CLIAMS:1. A method for network resource optimization in a service area of a communication network, the method comprising: dividing the service area into a plurality of sub-areas, wherein each of the plurality of sub-areas is serviced by at least one network resource from a pre-determined number of network resources; determining a locally optimal deployment solution, comprising at least one local allocation attribute for the at least one network resource in each of the plurality of sub-areas, to meet a plurality of objectives for network resource optimization; and
obtaining a globally optimal deployment solution, comprising at least one global allocation attribute for allocation of the pre-determined number of network resources in the service area, based on the locally optimal deployment solution to meet the plurality of objectives.
2. The method as claimed in claim 1 further comprising pre-computing of a base station (BS) cell design, wherein the BS cell design comprises at least coverage attributes associated with the BS, and wherein the determining of the locally optimal deployment solution is based on the BS cell design.
3. The method as claimed in claim 1, comprising identifying whether the locally optimal deployment solution meets the plurality of objectives.
4. The method as claimed in claim 1 further comprising:
determining whether the globally optimal deployment solution meets the plurality of objectives; and
implementing the globally optimal deployment solution in the service area based on the determining.
5. The method as claimed in claim 4 further comprising modifying the pre-determined number of network resources for allocation in the service area based on the determining.
6. The method as claimed in claim 1, wherein the plurality of objectives comprises at least two of a pre-defined deployment cost, a pre-defined coverage area, and a pre-defined service-level agreement parameter.
7. The method as claimed in claim 1, wherein the dividing is based on distribution of demand nodes within the service area, wherein each of the demand nodes corresponds to a pre-determined number of users on the communication network.
8. The method as claimed in claim 1, wherein the local allocation attribute comprises at least one of a position, a height, a transmission power, an electrical tilt, a mechanical tilt, number of sectors, operating frequencies, and an azimuth, of signal transceivers associated with each of the pre-determined number of network resources.
9. The method as claimed in claim 1, wherein the global allocation attribute comprises at least one of a position, a height, a transmission power, an electrical tilt, a mechanical tilt, number of sectors, operating frequencies, and an azimuth, of signal transceivers associated with each of the pre-determined number of network resources.
10. The method as claimed in claim 1, wherein the obtaining of the globally optimal deployment solution is by iteratively processing of the locally optimal deployment solution based on at least one of an existing traffic patterns, an estimated traffic patterns, and pragmatic network conditions in the service area.
11. The method as claimed in claim 1, wherein the obtaining of the globally optimal deployment solution is based on at least one of a population density, a path loss model, a propagation path loss, a coverage area, a traffic requirement, network resource locations, a maximum transmission power, a minimum transmission power, a maximum building height in the service area, a maximum antenna height, an average path loss in the service area, a frequency re-use factor, a frequency of operation, a number of channels, a channel capacity, a maximum Erlang capacity, a maximum number of transceiver slots, a minimum receiving threshold power for a subscriber, a minimum receiving threshold power for the pre-determined number of network resources, a call blocking probability, a call drop probability, and a minimum threshold to make successful calls.
12. The method as claimed in claim 1, wherein the obtaining of the globally optimal deployment solution is based on a coverage area and a Cooperative Game Theoretic Approach.
13. The method as claimed in claim 1, wherein the determining of the locally optimal deployment solution is based on at least one local threshold value corresponding to at least one of the plurality of objectives.
14. The method as claimed in claim 1, wherein the pre-determined number of network resource is calculated based on a total estimated traffic in the service area.
15. A network optimization system (100) for network resource optimization in a service area of a communication network, the network optimization system (100) comprising: a processor (102); a local optimization module (114) coupled to the processor (102), to: divide a service area into a plurality of sub-areas, wherein each of the plurality of sub-areas is serviced by at least one network resource from a pre-determined number of network resources; and
determine a locally optimal deployment solution comprising at least one local allocation attribute for the at least one network resource in each of the plurality of sub-areas, to meet a plurality of objectives for network resource optimization; and
a global optimization module (116) coupled to the processor (102), to obtain a globally optimal deployment solution comprising at least one global allocation attribute for allocation of the pre-determined number of network resources in the service area, based on the locally optimal deployment solution to meet the plurality of objectives.
16. The network optimization system (100) as claimed in claim 15 further comprising an initial network planning module (112) coupled to the processor (102), to pre-compute BS cell design, wherein the BS cell design comprises coverage attributes associated with the BS, and wherein the local optimization module (114) determines the locally optimal deployment solution, based on the BS cell design.
17. The network optimization system (100) as claimed in claim 15 further comprising, a decision module (118) coupled to the processor (102), to: determine whether the globally optimal deployment solution meets the plurality of objectives; and
implement the globally optimal deployment solution based on the determining.
18. The network optimization system (100) as claimed in claim 17 further comprising, a planning update module (120) coupled to the processor (102), to modify the pre-determined number of network resources based on the determination, for allocation in the service area.
19. The network optimization system (100) as claimed in claim 15, wherein the local optimization module (114) determines the locally optimal deployment solution, based on a path loss based clustering approach, based at least on propagation path loss.
20. The network optimization system (100) as claimed in claim 19, wherein the global optimization module (116) obtains the globally optimal deployment solution based on a coverage area and a Cooperative Game Theoretic Approach.
21. A non-transitory computer readable medium having a set of computer readable instructions that, when executed, cause a computing system to: divide a service area into a plurality of sub-areas, wherein each of the plurality of sub-areas is serviced by at least one network resource from a pre-determined number of network resources; determine a locally optimal deployment solution comprising at least one local allocation attribute for the at least one network resource in each of the plurality of sub-areas, to meet a plurality of objectives for network resource optimization; and
obtain a globally optimal deployment solution comprising at least one global allocation attribute for allocation of the pre-determined number of network resources in the service area, based on the locally optimal deployment solution to meet the plurality of objectives. ,TagSPECI:As Attached