Cost based model for wireless architecture

• US 6,393,290 B1
• Filed: 06/30/1999
• Issued: 05/21/2002
• Est. Priority Date: 06/30/1999
• Status: Expired due to Term

• Current Assignee: Lucent Technologies, Inc. (Nokia Corporation)
• Original Assignee: Lucent Technologies, Inc. (Nokia Corporation)
• Inventors: Ufongene, Charles M.
• Primary Examiner(s): Urban, Edward F.
• Assistant Examiner(s): Mehrpour, Naghmeh
• Application Number:
  US09/343,556
• Time in Patent Office: 1,056 Days
• Field of Search: 455/422, 455/446, 455/449, 455/67.1, 455/67.6, 455/447, 455/67.7
• US Class Current: 455/446
• CPC Class Codes: H04W 16/18 Network planning tools
• CPC Class Codes: H04W 16/20 for indoor coverage or short range network deployment

Abstract

A cost modeling technique derives node, link, and end-to-end costs for a communication network design as a function of radio frequency, demographic, traffic, system, and marketing variables, thereby enabling a comprehensive characterization of network cost in terms of these factors. A cost-sensitivity analysis tool allows a user to vary network parameters, track the impact of such variables on network node, link, and end-to-end cost; and recognize the variation of cost as a function of different input variables, thus facilitating design. One implementation models a fixed wireless access network, providing a cost estimate for provisioning service bandwidth to buildings of a service area which is divided into a number of cells. The size of each cell is defined as the minimum of radio range—a function of the modulation scheme implemented by the base station transmitter; rain radius—a function of rain zone, rain availability requirements, signal polarization, and receiving antenna gain; and hub capacity radius—a function of allocated bandwidth per cell, spectral efficiency, service bandwidth requirements, building density, and penetration rates. The cost of the feeder network and end-to-end network cost is considered as a function of cell size to indicate the effect of input variables which constrain cell size on end-to-end network cost.

Claims

1. A method of modeling costs of a communications network, said network being designed to serve subscribers in a geographic area which is partitioned into a number of cells, said method comprising:

defining a set of input variables which impact cell size;

determining cell size based on at least one of said input variables by taking the minimum of three constraining radii;

(1) a radio range radius;

(2) a rain radius; and

(3) a hub capacity radius; and

modeling network implementation costs as a function of cell size.

2. The method of claim 1, wherein said modeling step models total network implementation cost as a number of additive components, including a cell hub cost component, a feeder network cost component, an end user cost component, and a service node cost component.

3. The method of claim 1, wherein

the radio range radius is a function of the modulation technique utilized by a cellular base station;

the rain radius is a function of rain zone, rain availability requirements, and end node antenna gain; and

the hub capacity radius is a function of modulation spectral efficiency, service bandwidth requirements, and subscriber density.

4. The method of claim 3, wherein the hub capacity radius is determined by calculating:

$R_{\mathrm{hub}}=\{\begin{array}{cc}\sqrt{\frac{0.54<br><br>\text{\hspace{1em}<br><br>}<br><br>B_{\sec }<br><br>N_{\sec }<br><br>S_{\mathrm{eff}}}{2.6<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{\delta <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>D_b<br><br>{\mathrm{T1}}_b}}& \mathrm{for}<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{T1}<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{access}\\ \sqrt{\frac{12.96<br><br>\text{\hspace{1em}<br><br>}<br><br>B_{\sec }<br><br>N_{\sec }<br><br>S_{\mathrm{eff}}}{2.6<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{\delta <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>D_b<br><br>{\mathrm{T1}}_b}}& \mathrm{for}<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{nxDS0}<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{access}\end{array}$

where, R_hub is hub radius, N_sec is a number of hub sectors, B_sec is bandwidth per sector, S_eff is spectral efficiency, δ

is subscriber penetration rate, D_b is building density, and T1_b is required bandwidth per building.

5. The method of claim 1, wherein said communication network is a fixed wireless access network.

6. The method of claim 1, wherein said cost variation output is a cost sensitivity plot.

7. The method of claim 6, wherein said at least one input variable includes the modulation technique which is utilized to transmit from a cellular base station to subscribers.

8. The method of claim 6, wherein said at least one input variable includes the antenna type utilized by a subscriber node to receive signals from a cellular base station.

9. The method of claim 6, wherein said at least one input variable includes rain availability requirements.

10. The method of claim 6, wherein said cost sensitivity plot indicates total network cost impact of at least one input variable which affects cell radius.

11. The method of claim 2, wherein said feeder network cost component is modeled as a function cell radius.

12. An apparatus for modeling costs of a communications network, said network being designed to serve subscribers in a geographic area which is partitioned into a number of cells, said apparatus comprising:

input means for defining a set of input variables which impact cell size;

calculating means for determining cell size based on at least one of the input variables by taking the minimum of three constraining radii;

(1) a radio range radius;

(2) a rain radius; and

(3) a hub capacity radius; and

modeling means for modeling network implementation costs as a function of cell size.

13. The apparatus of claim 12, wherein said modeling means models total network implementation cost as a number of additive components, including a cell hub cost component, a feeder network cost component, an end user cost component, and a service node cost component.

14. The apparatus of claim 12, wherein

the radio range radius is a function of the modulation technique utilized by a cellular base station;

the rain radius is a function of rain zone, rain availability requirements, and end node antenna gain; and

the hub capacity radius is a function of modulation spectral efficiency, service bandwidth requirements, and subscriber density.

15. The apparatus of claim 12, wherein the hub capacity radius is determined by calculating:

$R_{\mathrm{hub}}=\{\begin{array}{cc}\sqrt{\frac{0.54<br><br>\text{\hspace{1em}<br><br>}<br><br>B_{\sec }<br><br>N_{\sec }<br><br>S_{\mathrm{eff}}}{2.6<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{\delta <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>D_b<br><br>{\mathrm{T1}}_b}}& \mathrm{for}<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{T1}<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{access}\\ \sqrt{\frac{12.96<br><br>\text{\hspace{1em}<br><br>}<br><br>B_{\sec }<br><br>N_{\sec }<br><br>S_{\mathrm{eff}}}{2.6<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{\delta <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>D_b<br><br>{\mathrm{T1}}_b}}& \mathrm{for}<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{nxDS0}<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{access}\end{array}$

where, R_hub is hub radius, N_sec is a number of hub sectors, B_sec is bandwidth per sector, S_eff is spectral efficiency, δ

is subscriber penetration rate, D_b is building density, and T1_b is required bandwidth per building.

16. The apparatus of claim 12, wherein said communication network is a fixed wireless access network.

17. The apparatus of claim 12, wherein said cost variation output is a cost sensitivity plot.

18. The apparatus of claim 17, wherein said at least one input variable includes the modulation technique which is utilized to transmit from a cellular base station to subscribers.

19. The apparatus of claim 17, wherein said at least one input variable includes the antenna type utilized by a subscriber node to receive signals from a cellular base station.

20. The apparatus of claim 17, wherein said at least one input variable includes rain availability requirements.

21. The apparatus of claim 17, wherein said cost sensitivity plot indicates total network cost impact of at least one input variable which affects cell radius.

22. The apparatus of claim 13, wherein said feeder network cost component is modeled as a function cell radius.

23. A computer readable medium storing program code for modeling costs of a communications network, said network being designed to serve subscribers in a geographic area which is partitioned into a number of cells, wherein, when the stored program is executed on a computer, the computer executes an operation comprising:

defining a set of input variables which impact cell size;

determining cell size based on at least one of the input variables by taking the minimum of three constraining radii;

(1) a radio range radius;

(2) a rain radius; and

(3) a hub capacity radius; and

modeling network implementation costs as a function of cell size.