# Category Archives: Network Planning

Network planning using ray-tracing and other techniques.

# WiMAX Path Loss and Antenna Height

As discussed previously the SUI (Stanford University Interim) model can be used to calculate the path loss of a WiMAX link. The SUI model is given as:

SUI Path Loss Equation

It has five components:

1. The free space path loss (A) up to the reference distance of ‘do’.
2. Additional path loss for distance ‘d’ with path loss exponent ‘n’.
3. Additional path loss (Xf) for frequencies above 2000 MHz.
4. Path gain (Xh) for receive antenna heights greater than 2 m.

The most important factor in this equation is the distance dependent path loss. The impact of this factor is controlled by the path loss exponent ‘n’. It is well known that in free space the path loss exponent has a value of 2. In more realistic channels its value ranges anywhere from 2 to 6. For SUI model the path loss exponent is calculated as:

n=a-(b*hb)+(c./hb)

where a, b and c are SUI model specific parameters. It is obvious that the path loss exponent decreases with increase in base station antenna height ‘hb’. The path loss exponent for various antenna heights is shown below.

Path Loss Exponent

It is observed that as the base station antenna height is varied from 10 m to 80 m the path loss exponent for the three scenarios varies from around 5.5-6.0 to 3.5-4.5. Basically what this means is that for higher base station antenna heights the cell radius would be larger. However we need to be careful when making this statement. Higher antenna heights also sometimes results in a weak signal area close to the base station. This is where the antenna downward tilt becomes an important factor. Antenna downward tilt usually has a value around 5-10 degrees. It is somewhat surprising that although it is such an important factor none of the well known empirical models take it into account.

Note: SUI Model was initially formulated based upon the data collected by AT&T Wireless across the United States in 95 existing macrocells at 1.9 GHz.

# LTE Path Loss at 700 MHz

In the previous post we had compared the path loss of LTE at 728 MHz and 1805 MHz in a free space line of sight channel. This is a very simplistic channel model which tells us that ratio of the received signal strengths at these frequencies can be simply found as:

(f1/f2)^2=(1805/728)^2=6.15

That is the received signal strength at 728 MHz is 6.15 times higher than the received signal strength at 1805 MHz.

Now let us consider a more realistic channel model known as the COST-231 model. According to this model the path loss (difference between the transmit power and receive power) is given as:

L=46.3+33.9*log10(f)-13.82*log(ht)-a+(44.9-6.55*log10(ht))*log10(d)+C

where

f=frequency in MHz (0.1500 MHz – 2000 MHz)

ht=base station antenna height in m (30 m – 200 m)

hr=mobile station antenna height in m (1 m – 10 m)

d=transmit receive separation in km (1 km – 20 km)

C=3 dB for metropolitan centres

and mobile station antenna correction factor is given as:

a=3.2*log10(11.75*hr)^2-4.97

Using the above equations with ht=30 m, hr=1 m and d=1 km the path loss at 728 MHz and 1805 MHz is found out to be 100.63 dB and 114.00 dB respectively i.e. there is a gain of 13.37 dB when using the lower frequency. In simpler terms the received signal at 728 MHz would be 21.72 times stronger than the signal at 1805 MHz.

Such a remarkable improvement in signal strength or in signal to noise ratio (SNR) has the potential of increasing the throughput four folds. For example at an SNR of 1.5 dB QPSK 1/2 would give a throughput of 6.00Mbps whereas at an SNR of 14.7 dB a modulation coding scheme (MCS) of 64QAM 2/3 would result in a throughput of 24.01 Mbps.

Modulation Coding Schemes

# Propagation and In-Building Penetration at 700MHz

It is quite well known that wireless signals travel further at lower frequencies. This phenomenon has become particularly important in the context of LTE where a frequency band has been allocated at 700MHz. We would like to quantify the benefits that can be achieved by using this frequency band.

Firstly we find the received signal power at 728 MHz (lowest downlink frequency) and at 3600 MHz (highest downlink frequency) in a free space line of sight channel. The transmit power is set to 1 W and omnidirectional antennas are considered at the transmitter and receiver. The received power for these two frequencies at a distance of 1000 m is found out to be -59.68dBm and -73.57dBm respectively i.e. there is a gain of 13.88 dB by using the lower frequency band. In simpler terms the signal power would be more than 20 times stronger at the lower frequency. This result can also be simply obtained by taking the square of the ratio the two frequencies.

(3600/728)^2 = 24.45

Similarly compared to a frequency of 1805 MHz, the signal at 728 MHz would be more than 6 times stronger.

(1805/728)^2 = 6.1474

Now we turn our attention to the penetration loss i.e. how much would the signal attenuate when passing through a concrete wall. For this we would have to calculate the attenuation constant (alpha) which is given as:

Propagation Constant

Alpha, the attenuation constant is the real part of the propagation constant gamma whereas Beta, the phase constant, is the imaginary part. These quantities depend upon the frequency, relative permittivity, relative permeability and conductivity of the material. The penetration loss can then be found as -20*log10(exp(-alpha*thickness)). Using the properties of concrete the penetration loss at 728 MHz and at 1805 MHz is found out to be 4.16 dB and 10.38 dB i.e. there is a gain of 6.22 dB when using the lower frequency. In simpler terms the signal at the lower frequency would be more than 4 times stronger. We have considered a concrete wall of 10 cm thickness.

It is quite evident that the frequency of operation plays a big role in determining the propagation loss and the penetration loss. The frequency band of 728-746 MHz would thus be a prized commodity and operators would be willing to pay handsome amount to secure it.

Note:

1. We have ignored the reflection that occurs at the interface as its effect is comparably quite small.

2. Following were the material properties of concrete used in the calculation for penetration loss.

```728 MHz
Relative permittivity = 4.5775
Relative permeability = 1.0000
Conductivity = 0.055

1805 MHz
Relative permittivity = 4.1000
Relative permeability = 1.0000
Conductivity = 0.1300```

# 4G LTE Coverage within Virginia

Since our last post on Verizon LTE coverage within California, Verizon has removed the LTE Coverage Map from its site. Now it only gives a list of cities that have 4G LTE service (just like T-Mobile). So we now move from the West Coast to the East Coast i.e. Virginia. The state that is home to Virginia Tech, one of the finest schools in the country and a breeding ground for Wireless Engineers. It is thus somewhat of a shock to see that Verizon Wireless has no 4G LTE footprint in the state of Virginia. The only place that it intends to deploy 4G in near future is Bristol Virginia. It claims that by the end of 2013 it would have 4G coverage throughout the US where 3G service is currently available.

As in California T-Mobile has a much wider coverage with many smaller cities getting 4G service. The list includes: Alexandria, Mclean, Newport News, Norfolk, Petersburg, Portsmouth, Reston, Richmond, Roanoke and Lychburg. So although Verizon might be winning the speed race it is definitely not winning the coverage race (at least in CA and VA). And with AT&T T-Mobile merger also a possibility early next year Verizon is set to face some stiff challenge.

Given below are the results of a 4G speed test conducted by PC Magazine in the Northeast.

4G LTE Speed Test

The above results show that in areas where 4G coverage is available Verizon allows for average download speeds that are twice that of T-Mobile. The upload speeds are somewhat similar. Overall Verizon is by far the best in terms of the Mobile Speed Index, with T-Mobile in second spot and AT&T at third.

# Ray-Tracing for Network Planning-II

It’s very easy to get lost in the jargon when selecting a simulation tool for planning your wireless network. You will be faced with complex terminology which would not make much sense. At one end of the spectrum are solutions based on simple empirical models while at the other end are solutions based on ray-tracing techniques. Empirical models are based on measurement data and are your best bet if you want a quick and cheap solution whereas ray-tracing techniques are based on laws of physics and promise more accurate results. In principle ray-tracing techniques are quite simple: just transmit a bunch of rays in all directions and see how they behave. However when the number of rays and their interactions becomes large the simulation time may become prohibitively expensive. The simulation time for complex geometries may vary from a few hours to several days.

Following are some of the factors that you must consider when selecting a ray-tracing simulator.

1. Upper limit on the number of interactions

Ray-tracing simulators essentially generate a bunch of rays (image based techniques are an exception) and then follow them around as they reflect, refract, diffract and scatter. Each interaction decreases the strength of the rays. The strength of the rays also decays with distance. As a result the simulator needs to decide when to terminate a ray path. This is usually done based upon the number of interactions that a ray undergoes (typically 8-10 interactions are considered) or based upon its strength (once the strength of a ray falls below -110 dBm there is no point following it any further). Higher the number of interactions considered, greater the accuracy of the simulation but higher the computational complexity.

2. Granularity in field calculations

Field calculations cannot be performed at each and every point within the simulation space. The usual approach is to divide the region under study into a grid such that locations closer to a transmitter are covered more finely and the regions further away are covered in lesser detail. The rays are then combined within each block of the grid to get the resultant field strength. The level of granularity determines the computation load. It would be prohibitively expensive to have a very high level of granularity for a large network.

3. Accuracy in modeling the various propagation phenomenon

As mentioned previously an accurate modeling of all propagation phenomena is required including reflection, refraction, diffraction and scattering. Some ray-tracing simulators might model reflection and refraction only while ignoring the other phenomenon such as diffraction. Furthermore some ray-tracing simulators might consider all reflections to be specular (no scattering). This is a good approximation for large smooth surfaces but is not such a good assumption for irregular terrain.

4. Granularity of the terrain database

Most state of the art ray-tracing tools use some sort of terrain database to perform their calculations. These terrain databases are required for determining the paths of the rays as they travel in dense urban environments. These databases may contain simple elevation data or actual 3D building data. These databases may have accuracy of 10m or 30m or maybe more. The accuracy of the simulation is highly dependent on the granularity of the terrain database.

5. Accuracy in representation of building materials

The wireless signal propagation within cities is governed by complex phenomena such as reflection, refraction, diffraction and scattering. Let’s take the example of the phenomenon of reflection. The percentage of signal reflected back at a particular interface is dependent on permittivity and permeability of the object. Based on these properties only 10% of the signal maybe reflected or 50% of the signal may be reflected. So, for accurate simulation not only should we have a high level of granularity of the 3D building data, we also need an accurate description of the building materials.

6. Dynamic Channel Behavior

A wireless channel is continuously changing i.e. the channel is dynamic (as opposed to being static). However the ray-tracing techniques available in the literature do not capture this dynamic behavior. The dynamic behavior of the channel is mainly due to the motion of the transmitter or receiver as well as motion of the surroundings. While the position of the transmitter and receiver can be varied in the ray-tracing simulation the surroundings are always stationary. Hence a ray-tracing simulator is unable to capture the time-varying behavior of the channel.

The accuracy of ray-tracing simulators is bound to increase as the computational power of computers increases and as accurate 3D building databases become available throughout the world. Until that time we would have to fall back to approximate simulations or maybe measurement results.

# Ray-Tracing for Network Planning-I

It’s very easy to get lost in the jargon when selecting a simulation tool for planning your wireless network. You will be faced with complex terminology which would not make much sense. You will be told that ray-tracing is the solution to all problems and outperforms all other techniques. However ray-tracing is only accurate when the following factors have been considered.

1. Granularity of the terrain database
2. Granularity in field calculations
3. Accuracy in representation of building materials
4. Accuracy in modeling the various propagation phenomenon
5. Upper limit on the number of interactions