Abstract: Some aspects of this disclosure relate to apparatuses and methods for implementing time domain bandwidth part (TD-BWP) switch for balancing between the UE power consumption and a latency of the UE. For example, a UE can be configured to add a plurality of padding bits to a transport block (TB) to reach a predetermined slot capacity for one transmission time interval (TTI) associated with UE traffic in response to determining that a parameter associated with the UE traffic meets a condition. The UE can be further configured to transmit the TB over the TTI to the base station and receive a message from the base station. The UE is further configured to change a time domain bandwidth part (TD-BWP) based on the received message.

Abstract: Some aspects of this disclosure relate to apparatuses and methods for implementing time domain bandwidth part (TD-BWP) switch for balancing between the UE power consumption and a latency of the UE. For example, a UE can be configured to add a plurality of padding bits to a transport block (TB) to reach a predetermined slot capacity for one transmission time interval (TTI) associated with UE traffic in response to determining that a parameter associated with the UE traffic meets a condition. The UE can be further configured to transmit the TB over the TTI to the base station and receive a message from the base station. The UE is further configured to change a time domain bandwidth part (TD-BWP) based on the received message.

Abstract: A system and method of evaluating processors by first operating a neural network to receive primary data created by data producing processors, including graphical or audio data, as an input layer, with the neural network configured to predict processor evaluations, then blending processor evaluations derived from the neural network with evaluations obtained from processors requesting the primary data, then taking a modified average of a continuing series of blended evaluations to obtain processor performance evaluations. The processor performance evaluations are then used to match data producing processors with data requesting processors.

Abstract: A system and method of evaluating processors by first operating a neural network to receive primary data created by data producing processors, including graphical or audio data, as an input layer, with the neural network configured to predict processor evaluations, then blending processor evaluations derived from the neural network with evaluations obtained from processors requesting the primary data, then taking a modified average of a continuing series of blended evaluations to obtain processor performance evaluations. The processor performance evaluations are then used to match data producing processors with data requesting processors.

Abstract: A system and method of evaluating processors by first operating a neural network to receive primary data created by data producing processors, including graphical or audio data, as an input layer, with the neural network configured to predict processor evaluations, then blending processor evaluations derived from the neural network with evaluations obtained from processors requesting the primary data, then taking a modified average of a continuing series of blended evaluations to obtain processor performance evaluations. The processor performance evaluations are then used to match data producing processors with data requesting processors.

Abstract: A system featuring a network of processors, including a control processor which tracks data transfers between the processors, distinguishes between unique and repeat data transfers, and orders the processors according to the quality and quantity of those unique and repeat data transfers, with repeat data transfers being prioritized over unique data transfers.