the HMD changes, the pose is provided to the server and beam steering apparatus. Based on the pose, the server renders image frames for transmission to the HMD and the beam steering apparatus directs an optical beam to the HMD to enable transmission of the image frames from the server to the HMD.

Abstract: Techniques for a motion tracing device using radio frequency signals are presented. The motion tracing device utilizes radio frequency signals, such as WiFi to identify moving objects and trace their motion. Methods and apparatus are defined that can measure multiple WiFi backscatter signals and identify the backscatter signals that correspond to moving objects. In addition, motion of a plurality of moving objects can be detected and traced for a predefined duration of time.