Future Wei Technologies, Inc.

Huawei Technologies Co., Limited

Yongxi Tan

Jin Yang

Nandu Gopalakrishnan

Yan Xin

Frank J Effenberger

Xiang Liu

Ning Cheng

Yanjie Fu

Iyad Alfalujah

James Mathew

Qitao Song

Kamalaharan Dushyanthan

James Matthew

Yunjun Chen

Zhangxiang Ye

An apparatus comprises a first mirror; a second mirror; a modulation layer positioned between the first mirror and the second mirror and comprising a plurality of modulation regions; a diffraction layer positioned between the modulation layer and the second mirror, and an input port admitting a light beam into the apparatus. The light beam passes through the diffraction layer and is modulated by the modulation layer to create a first modulated beam before being reflected by the first mirror, the first mirror reflecting the first modulated beam toward the second mirror, the second mirror reflecting the first modulated beam toward the modulation layer to be modulated for at least a second time.

Diffractive deep neural network (D2NN) processing using a single modulation layer

Techniques for optimizing configuration parameters in a wireless network may iteratively adjust one or more wireless configuration parameters during a first stage of an adjustment period until either a cost increase during a single iteration exceeds an upper cost threshold or (ii) the number of successive iterations exhibiting a per-iteration cost increase between the upper cost threshold and a lower cost threshold exceeds a threshold number of iterations. This may trigger the AP proceed to a second stage of the adjustment period, where the AP may adjust the same wireless configuration parameter in an opposite direction, maintain a value of the wireless parameter, or adjust a different wireless configuration parameter during the second stage. Progression between successive adjustment periods may be at least partially coordinated between APs in a group of APs.

Self-learning, adaptive approach for intelligent analytics-assisted self-organizing-networks (SONs)

A neural network is trained using deep reinforcement learning (DRL) techniques for adjusting cell parameters of a wireless network by generating a plurality of experience tuples, and updating the neural network based on the generated experience tuples. The trained neural network may be used to select actions to adjust the cell parameters. Each experience tuple includes a cell identifier, a first state, a second state, an action applied to the cell that moves the cell from the first state to the second state, a local reward, and a global reward. The neural network is updated based on whether or not each action is acceptable, which is determined based on the global reward and the local reward associated with each action.

System and method for deep learning and wireless network optimization using deep learning

The present technology provides a new approach to optimizing wireless networks, including the coverage and capacity of cellular networks, using deep learning. The proposed method involves generating a group of cells comprising a cell identified as underperforming and one or more neighboring cells, ranking the one or more neighboring cells based on one or more relationship parameters between the underperforming cell and the one or more neighboring cells, and generating a multi-dimensional multi-channel state tensor for the group of cells based on the ranking of the one or more neighboring cells. This approach to cellular network optimization improves the coverage and capacity of cellular networks using a process that is faster, more accurate, less costly, and more robust.

Optimizing cellular networks using deep learning

Convergence times associated with simulated annealing based (SA-based) optimization in wireless networks can be reduced by introducing an additional local or cell-level evaluation step into the evaluation of global solutions. In particular, new local solutions may be evaluated based on local performance criteria when the new solutions are in a global solution deemed to have satisfied a global performance criteria. New local solutions that satisfy their corresponding local performance criteria remain in the new global solution. New local solutions that do not satisfy their corresponding local performance criteria are replaced with a corresponding current local solution from a current global solution, thereby modifying the new global solution. The resulting modified global solution includes both new local solutions and current local solutions prior to being accepted as the current global solution for the next iteration.

Autonomous, closed-loop and adaptive simulated annealing based machine learning approach for intelligent analytics-assisted self-organizing-networks (SONs)

The strengths of alternative self-organizing-network (SON) techniques can be leveraged by deriving a compromise result from alternative results generated by the respective SON techniques. In particular, the compromise result may be derived from the alternative results based on reputations assigned to alternative SON techniques used to generate the respective results. The compromise result may be calculated based on weighted averages of the alternative results (e.g., solutions, diagnoses, predicted values, etc.), or on weighted averages of parameters specified by the alternative results (e.g., parameter adjustments, underlying causes, KPI values, etc.). In such an embodiment, the weights applied to the alternative results may be based on the reputations of the corresponding SON techniques used to generate the respective alternative results.

Analytics-assisted, multi-agents, self-learning, self-managing, flexible and adaptive framework for intelligent SON

Adjusting cell specific antenna configuration parameters includes receiving, at each of a plurality of radio access nodes in a network, measurement reports from a plurality of user equipment devices. Base incremental adjustments to configuration parameters of one or more antennas are performed at each radio access node in response to the measurement reports. Additional measurement reports are received from the plurality of user equipment devices after the incremental adjustments. Base incremental adjustments to the configuration parameters of the one or more antennas at the radio access nodes continue to be performed in response to the measurement reports after previous incremental adjustments until an improvement limit has occurred. Biased random adjustments to the configuration parameters of the one or more antennas at the radio access nodes are performed in response to the improvement limit until a desired optimum result is achieved.

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Hillsborough, NJ, United States of America

Yongxi Tan