Disclosed are interference simulation system, method and apparatus, interference test system, method and apparatus, and non-transitory computer-readable storage medium. The interference simulation system, provided at a sending end, may include a configuration unit and a simulation unit. The configuration unit is configured to configure interference parameters according to an interference model, where the interference model is generated according to a preset interference scenario. The simulation unit is configured to acquire the interference parameters and generate interference information according to the interference parameters, and further configured to simulate the interference scenario at the sending end according to the interference information.

Disclosed are interference simulation system, method and apparatus, interference test system, method and apparatus, and non-transitory computer-readable storage medium. The interference simulation system, provided at a sending end, may include a configuration unit and a simulation unit. The configuration unit is configured to configure interference parameters according to an interference model, where the interference model is generated according to a preset interference scenario. The simulation unit is configured to acquire the interference parameters and generate interference information according to the interference parameters, and further configured to simulate the interference scenario at the sending end according to the interference information.

A method, a device, and a non-transitory storage medium are described in which a polymorphic algorithm service is provided. The service may coordinate and manage the execution of polymorphic algorithms of various optimization types in a multi-tier network, such as a radio access network or a self-organizing network. The service may coordinate the transition of execution of the polymorphic algorithms among the tiers of the multi-tier network, such that the polymorphic algorithm of a type may be active, for any given node, in a single tier of the multi-tier network. The service may monitor the yield of the optimizations based on various machine learning technologies, polices, and optimization targets. The polymorphic algorithms may operate in different time granularities in correspondence to the tiers of the multi-tier network.

A method, a device, and a non-transitory storage medium are described in which a polymorphic algorithm service is provided. The service may coordinate and manage the execution of polymorphic algorithms of various optimization types in a multi-tier network, such as a radio access network or a self-organizing network. The service may coordinate the transition of execution of the polymorphic algorithms among the tiers of the multi-tier network, such that the polymorphic algorithm of a type may be active, for any given node, in a single tier of the multi-tier network. The service may monitor the yield of the optimizations based on various machine learning technologies, polices, and optimization targets. The polymorphic algorithms may operate in different time granularities in correspondence to the tiers of the multi-tier network.

A method includes receiving a representation of a predefined planned event that includes the use of a first set of cellular data service infrastructure elements. A performance of the first set of cellular data service infrastructure elements is simulated, and a predicted failure of at least one cellular data service infrastructure element from the first set of cellular data service infrastructure elements is identified based on the simulation. In response to identifying the predicted failure, a modification to the at least one cellular data service infrastructure element or an additional cellular data service infrastructure element is determined and included in a second set of cellular data service infrastructure elements whose performance is subsequently simulated. The simulated performance of the first set of cellular data service infrastructure elements is compared with the simulated performance of the second set of cellular data service infrastructure elements to determine a performance improvement.

A computer implemented method of training a reinforcement learning model for controlling a dynamic system includes generating a trajectory sample of a simulated system that corresponds to the dynamic system, the trajectory sample including a current state s.sub.t of the simulated system at time t, an action a.sub.t taken on the simulated system at time t according to a policy π, a subsequent state s.sub.t+1 of the simulated system following the action a.sub.t, and a reward r associated with the action at, and estimating a robust target value V.sup.π(s.sub.t) for the trajectory sample, wherein the robust target value V.sup.π(s.sub.t) includes an expected value of a sum of the reward r and a minimum estimated value V.sup.π(s.sub.t+1) of the simulated system at the subsequent state s.sub.t+1 based on a plurality of transition possibilities p from the current state s.sub.t in response to the action a.sub.t. The method updates a value function estimator based on the robust target value, and updates the policy based on the trajectory and the value function estimator.

A computer implemented method of training a reinforcement learning model for controlling a dynamic system includes generating a trajectory sample of a simulated system that corresponds to the dynamic system, the trajectory sample including a current state s.sub.t of the simulated system at time t, an action a.sub.t taken on the simulated system at time t according to a policy π, a subsequent state s.sub.t+1 of the simulated system following the action a.sub.t, and a reward r associated with the action at, and estimating a robust target value V.sup.π(s.sub.t) for the trajectory sample, wherein the robust target value V.sup.π(s.sub.t) includes an expected value of a sum of the reward r and a minimum estimated value V.sup.π(s.sub.t+1) of the simulated system at the subsequent state s.sub.t+1 based on a plurality of transition possibilities p from the current state s.sub.t in response to the action a.sub.t. The method updates a value function estimator based on the robust target value, and updates the policy based on the trajectory and the value function estimator.

Aspects of the subject disclosure may include, for example, determining a coherence block for each user equipment (UE) of a plurality of UEs being served by the first cell, resulting in a plurality of coherence blocks, responsive to the determining, identifying a smallest coherence block from the plurality of coherence blocks, identifying a pilot sequence length based on the smallest coherence block, determining a plurality of orthogonal pilot sequences based on the identifying the pilot sequence length, designating, from the plurality of orthogonal pilot sequences, a first group of orthogonal pilot sequences for use in the first cell, and distributing, to each neighboring cell of a plurality of neighboring cells adjacent to the first cell, a respective group of orthogonal pilot sequences from a remainder of the plurality of orthogonal pilot sequences, to prevent pilot contamination between the first cell and the plurality of neighboring cells. Other embodiments are disclosed.

The embodiments disclosed herein include capturing images via cameras or light sensors of a mobile communication device, processing the image to determine obstructed (e.g., by foliage) portions and unobstructed (e.g., open sky) portions of the images, and generating a grid indicating the obstructed and unobstructed portions. The device may then adjust operating characteristics, such as synchronizing with a communication hub or other mobile communication device, performing a handover with the communication hub or other mobile communication device, determining transmission power or an amount to increase transmission power, selecting an antenna, determining a beam direction, determining a discontinuous reception cycle or a frequency for receiving data, providing an indication to stop or proceed through certain areas (e.g., to take advantage of areas with higher signal quality or avoid areas with lower signal quality), and the like, based on the obstructed and unobstructed portions identified in the grid.

The embodiments disclosed herein include capturing images via cameras or light sensors of a mobile communication device, processing the image to determine obstructed (e.g., by foliage) portions and unobstructed (e.g., open sky) portions of the images, and generating a grid indicating the obstructed and unobstructed portions. The device may then adjust operating characteristics, such as synchronizing with a communication hub or other mobile communication device, performing a handover with the communication hub or other mobile communication device, determining transmission power or an amount to increase transmission power, selecting an antenna, determining a beam direction, determining a discontinuous reception cycle or a frequency for receiving data, providing an indication to stop or proceed through certain areas (e.g., to take advantage of areas with higher signal quality or avoid areas with lower signal quality), and the like, based on the obstructed and unobstructed portions identified in the grid.