System and method for reduction of cost of ownership for wireless communication networks

• US 8,073,720 B2
• Filed: 08/08/2006
• Issued: 12/06/2011
• Est. Priority Date: 04/18/2006
• Status: Expired due to Fees

• Current Assignee: Cerion Optimization Services, Inc.
• Original Assignee: Cerlon Optimization Services Incorporated
• Inventors: Egner, Will A., Yuan, Wei, Liu, Henry, Wank, Richard, Griffith, Peter, Li, Dongdong
• Primary Examiner(s): Wu, Rutao
• Application Number:
  US12/297,428
• Publication Number:
  US 20090106064A1
• Time in Patent Office: 1,946 Days
• Field of Search: None
• US Class Current: 705/7.11
• CPC Class Codes: G06Q 10/063 Operations research, analysis or management
• CPC Class Codes: G06Q 10/0631 Resource planning, allocation, distributing or scheduling for enterprises or organisations
• CPC Class Codes: G06Q 10/06316 Sequencing of tasks or work
• CPC Class Codes: G06Q 10/06375 Prediction of business process outcome or impact based on a proposed change
• CPC Class Codes: H04W 16/00 Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
• CPC Class Codes: H04W 16/18 Network planning tools
• CPC Class Codes: H04W 92/02 Inter-networking arrangements

Abstract

Systems and methods to assist wireless telecom service providers to determine the optimal core network evolution plan in a network planning time period. Total cost of ownership of wireless core networks is generally modeled to include capital expenditure, customer acquisition and retention cost, network operational cost and staffing and engineering cost. The total cost of ownership model is used to determine the optimal core network evolution plan at different layers of the core networks, which are modeled as a number of abstract networks in mathematical terms. A core network evolution optimization algorithm determines the optimal number of core network configurations and when to deploy the configurations in the planning period in order to minimize the total cost of ownership. A multi-level serving area optimization algorithm determines an optimal core network configuration at a given point of time.

Claims

1. A method of reducing a total cost of ownership for a wireless telecommunications core network, the method comprising:

determining, by a computer system, an optimal core wireless network evolution plan for a network planning period;

determining, by the computer system, an optimal core wireless network equipment count for supporting a first amount of wireless network traffic; and

determining, by the computer system, an optimal core wireless network configuration for supporting the first amount of wireless network traffic, wherein the total cost of ownership includes capital expenditures, operational cost, customer acquisition and retention (CAR) cost, and staffing and engineering cost, and wherein the CAR cost is based on unsupported traffic and inter-system handovers.

2. The method of claim 1, wherein the capital expenditures and costs are determined using net present value or annualized payments.

3. The method of claim 1, wherein the capital expenditures for the core network include core network equipment costs.

4. The method of claim 1, wherein the operational cost for the core network includes a leasing cost of facilities.

5. The method of claim 4, wherein the facilities include transport facilities and real estate facilities.

6. The method of claim 1, further comprising:

basing the CAR cost on statistical models of subscriber churn historical data;

basing the subscriber churns on statistical models of deny-to-access rate and predicted call drops historical data; and

basing the deny-to-access rate and predicted call drops on the unsupported traffic and the inter-system handovers, respectively.

7. The method of claim 1, wherein the staffing and engineering cost for the core network is based on network node re-parenting efforts involved in transactions between network configurations.

8. The method of claim 1, wherein the optimal core network evolution plan includes a plurality of core network configurations, each implemented at a particular time during the network planning period, and which together provide a lowest total cost of ownership in the network planning period.

9. The method of claim 1, further comprising a multi-level serving area optimization that determines a minimal core network equipment count for supporting predicted network traffic at any given time during the network planning period.

10. The method of claim 9, wherein the multi-level serving area optimization is based on the determined optimal core network equipment count.

11. The method of claim 8, wherein the determining the optimal core network evolution plan operates on different levels of the wireless core network in a bottom-up fashion.

12. The method of claim 11, wherein the determining the optimal core network evolution plan further comprises:

determining a first optimal core network evolution plan at a BSC/RNC level based on predicted network traffic demands from BTS sites; and

determining a second optimal core network evolution plan at an MSC level and at an SGSN level based on traffic demand results from the first optimal core network evolution plan.

13. The method of claim 9, wherein the multi-level serving area optimization splits the wireless core network into a number of abstract networks, each for a core network level, where a network node is a node in the abstract network, traffic demands from the network node are node weights in the abstract network, a network node adjacency between two adjacent network nodes is an edge in the abstract network, mobility between adjacent network nodes is an edge weight in the abstract network, and a serving area is a sub-network in the abstract network.

14. The method of claim 10, wherein the multi-level serving area optimization generates a sequence of network models based on the wireless core network, the optimization comprising:

generating an network model by combining adjacent nodes in each serving area in the wireless core network;

determining whether traffic demand on each node of the network model is over a selected fraction of a capacity of equipment for a serving area in which the node is located; and

repeating the generating the network model and the determining the traffic demand until the traffic demand on each node is over the selected fraction.

15. The method of claim 14, wherein the multi-level serving area optimization determines the core network equipment count in a last-generated network model by adding new equipment until the average equipment utilization is within engineering limits for the network.

16. The method of claim 14, wherein the multi-level serving area optimization further comprises:

partitioning a last-generated network model to determine a core network configuration with a plurality of serving areas;

mapping the serving areas back to an immediately prior network model;

partitioning the prior network model to refine the serving areas;

repeating the mapping the serving areas and the partitioning the prior network model until the partitioning is performed on the network model abstracted from the wireless core network; and

mapping final serving areas on the optimal network configuration.

17. The method of claim 1, wherein the determining the optimal core network evolution plan comprises determining an optimal number of core network configurations and when to deploy the configurations in the network planning period in order to minimize the total cost of ownership.

18. The method of claim 17, wherein the determining the optimal core network evolution plan comprises:

determining a time in the planning period when the core network experiences a highest level of predicted traffic;

applying a multi-level serving area optimization to determine a first optimal core network configuration for the highest traffic month;

applying the first optimal core network configuration with optimal serving areas to all months in the planning period; and

inserting intermediate core network configurations at intermediate times in the planning period by applying the multi-level serving area optimization.

19. The method of claim 1, wherein the inter-system handovers include inter-base station controller handovers.

20. The method of claim 1, wherein the inter-system handovers include inter-mobile switching center handovers.

21. The method of claim 1, wherein the inter-system handovers include inter-base station controller and inter-mobile switching center handovers.

22. A method of reducing a total cost of ownership for a wireless telecommunications core network, the method comprising:

determining, by a computer system, an optimal core wireless network evolution plan for a network planning period;

determining, by the computer system, an optimal core wireless network equipment count for supporting a first amount of wireless network traffic; and

determining, by the computer system, an optimal core wireless network configuration for supporting the first amount of wireless network traffic, wherein the total cost of ownership includes capital expenditures, operational cost, customer acquisition and retention (CAR) cost, and staffing and engineering cost, and wherein the staffing and engineering cost for the core network is based on network node re-parenting efforts involved in transactions between network configurations.

23. A method of reducing a total cost of ownership for a wireless telecommunications core network, the method comprising:

determining, by a computer system, an optimal core wireless network evolution plan for a network planning period;

determining, by the computer system, an optimal core wireless network equipment count for supporting a first amount of wireless network traffic;

determining, by the computer system, an optimal core wireless network configuration for supporting the first amount of wireless network traffic; and

determining, by a multi-level serving area optimization, a minimal core network equipment count for supporting predicted network traffic at any given time during the network planning period, wherein the multi-level serving area optimization splits the wireless core network into a number of abstract networks, each for a core network level, where a network node is a node in the abstract network, traffic demands from the network node are node weights in the abstract network, a network node adjacency between two adjacent network nodes is an edge in the abstract network, mobility between adjacent network nodes is an edge weight in the abstract network, and a serving area is a sub-network in the abstract network.

24. A method of reducing a total cost of ownership for a wireless telecommunications core network, the method comprising:

determining, by a computer system, an optimal core wireless network evolution plan for a network planning period;

determining, by the computer system, an optimal core wireless network equipment count for supporting a first amount of wireless network traffic;

determining, by the computer system, an optimal core wireless network configuration for supporting the first amount of wireless network traffic; and

determining, by a multi-level serving area optimization, a minimal core network equipment count for supporting predicted network traffic at any given time during the network planning period, wherein the multi-level serving area optimization is based on the determined optimal core network equipment count, and wherein the multi-level serving area optimization generates a sequence of network models based on the wireless core network, the optimization comprising

(a) generating an network model by combining adjacent nodes in each serving area in the wireless core network;

(b) determining whether traffic demand on each node of the network model is over a selected fraction of a capacity of equipment for a serving area in which the node is located; and

(c) repeating the generating the network model and the determining the traffic demand until the traffic demand on each node is over the selected fraction.