Capacity augmentation of 3G cellular networks: a deep learning approach
First Claim
1. A method of redistributing traffic from congested cellular towers to non-congested cellular towers in a 3G cellular network for the purpose of increasing the capacity of said cellular network wherein said cellular network comprises clusters, clusters comprise sites, and sites comprise cellular towers, and wherein the method comprises:
- a. importing per cellular tower information including neighbor handover, traffic demand, traffic carried, average transmit power, and minimum acceptable quality;
b. waiting for the expiration of a refresh timer;
c. importing additionally collected learning measurements since the previous expiration of said refresh timer;
d. applying an MLPDL technique to predict breakpoints of the plurality of both congested and non-congested cellular towers one cellular tower at a time, wherein a breakpoint reflects the maximum load limit of associated cellular tower;
e. applying inputs to the BCDSA algorithm including imported topology information and predicted breakpoints;
f. performing the BCDSA algorithm to generate CPiCH and CIO values of the plurality of both congested and non-congested cellular towers; and
g. going back to step b to wait again for the expiration of said refresh timer.
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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. Accordingly, integrated iterative method, program, and system are described aiming at maximizing the capacity of 3G cellular networks by redistributing traffic from congested cellular towers to non-congested cellular towers.
31 Citations
57 Claims
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1. A method of redistributing traffic from congested cellular towers to non-congested cellular towers in a 3G cellular network for the purpose of increasing the capacity of said cellular network wherein said cellular network comprises clusters, clusters comprise sites, and sites comprise cellular towers, and wherein the method comprises:
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a. importing per cellular tower information including neighbor handover, traffic demand, traffic carried, average transmit power, and minimum acceptable quality; b. waiting for the expiration of a refresh timer; c. importing additionally collected learning measurements since the previous expiration of said refresh timer; d. applying an MLPDL technique to predict breakpoints of the plurality of both congested and non-congested cellular towers one cellular tower at a time, wherein a breakpoint reflects the maximum load limit of associated cellular tower; e. applying inputs to the BCDSA algorithm including imported topology information and predicted breakpoints; f. performing the BCDSA algorithm to generate CPiCH and CIO values of the plurality of both congested and non-congested cellular towers; and g. going back to step b to wait again for the expiration of said refresh timer. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19)
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20. A computer program product stored in a non-transitory computer readable storage medium to redistribute traffic from congested cellular towers to non-congested cellular towers in a 3G cellular network for the purpose of increasing the capacity of said cellular network wherein said cellular network comprises clusters, clusters comprise sites, and sites comprise cellular towers, and wherein the computer program comprises:
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a. code for importing per cellular tower information including neighbor handover, traffic demand, traffic carried, average transmit power, and minimum acceptable quality; b. code waiting for the expiration of a refresh timer; c. code for importing additionally collected learning measurements since the previous expiration of said refresh timer; d. code for applying a Machine Learning Regression and an MLPDL technique to predict breakpoints of the plurality of both congested and non-congested cellular towers one cellular tower at a time, wherein a breakpoint reflects the maximum load limit of associated cellular tower; e. code for applying inputs to the BCDSA algorithm including imported topology information and predicted breakpoints; f. code for performing the BCDSA algorithm to generate CPiCH and CIO values of the plurality of both congested and non-congested cellular towers; and g. code for going back to step b to wait again for the expiration of said refresh timer. - View Dependent Claims (21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38)
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39. A system comprising processors and memory coupled to processors, the memory storing instructions readable by a computing device that, when executed by processors, cause processors to perform operations to redistribute traffic from congested cellular towers to non-congested cellular towers in a 3G cellular network for the purpose of increasing the capacity of said cellular network wherein said cellular network comprises clusters, clusters comprise sites, and sites comprise cellular towers, and wherein said operations comprise:
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a. importing per cellular tower information including neighbor handover, traffic demand, traffic carried, average transmit power, and minimum acceptable quality; b. waiting for the expiration of a refresh timer; c. importing additionally collected learning measurements since the previous expiration of said refresh timer; d. applying a Machine Learning Regression and an MLPDL technique to predict breakpoints of the plurality of both congested and non-congested cellular towers one cellular tower at a time, wherein a breakpoint reflects the maximum load limit of associated cellular tower; e. applying inputs to the BCDSA algorithm including imported topology information and predicted breakpoints; f. performing the BCDSA algorithm to generate CPiCH and CIO values of the plurality of both congested and non-congested cellular towers; and g. going back to step b to wait again for the expiration of said refresh timer. - View Dependent Claims (40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57)
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Specification