The signal power of FBS leaked to outside of the building depends on the floor plan of the building and position of the FBS. A transmitter seated near a window would have the most powerful signal outside, whereas one placed in an enclosed room has a limited effect on macro users outside. In this section, we study the effect of FBS’s position and find the best possible position that minimizes the expected interference. The interference at a particular point can be minimized by;
Im = min
xt Z
c
I(xt, xr)fXr(xr)dc, (3.12)
wherext andxr represent transmitter and receiver coordinates, respectively. I(xt, xr) is the interference atxr caused by a transmitter atxt, andfXr(xr) is the probability density function of the receiver position. The interference is to be minimized on c which is defined as a circle around house with radius R centred at the center of the house. Assuming equal probability of receiving signal at each angle, we may write,
Im = min xt 1 2π 2π Z 0 I(xt,[R, θ])dθ. (3.13)
For every realization in the building database, an extensive search on every possible transmitted point results in the optimal position. The diagram in Fig. 3.5 illustrates the expected interference at some potential FBS’s position. FBSs are represented by red crosses. The radius of each circle is proportional to the expected interference. Large circles show high level of interference, whereas small circles indicates that little interference is expected, which could be potential candidates for placing a FBS. High level of interference is expected around the windows, which makes this regions as the worst for placing FBS. On the other hand, enclosed areas inside the house with no windows, like corridors or corner of rooms have a better chance of limiting interference to outside. To study the effect of FBS’s location, the calculations are repeated for 500 realizations and the interference is measured on a circle with radius equal to 15 m.
Tx
Interference diagram:
Circle radius α Expected Interference
Figure 3.5: Expected signal attenuation map of a sample house.
−880 −86 −84 −82 −80 −78 −76 0.1 0.2 0.3 0.4 Path loss (dB) Frequency
Figure 3.6: The histogram ofIm.
The histogram of the expected path loss (Im) of a transmitter placed at the optimum point is shown in Fig. 3.6.
The average value of the path loss for optimal position over all realizations is 80.4 dB. Considering 62 dB path loss at the distance of 15 m, the mean excess path loss due to building is worked out to be 18.4 dB. The simulations also show that the expected interference can increase up to 22.9 dB by moving the FBS in the house. These values can be used as a rule of thumb to consider the attenuation effects of the building.
3.6
Chapter Summary
This chapter presented a novel model that considers the complex effects of buildings’ floor plan on the signal power. The proposed model, namely Building Architectural Model (BAM), predicts the power of the femtocell signal leaking outdoors. It con- siders the main effects of the buildings, including signal attenuation due to passing through walls/windows/doors and a mixed indoor-outdoor channel. The model is based on widely accepted propagation models. However, validation of the results by measurements would be highly desirable.
The model can be used to derive several performance metrics of the network. Moreover, it can be used by the network operator to predict the effect of the instal- lation of new femtocells on its network. It is also used as a basis for a composite shadowing/multipath fading model derived in Chapter 5. As an application of the model, it was used in Section 3.5 to minimize the femtocell power leakage outside the building by optimizing the FBS’s placement and its interference. The achieved re- sults confirmed that a proper placement of a FBS can decrease the mean interference around the house by as much as 23 dB.
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//www.statcan.gc.ca/
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Chapter 4
Novel Algorithm for Real-time Procedural
Generation of Building Floor Plans
4.1
Preamble
Dynamic generation of virtual environments gained popularity in the last decade. For example, in modern games, generating dynamic virtual environments for each round of the game allows even the savvy gamers to enjoy the game endlessly. A lot of games’ scenes take place in a town which has to be generated either manually or with a rule- based procedure. In some massive multiplayer games, producing the whole world is required. Besides the scale of the virtual environment, its details are also essential. Creating environments where the player can go inside the buildings adds a layer of sophistication which multiplies its complexity. As a result, creating and managing such environments constitute a large portion of game design process.
A model describing the architecture of a city or inside the buildings can also be used in different disciplines for various applications. As an instance, such an algorithm can be used to statistically model the signal propagation in built-up areas or used as a hypothetical signal propagation benchmark which can be customized to adapt to different scenarios. The model developed in this Chapter has been used in the works presented in Chapter 3 and Chapter 5 to statistically model the propagation of a radio signal transmitted from an indoor transmitter to outside, which extremely depends on the floor plan of the building that encloses the transmitter.
In this chapter, we introduce a novel procedural floor plan generation for subur- ban houses. The remaining of the chapter is organized as follows. Section 4.2 reviews some of the previous work related to automatic generation of buildings. Section 4.3
4.5 concludes the chapter.