Abstract: Features related to systems and methods for automated generation of a machine learning model based in part on a pretrained model are described. The pretrained model is used as a starting point to augment and retrain according to client specifications. The identification of an appropriate pretrained model is based on the client specifications such as model inputs, model outputs, and similarities between the data used to train the models.

Abstract: Techniques are described automatically determining runtime configurations used to execute recurrent neural networks (RNNs) for training or inference. One such configuration involves determining whether to execute an RNN in a looped, or “rolled,” execution pattern or in a non-looped, or “unrolled,” execution pattern. Execution of an RNN using a rolled execution pattern generally consumes less memory resources than execution using an unrolled execution pattern, whereas execution of an RNN using an unrolled execution pattern typically executes faster. The configuration choice thus involves a time-memory tradeoff that can significantly affect the performance of the RNN execution. This determination is made automatically by a machine learning (ML) runtime by analyzing various factors such as, for example, a type of RNN being executed, the network structure of the RNN, characteristics of the input data to the RNN, an amount of computing resources available, and so forth.

Abstract: Features related to systems and methods for automated generation of a machine learning model based in part on a pretrained model are described. The pretrained model is used as a starting point to augment and retrain according to client specifications. The identification of an appropriate pretrained model is based on the client specifications such as model inputs, model outputs, and similarities between the data used to train the models.

Abstract: Techniques for model adaptation are described. For example, a method of receiving a call to provide either a model variant or a model variant profile of a deep learning model, the call including desired performance of the deep learning model, a deep learning model identifier, and current edge device characteristics; comparing the received current edge device characteristics to available model variants and profiles based on the desired performance of the deep learning model to generate or select a model variant or profile, the available model variants and profiles determined by the model identifier; and sending the generated or selected model variant or profile to the edge device to use in inference is detailed.

Abstract: Techniques are described for using machine learning (ML) models to create information technology (IT) infrastructures at a service provider network based on image of IT system architecture diagrams. To create IT system architecture diagrams, system architects often use tools ranging from pen and paper and whiteboards to various types of software-based drawing programs. Based on a user-provided image of an IT system architecture diagram (for example, a digital scan of a hand drawn system diagram, an image file created by a software-based drawing program, or the like), a service provider network uses one or more ML models to analyze the image to identify the constituent elements of the depicted IT system architecture and to create an infrastructure template that can be used to automatically provision corresponding computing resources at the service provider network.

Abstract: Techniques for benchmarking a machine learning model/algorithm are described. For example, in some instances a method includes generating an execution plan for benchmarking of at least one task corresponding to a machine learning model based on an identified machine learning model, identified training data, and at least one objective for the benchmarking job; receiving execution statistics about the execution of the task as a part of the benchmarking job according to the execution plan; and updating the execution plan based at least in part on the received execution statistics of the task.

Abstract: Apparatuses, systems, and methods are described concerning a new type of superposition multiplexing transmission constellation (super-constellation): the Gray-mapped Non-uniform-capable Constellation (GNC). Apparatuses, systems, and methods for generating GNC super-constellations are described, as well as apparatuses, systems, and methods for receiving, demapping, and decoding transmissions using GNC super-constellations. Apparatuses, systems, and methods for selecting a type of superposition multiplexing transmission constellation based on various conditions are also described.

Abstract: Apparatuses, systems, and methods are described concerning a new type of superposition multiplexing transmission constellation (super-constellation): the Gray-mapped Non-uniform-capable Constellation (GNC). Apparatuses, systems, and methods for generating GNC super-constellations are described, as well as apparatuses, systems, and methods for receiving, demapping, and decoding transmissions using GNC super-constellations. Apparatuses, systems, and methods for selecting a type of superposition multiplexing transmission constellation based on various conditions are also described.

Abstract: Apparatuses, systems, and methods are described concerning a new type of superposition multiplexing transmission constellation (super-constellation): the Gray-mapped Non-uniform-capable Constellation (GNC). Apparatuses, systems, and methods for generating GNC super-constellations are described, as well as apparatuses, systems, and methods for receiving, demapping, and decoding transmissions using GNC super-constellations. Apparatuses, systems, and methods for selecting a type of superposition multiplexing transmission constellation based on various conditions are also described.

Abstract: Apparatuses, systems, and methods are described concerning a new type of superposition multiplexing transmission constellation (super-constellation): the Gray-mapped Non-uniform-capable Constellation (GNC). Apparatuses, systems, and methods for generating GNC super-constellations are described, as well as apparatuses, systems, and methods for receiving, demapping, and decoding transmissions using GNC super-constellations. Apparatuses, systems, and methods for selecting a type of superposition multiplexing transmission constellation based on various conditions are also described.

Abstract: Apparatuses, systems, and methods are described concerning a new type of superposition multiplexing transmission constellation (super-constellation): the Gray-mapped Non-uniform-capable Constellation (GNC). Apparatuses, systems, and methods for generating GNC super-constellations are described, as well as apparatuses, systems, and methods for receiving, demapping, and decoding transmissions using GNC super-constellations. Apparatuses, systems, and methods for selecting a type of superposition multiplexing transmission constellation based on various conditions are also described.

Abstract: Apparatuses, systems, and methods are described for power allocation in a superposition multiple access communication system capable of using non-uniform joint constellations or super-constellations. In one method, the conditional probability of a correctly-received symbol and a normalized weighting coefficient is calculated for each receiver and then the sum of weighted efficiencies is calculated. The optimal power allocation is determined for each receiver by maximizing the sum of weighted spectral efficiencies.

Abstract: Apparatuses, systems, and methods are described for power allocation in a superposition multiple access communication system capable of using non-uniform joint constellations or super-constellations. In one method, the conditional probability of a correctly-received symbol and a normalized weighting coefficient is calculated for each receiver and then the sum of weighted efficiencies is calculated. The optimal power allocation is determined for each receiver by maximizing the sum of weighted spectral efficiencies.

Abstract: Apparatuses, systems, and methods are described concerning a new type of superposition multiplexing transmission constellation (super-constellation): the Gray-mapped Non-uniform-capable Constellation (GNC). Apparatuses, systems, and methods for generating GNC super-constellations are described, as well as apparatuses, systems, and methods for receiving, demapping, and decoding transmissions using GNC super-constellations. Apparatuses, systems, and methods for selecting a type of superposition multiplexing transmission constellation based on various conditions are also described.

Abstract: A method (300) and apparatus (106) for transmitting information based on a relationship between a first channel and a second channel is disclosed. The method can include taking (302) a first channel measurement corresponding to a first antenna of a wireless terminal and taking (304) a second channel measurement corresponding to a second antenna of the wireless terminal. The method can include determining (306) a relationship between the first channel and the second channel based on the first channel measurement and based on the second channel measurement. The method can include transmitting (310) information related to an uplink transmission where the information can be based on the relationship.

Abstract: A system and method for neighbor-cell measurement reporting in a cellular environment supporting multistate cells limits measurement reporting by requiring that state-specific trigger conditions are met. Thus for example, a dormant cell may need to meet more stringent measurement conditions before a report is generated by the user device, since a current primary cell may prefer to hand off to an active cell. In particular, state-specific thresholds, offsets, and hysteresis values may be used to enforce a preference for active cells, for example.

Abstract: Methods and apparatus' of determining radio link quality are disclosed. According to various implementations, a user equipment detects an out-of-synchronization condition corresponding to a first control channel, and monitors a second control channel in response to the detecting the out-of synchronization condition.

Abstract: A system and method for neighbor-cell measurement reporting in a cellular environment supporting multistate cells limits measurement reporting by requiring that state-specific trigger conditions are met. Thus for example, a dormant cell may need to meet more stringent measurement conditions before a report is generated by the user device, since a current primary cell may prefer to hand off to an active cell. In particular, state-specific thresholds, offsets, and hysteresis values may be used to enforce a preference for active cells, for example.

Abstract: A method, a user communication device, and a base station are disclosed. A transceiver 302 may receive a serving transmission from a serving base station. A processor 304 may make a status determination of an autonomous muting status of a neighbor base station based on the serving transmission.

Abstract: A method, a mobile system, and a user device for determining a delay spread are disclosed. A memory 306 may store a compound test value based on a multiburst history. The multiburst history may be a set of power delay profile decisions. A processor 304 may create a short power delay profile channel estimate and a long power delay profile channel estimate. The processor 304 may select a chosen power delay profile channel estimate based on the compound test value.