In some implementations, an initiator device establishes a measurement session with each of a plurality of responder devices based at least in part on their capabilities. The initiator device negotiates, with each of the responder devices, a respective set of one or more sensing parameters based at least in part on the capabilities of the initiator device and the capabilities of the respective responder device, the respective sets of sensing parameters associated with the responder devices being identified by a unique Measurement Setup identifier (ID). The initiator device performs one or more measurement instances with the responder devices based on the respective sets of sensing parameters, each measurement instance identified by a unique Measurement Instance ID and the respective Measurement Setup ID. The initiator device obtains, from each responder device, one or more measurement reports indicating channel state information (CSI) based on the one or more respective measurement instances.

Systems, methods, and apparatuses for providing dynamic, prioritized spectrum utilization management. The system includes at least one monitoring sensor, at least one data analysis engine, at least one application, a semantic engine, a programmable rules and policy editor, a tip and cue server, and/or a control panel. The tip and cue server is operable utilize the environmental awareness from the data processed by the at least one data analysis engine in combination with additional information to create actionable data.

Systems, methods, and apparatuses for providing dynamic, prioritized spectrum utilization management. The system includes at least one monitoring sensor, at least one data analysis engine, at least one application, a semantic engine, a programmable rules and policy editor, a tip and cue server, and/or a control panel. The tip and cue server is operable utilize the environmental awareness from the data processed by the at least one data analysis engine in combination with additional information to create actionable data.

A method, a device, and a non-transitory storage medium are described in which a network performance optimization service is provided. A load balancer device may receive, from an end device, an application service request for an application service that is available from multiple server devices of an application layer network. The load balancer device may determine, from a source identifier associated with the end device and obtained from the application service request, that the source identifier does not map to a network traffic forwarding rule of a set of stored network traffic forwarding rules. In response, the load balancer device may map the source identifier to an application service profile of a set of stored application service profiles; select, based on the application service profile, a first server device of the multiple server devices and establish the first server device to be the destination of the application service request.

A method, a device, and a non-transitory storage medium are described in which a network performance optimization service is provided. A load balancer device may receive, from an end device, an application service request for an application service that is available from multiple server devices of an application layer network. The load balancer device may determine, from a source identifier associated with the end device and obtained from the application service request, that the source identifier does not map to a network traffic forwarding rule of a set of stored network traffic forwarding rules. In response, the load balancer device may map the source identifier to an application service profile of a set of stored application service profiles; select, based on the application service profile, a first server device of the multiple server devices and establish the first server device to be the destination of the application service request.

With advanced compute capabilities and growing convergence of wireless standards and artificial intelligence (AI) applications, there is a requirement to run multiple wireless standards, e.g., 4G LTE, 5G NR and Wi-Fi, and AI on a single hardware together. Typical solutions include reserving some compute resources for specific wireless standards and a dedicated AI resource due to different precision compared to baseband processing. Typical solutions for signal processing with converged wireless standards and machine learning (ML) applications include having dedicated hardware acceleration and reserving some commutating or processing resources for specific wireless standards and a dedicated AI operation. Such approaches result in inefficient use of resources and lack of operation flexibility. The present patent document discloses embodiments to leverage commonalities in hardware acceleration for converged baseband and AI operators, thus achieving improved efficiency in power consumption and improved hardware resources utilization.

With advanced compute capabilities and growing convergence of wireless standards and artificial intelligence (AI) applications, there is a requirement to run multiple wireless standards, e.g., 4G LTE, 5G NR and Wi-Fi, and AI on a single hardware together. Typical solutions include reserving some compute resources for specific wireless standards and a dedicated AI resource due to different precision compared to baseband processing. Typical solutions for signal processing with converged wireless standards and machine learning (ML) applications include having dedicated hardware acceleration and reserving some commutating or processing resources for specific wireless standards and a dedicated AI operation. Such approaches result in inefficient use of resources and lack of operation flexibility. The present patent document discloses embodiments to leverage commonalities in hardware acceleration for converged baseband and AI operators, thus achieving improved efficiency in power consumption and improved hardware resources utilization.

In one aspect, a first playback device is configured to (i) receive a set of voice signals, (ii) process the set of voice signals using a first set of audio processing algorithms, (iii) identify, from the set of voice signals, at least two voice signals that are to be further processed, (iv) determine that the first playback device does not have a threshold amount of computational power available, (v) receive an indication of an available amount of computational power of a second playback device, (vi) send the at least two voice signals to the second playback device, (vii) cause the second playback device to process the at least two voice signals using a second set of audio processing algorithms, (viii) receive, from the second playback device, the processed at least two voice signals, and (ix) combine the processed at least two voice signals into a combined voice signal.

A sensor control system (202) for managing at least a first set of one or more sensors (101) for monitoring a first domain of an industrial process and a second set of one or more sensors (102) for monitoring a second domain of the industrial process, wherein the sensor control system (202) comprises at least a first reinforcement learning, RL, agent (A1) and a second RL agent (A2), wherein the first and second RL agents were trained using reinforcement learning and a process graph (196) representing the industrial process.

The present disclosure relates to a serving wireless communication node (AP.sub.1) in a wireless communication system (1), wherein the serving node (AP.sub.1) is adapted to determine that a served user terminal (2) is going to enter a zone (3) that switches between being reachable and unreachable for the serving node (AP.sub.1), and to predict data (x.sub.m+1 . . . x.sub.n) to be transmitted to the user terminal (2) for at least a part of the time the user terminal (2) is in the zone (3) and is unreachable for the serving node (AP.sub.1). When the zone (3) is reachable, the sewing node (AP.sub.1) is adapted to transfer predicted data (x.sub.m+1 . . . x.sub.n) to a cache node (AP.sub.C) positioned within the zone (3), enabling the cache node (AP.sub.C) to transfer the predicted data (x.sub.m+1 . . . x.sub.n) to the user terminal (3) when the user terminal (2) is in the zone (3) and is unreachable for the serving node (AP.sub.1).