Abstract: Load balancing of 5G cellular networks is achieved by reducing network congestion utilizing two components of learning and optimization. First, a number of learning approaches including Linear Least Square Regression (LLSR), Auto Regressive Integrated Moving Average (ARIMA), and Multi-Layer Perceptron Deep Learning (MLPDL) are used to model either Physical Resource Block (PRB) or Packet Dedicated Control CHannel (PDCCH) utilization as a function of average connected user equipment and predict the number of average users corresponding to predefined thresholds of congestion in utilizing cellular towers. Then, an optimization problem is formulated to minimize 5G network congestion subject to constraints of user quality and load preservation. Three alternative solutions, namely Constrained Simulated Annealing (CSA), Block Coordinated Descent Simulated Annealing (BCDSA), and Genetic Algorithms (GA) are presented to solve the optimization problem.

Abstract: Optimal reduction of 4G LTE cellular network congestion utilizes two components of learning and optimization. First, an MLPDL learning approach is used to model cellular network congestion measured in terms of PRB utilization and predict 80% utilization as breakpoint thresholds of cellular towers as a function of average connected user equipments. Then, an optimization problem is formulated to minimize LTE network congestion subject to constraints of user quality and load preservation. Two alternative solutions, namely Block Coordinated Descent Simulated Annealing (BCDSA) and Genetic Algorithms (GA) are presented to solve the problem. Performance measurements demonstrate that GA offers higher success rates in finding the optimal solution while BCDSA has much improved runtimes with reasonable success rates.

Abstract: Optimal enhancement of 3G cellular network capacity utilizes two components of learning and optimization. First, a pair of learning approaches are used to model cellular network capacity measured in terms of total number of users carried and predict breakpoints of cellular towers as a function of network traffic loading. Then, an optimization problem is formulated to maximize network capacity subject to constraints of user quality and predicted breakpoints. Among a number of alternatives, a variant of simulated annealing referred to as Block Coordinated Descent Simulated Annealing (BCDSA) is presented to solve the problem. Performance measurements show that BCDSA algorithm offers dramatically improved algorithmic success rate and the best characteristics in utility, runtime, and confidence range measures compared to other solution alternatives.

Abstract: Optimal reduction of 4G LTE cellular network congestion utilizes two components of learning and optimization. First, an MLPDL learning approach is used to model cellular network congestion measured in terms of PRB utilization and predict 80% utilization as breakpoint thresholds of cellular towers as a function of average connected user equipments. Then, an optimization problem is formulated to minimize LTE network congestion subject to constraints of user quality and load preservation. Two alternative solutions, namely Block Coordinated Descent Simulated Annealing (BCDSA) and Genetic Algorithms (GA) are presented to solve the problem. Performance measurements demonstrate that GA offers higher success rates in finding the optimal solution while BCDSA has much improved runtimes with reasonable success rates.

Abstract: Optimal enhancement of 3G cellular network capacity utilizes two components of learning and optimization. First, a pair of learning approaches are used to model cellular network capacity measured in terms of total number of users carried and predict breakpoints of cellular towers as a function of network traffic loading. Then, an optimization problem is formulated to maximize network capacity subject to constraints of user quality and predicted breakpoints. Among a number of alternatives, a variant of simulated annealing referred to as Block Coordinated Descent Simulated Annealing (BCDSA) is presented to solve the problem. Performance measurements show that BCDSA algorithm offers dramatically improved algorithmic success rate and the best characteristics in utility, runtime, and confidence range measures compared to other solution alternatives.

Abstract: Untapped capacity and opportunities for immediate performance improvement can be brought to light in wireless networks through the use of new predictive analytics tools and processes. By knowing the specific breaking points in the network well in advance, network adjustments can be planned and implemented in time to preserve a good customer experience. To successfully manage rapidly rising traffic, network operators can adopt a performance-based approach to capacity planning and optimization. Predictive analytics tools and processes may allow a user to view current network conditions for one or more cells in a network. The tools and processes may also allow the user to view predicted network conditions on a chosen future date for one or more cells in the network.