Cellular Radio Network Traffic Balancing by Using RL Techniques

These days, most of the service providers optimizing network manually by referring several measurement reports/ patterns. Which is very time consuming and there is no guarantee for improvement also.

Now ML is being used for pattern detection and designing optimized telecom networks. Within AI ML, Reinforcement learning (RL) is a viable and elegant approach to yield an optimal policy for sequential decision-making problems. The tricky electromagnetic environment can be tracked by RL in a trial-and-error paradigm. Therefore, one can use a DRL net to optimize setting of LTE network configuration (parameters).

This video will help you understand how to use DRL to optimize the LTE Network configuration.

Presenter - Subhash Kalane, AI Researcher and Telecom Expert

-----------------------------------------------------------------------------------------------------------------------
CellStrat (www.cellstrat.com) is a full stack AI firm with AI Developer tools Platform - CellStrat Hub serving global AI customers!

CellStrat AI Platform (www.cellstrathub.com) allows thousands of global AI teams and developers to develop and launch AI quickly and efficiently (AI Developer Tools and AI APIs on the Cloud).

We are doing cutting-edge research in advanced AI domains such as Computer Vision, NLP, Reinforcement Learning, Graph Networks, Artificial General Intelligence etc. (http://www.cellstrat.com/research-blog/), and operationalizing these projects as AI SAAS services on the Cloud.

CellStrat is backed up a team of 250+ AI Scientists and 15000+ AI community globally.

CellStrat can be reached at contact@cellstrat.com
#AI, #ArtificialIntelligence, #Classification, #Clustering, #DecisionTrees, #NaiveBayes, #neuralnetworks, #RandomForests, #Regression, #SVM, #DL, #deeplearning, #machinelearning, #ml, #neuralnetworks, #polly, #rekognition, #tensorflow, #training, #autoencoders, #deeplearning, #dimensionality, #dimensionalityreduction, #neuralnetworks, #principalcomponentanalysis, #curseofdimensionality, #contractiveautoencoders, #denoisingautoencoders, #overcompleteautoencoders, #reconstructionerror, #reconstructionpenalty, #regularization, #regularizer, #sparseautoencoders, #featurediscovery, #feature extraction, #featurelearning, #learning, #linearregression, #logisticsigmoid, #AGI, #RL, #CNN, #GAN, #GNN, #RNN