Clutter Data Assists System Design

With literally billions of dollars being spent annually on building wireless-communications systems, there is significant incentive to devise engineering tools that can design and plan such systems accurately and efficiently. Considering that a single-cell-system base station can cost several hundred thousand dollars, an efficient system design, which eliminates just one such station, can justify the expense of the design tool and the effort of using it. As the wireless system grows to meet increasing and changing demands for service, the design tool is again a valuable asset in planning optimum system modifications to accommodate growth.

PROPAGATION MODELS Of fundamental importance to the wireless-system-design tool is the ability to predict the strength of radio signals accurately from the various system transmitters. The mathematical algorithms used for prediction generally are known as propagation models.

Traditionally, propagation models have relied on terrain-elevation data as the sole environment parameter on which to base predictions. Substantial effort has been invested over the past several years in developing accurate digital terrain-elevation models for various parts of the world. Although terrain has a profound effect on the propagation of radio signals (especially at higher frequencies), more localized environmental features, such as trees, buildings and houses, also can have a substantial effect.

An important trend in wireless-system architecture is to use microcells and much higher radio frequencies where significantly greater transmission bandwidth is available. These broadband point-to-multipoint systems are used for carrying video, voice and data signals to fixed rather than mobile locations. For such short-range microcell system architectures where the coverage radius of the transmitter may range from 0.5km to 5km, the terrain often can be regarded as locally flat. In this case, signal propagation is dominated by local obstructions rather than by terrain, making the description and accurate classification of clutter (land use/land cover data) of primary importance.

Atmosphere and terrain have been included for many decades in the propagation models that are designed to predict the strength of radio signals. Only recently has propagation modeling attempted to incorporate information about foliage and structures and their impact on signal strength.

Although the signal arriving at the receiver will include some reflections from the terrain, the dominant signal is the one that arrives directly from the transmitter. Because of the tree, the signal is some 8dB weaker than it is without the tree at a frequency of 870MHz. This is the effect of clutter on signal propagation.

CLASSIFYING CLUTTER DATA Acquiring databases describing the location and characteristics of every tree or building is generally not practical (or necessary) for most frequencies. However, for new point-to-multipoint microwave systems such as LMDS at 28GHz, the exact height and location of buildings can be essential to system design. Considerable effort is now under way to develop such building databases using aerial photography and satellite imagery and LIDAR (light detection and ranging).

Therefore, to simplify the problem of accounting for clutter data in propagation prediction, you need to classify the data in some way. For example, instead of considering every tree in a forest, a wider area would be classified as "forest" and any receiver located within that area would experience an additional signal-strength loss of 8dB at 870MHz. A similar approach could be used to find the loss at other frequencies. If signal-loss data also were available for different types of forest, multiple clutter classifications for forests would be appropriate.

Many land-use classification schemes provide for a large number of categories that may be appropriate for land-use planning or zoning. However, unless you have calculations or measurements that demonstrate a statistically significant difference between categories in terms of their relative impact on radio signals, having the additional categories does not provide more accurate signal predictions.

SIGNAL-STRENGTH STUDIES To illustrate the role of clutter data on signal-strength prediction, three signal studies were performed. The area for the signal study is Eugene, OR, which is at the southern end of the Willamette Valley.

Near the city center, elevation is approximately 120 meters. Eugene is surrounded on the west, south and east by hills with elevations as high as 850 meters. Major roads of the area are shown.

The first signal study was performed using only terrain as a factor in determining signal strength. The transmitter is located in Coburg Hills, at an elevation of 570 meters. This transmitting station uses a directional antenna, which directs most of its power to the southwest over Eugene (a pointing azimuth of 225 degrees). The receive antenna is a low-gain omnidirectional antenna. By systematically positioning the receive antenna at a uniform grid of locations throughout the service area around the transmitting antenna and calculating signal strength at each location, a map grid is developed in which the different signal-level ranges are depicted by different colors.

Notice that the signal patterns are strongly affected by the terrain. The strongest signals occur in the lowest elevations between the transmitter and receiver, where there is little to obstruct the signal. The weakest signals generally occur due to shadowing -- the signal is greatly weakened due to an obstruction, such as a hill. It has been diffracted over the top and around the sides. In the shadow cast by the hill, the strength of the signal is diminished. The southwest portion of the signal study illustrates this point.

TERRAIN ELEVATION & CLUTTER TYPE Forest land covers the majority of the area surrounding Eugene. Agricultural land covers a large portion of the Willamette Valley.

A signal study takes into account signal attenuation due to clutter as well as terrain. The area that has the largest reduction in signal strength is the urban area. This is due to reflection, scattering and absorption by the urban and residential structures. The shadowing patterns caused by hills are basically the same. Also, along the main roads of Eugene, which are flanked by commercial and industrial usage, the signal has weakened. In the southeast and southwest quadrants of the study area where the clutter is forest land, the signal has weakened due to scattering of the radio waves by leaves and branches.

TERRAIN ELEVATION & CLUTTER HEIGHT In Eugene, "dense urban" has a different feel than in Manhattan. Average "dense urban" height in Eugene is around 15 meters; in Manhattan it is closer to 40 meters. Assigning an average clutter height can give a sense of this difference. Clutter categories were assigned average height values in order to give an idea of potential blockage. For example, forested areas were assigned a value of 25 meters, residential 5 meters, mixed urban 15 meters, and commercial/industrial 12 meters. This further refinement of the clutter changes the signal-prediction pattern.

>From these examples, it is clear that clutter data plays an important role >in wireless-system design. Adding this information can provide an >important refinement to a signal study by taking into account a more >accurate and realistic characteristic of the service area where the >wireless system will be used.

LAND-USE DATA TO PREDICT TRAFFIC Wireless networks must be configured so that enough channels are allocated to handle the demand. Considerations in system design are: locating transmitters to provide adequate signal levels throughout the service area, judging where system capacity may be insufficient (or underused), and in the case of cellular systems, gauging how the system may have to evolve to accommodate changing call-traffic patterns.

These dynamic call-traffic patterns refer to time and location. People are placing calls at random times throughout the day, and because callers are mobile, they may be located anywhere in the system service area. Large, temporary gatherings of users must be considered. During work hours, call traffic is highest in the city centers. But as wireless cable, telephone and data transmission (fax and computer modem) services are deployed, wireless traffic in residential areas will increase dramatically.

To determine the number of radio channels that is needed by each cell site in order to handle the call traffic, it is necessary to somehow establish a geographical distribution for the calls relative to the cell sites. Planners traditionally have accounted for traffic in one of four ways:

* Assume geographically uniform traffic distribution (in the absence of other data).

* Use a standard demographic database and assume that more capacity is needed where population density is highest.

* Use a road database as a guide for defining traffic and the geographic location of where calls are originated.

* Use actual call-traffic patterns, which vary by location and time of day. This is the best indicator of traffic demand, but is difficult to obtain because this information is generated in the switches of existing cellular systems whose operators do not release this data for proprietary reasons.

A fifth alternative for predicting where usage is highest throughout the day is to use up-to-date land-use classifications. By seeing where transportation, urban, dense urban and residential categories are located and correlating this information with call-density trends, a picture of call traffic will emerge.

USING CLUTTER DATA Land use/land cover (clutter) data can have many uses in wireless-system design. The most significant application is in refining signal-level predictions to take into account local-clutter-attenuation effects due to clutter type and clutter height. Using clutter data can offer clear advantages for signal-level predictions. However, the quality of the information regarding clutter categories and the signal-attenuation levels for various frequencies in each category will determine the ultimate value of land-use data. Given the current level of engineering information about such signal attenuation, typically only five or six categories of clutter information can be used to provide statistically significant distinctions in signal-level-attenuation prediction. Obtaining clutter data with a large variety of clutter classifications may be an unnecessary expense and slow down processing time because of large file sizes.

Through the use of modern wireless-system-engineering software, the system designer can not only prepare accurate predictions of service and interference areas, but also can rapidly evaluate a wide variety of strategies to arrive at a configuration that best meets the service providers' objectives at the least cost.

Land-use data also can play an important role in projecting demand for wireless services. With an increasing trend toward fixed, broadband wireless services, land-use data used with building-height and location data can provide the wireless-system designer with a valuable understanding of where service will be required and at what capacity. In this regard, land-use data, which is as up-to-date as possible, is critical. Zoning data that shows a community's plans for future growth also can be applied in the design process.