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University of Colorado – 2500MHz WiMAX RF Plan Airspan RF Planning January 2011 V1.1.

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Presentation on theme: "University of Colorado – 2500MHz WiMAX RF Plan Airspan RF Planning January 2011 V1.1."— Presentation transcript:

1 University of Colorado – 2500MHz WiMAX RF Plan Airspan RF Planning January 2011 V1.1

2 2 Commercial in Confidence WiMAX 16e – MacroMAXe RF Inputs DTM / Clutter 5 Meter Resolution Heights and Clutter Propagation ModelCRC Predict 4.x deterministic; MODEL IS NOT CALIBRATED Frequency Band2500 MHz RF Channels10 MHz Channel BW, 3 Channels Duplexing MethodTDD Base Station TypeMacroMAXe 3.6GHz Base Station Tx Power40 dBm at antenna port; 43 dBm Combined Tx Power Base Station SectorizationMulti-sector using 90-deg external antenna Base Station Tx HeightAs specified in specs. Base Station Antenna Type, Gain90-deg AW3008 2.5GHz T4, 17dBi Base Station Antenna Downtilt4-deg Electrical; 1 to 4-deg Mechanical Advanced Radio/Antenna TechniquesDL MIMO (2TX/2RX) and UL MRC (1TX/4RX) CPE Type 1, Tx Power, Ant Ht, Ant Type, Gain, EnvironmentMiMAX-Easy, 27dBm, 3m AGL, Omni-external, 3dBi, Mobile Outdoor - On Vehicle Planning ToolMP Planet 5.2 Site Count3 BS Sector Count5 Sectors

3 3 Commercial in Confidence DTM Layer

4 4 Commercial in Confidence Clutter Layer

5 5 Commercial in Confidence Network Layout

6 6 Commercial in Confidence Frequency Plan

7 7 Commercial in Confidence Frequency and Preamble Plan SiteSectorChannel IDPreambleDownlink Perm BaseUplink Perm Base Darley Twr1WimaxTddBand_37302 Engg1WimaxTddBand_27720 Engg2WimaxTddBand_37411 Gamow Twr1WimaxTddBand_17534 Gamow Twr2WimaxTddBand_27643 Channel No. Channel ID Center Frequency (MHz) Bandwidth (MHz) 1WimaxTddBand_12508.50010 2WimaxTddBand_22518.50010 3WimaxTddBand_32528.50010

8 8 Commercial in Confidence BS and Sector Details SiteSectorLongitudeLatitudeAntenna TypeHeight (m)AzimuthMechanical Tilt Darley Twr1-105.252086139.99828894AW3008_90DEG_QUAD_Fixed_Tilt_2.5GHz473504 Engg1-105.263340140.00728204AW3008_90DEG_QUAD_Fixed_Tilt_2.5GHz441202 Engg2-105.263340140.00728204AW3008_90DEG_QUAD_Fixed_Tilt_2.5GHz442402 Gamow Twr1-105.267804840.00815396AW3008_90DEG_QUAD_Fixed_Tilt_2.5GHz42901 Gamow Twr2-105.26780540.008154AW3008_90DEG_QUAD_Fixed_Tilt_2.5GHz422502

9 9 Commercial in Confidence Network Analysis Layers Description Best Server Signal Strength - This layer provides the downlink signal strength expressed in dBm for the best serving sector and for the chosen subscriber equipment. The best server is determined from the best signal CNIR of the preamble signal. Best Server - This layer provides the downlink coverage area for the sector with the best preamble signal CNIR. Downlink MCS - This layer provides information on the downlink modulation that has the highest spectral efficiency, i.e., the modulation that provides the highest useful bits per symbol ratio and where the coverage probability is above the defined target cell edge coverage probability. Uplink MCS - This layer provides information about the best uplink modulation that offers the highest spectral efficiency, i.e., the modulation that provides the highest useful bits per symbol ratio. This layer only uses a fraction of all available sub-channels to illustrate uplink UL MCS coverage. Downlink C/(N+I) - This layer provides the downlink C/(N+I) value of the best channel where C is computed based on the data or traffic power. Uplink C/(N+I) - This layer provides the uplink C/(N+I) value of the best channel where C is computed based on the data or traffic power.

10 10 Commercial in Confidence NETWORK ANALYSIS PLOTS

11 11 Commercial in Confidence Best Server Signal Strength

12 12 Commercial in Confidence Best Serving Sector

13 13 Commercial in Confidence Downlink MCS

14 14 Commercial in Confidence Uplink MCS UL – 10 Subchannels

15 15 Commercial in Confidence Downlink C/(N+I)

16 16 Commercial in Confidence Uplink C/(N+I) UL – 10 Subchannels

17 17 Commercial in Confidence BS Sector Antenna 90-deg AW3008 2500MHz Fixed 4-deg Tilt

18 18 Commercial in Confidence RF Plan Notes: Propagation Modelling Propagation models simulate how radio waves travel through the environment from one point to another. Because of the complex nature of propagation modelling and the great amount of information needed to perform an accurate estimation of path loss, there will always be differences between the path loss estimation of a model and real-world measurements. Nevertheless, some models are inherently more accurate than others in specific situations, and it is always possible to refine a model (or its understanding of the environment) so that it better matches the real world. There are several things that can be done in order to minimize discrepancies between the propagation model and the real world, including choosing an appropriate model and calibrating it effectively. This study uses the CRC-Predict 4.x propagation model. CRC-Predict is the most widely used propagation model in the suite of radio-wave prediction algorithms available in Mentum Planet. Originally developed by the Communications Research Centre (Ottawa, Canada), CRC-Predict is now developed by Mentum. Some traditional approaches to radio- wave propagation are empirical in nature and begin with the collection of real-world measurements, fitting them to curves and then applying the curves to similar geographic areas. The limitation of these approaches is that they cannot take into account the infinite variety of landscapes that can occur. In contrast, CRC-Predict is a deterministic model based on Physical Optics, a form of wave theory. Predictions are based on a detailed simulation of diffraction over terrain (including clutter), and include an estimate of local clutter attenuation. As a result, predictions of coverage gaps and interference areas are based specifically on the particular terrain in question and are more likely to be accurate, given that the terrain and clutter data are accurate. Drive-test measurements are still required for reliable planning, but their use is more a matter of compensating for the incompleteness/inaccuracy of clutter data and adjustment of the model’s clutter property assignments to increase accuracy. This is called model tuning.

19 19 Commercial in Confidence www.airspan.com


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