# Blog Archives

## Oracle R Enterprise Configuration on Oracle Linux

Before starting to deal with large volumes of data problems on Oracle R Enterprise (ORE) you need to perform a couple of configurations over your Oracle Linux and Oracle Database systems. Here is the recipe:

- Ensure that you have the following lines in your oracle users .bash_profile file
export R_HOME=/usr/lib64/R export PATH=/usr/bin:$PATH

- Ensure that you have already installed libpng.x86_64 and libpng-devel.x86_64 packages on your Oracle Linux otherwise issue to install them.
yum install libpng.x86_64 libpng-devel.x86_64

- Switch to root and issue R. Once you are in R session, install two prerequisites of ORE:
install.packages("DBI") install.packages("png")

- Ensure that your database is 11.2.0.3 otherwise refer you need to apply several database patches:
- Go to Oracle R Enterprise Download Page and download
*Oracle R Enterprise Server Install for Oracle Database on Linux 64-bit (91M) and Oracle R Enterprise Client Supporting Packages for Linux 64-bit Platform (1M)*(ore-server-linux-x86-64-1.1.zip and ore-supporting-linux-x86-64-1.1.zip) under*Oracle R Enterprise Downloads (v1.1)*section - Unzip the file by issuing
unzip ore-server-linux-x86-64-1.1.zip ore-supporting-linux-x86-64-1.1.zip

- At this point ensure that your database to support Oracle R Enterprise is up and running
- Execute
`install.sh`

in order to create ORE libraries and database objects into SYS and RQSYS schemas.cd server ./install.sh

Oracle R Enterprise 1.1 Server Installation.Copyright (c) 2012, Oracle and/or its affiliates. All rights reserved.

Do you wish to proceed? [yes]

Checking R ................... Pass

Checking R libraries ......... Pass

Checking ORACLE_HOME ......... Pass

Checking ORACLE_SID .......... Pass

Checking sqlplus ............. Pass

Checking ORE ................. PassChoosing RQSYS tablespaces

PERMANENT tablespace to use for RQSYS [SYSAUX]:

TEMPORARY tablespace to use for RQSYS [TEMP]:Current configuration

R_HOME = /usr/lib64/R

R_LIBS_USER = /u01/app/oracle/product/11.2.0/dbhome_1/R/library

ORACLE_HOME = /u01/app/oracle/product/11.2.0/dbhome_1

ORACLE_SID = orcl

PERMANENT tablespace = SYSAUX

TEMPORARY tablespace = TEMPInstalling libraries ......... Pass

Installing RQSYS ............. Pass

Installing ORE packages ...... Pass

Creating ORE script .......... PassNOTE: To use ORE functionality, a database user with RQROLE role,

a few more grants and synonyms is required. A complete list of

requirements is available in rquser.sql. There is also a demo

script demo_user.sh creating a new user RQUSER.To use embedded R functionality, an RQADMIN role is required.

Please, consult the documentation for more information on various

roles.`Done`

- Finally install some required R libraries/packages by using
`install.packages`

command in R. Ensure that user (`root`

will do that) you will start R has a write permission on`/usr/lib64/R/library`

install.packages("/home/oracle/Desktop/server/ORE_1.1_R_x86_64-unknown-linux-gnu.tar.gz", repos = NULL) install.packages("/home/oracle/Desktop/server/OREbase_1.1_R_x86_64-unknown-linux-gnu.tar.gz", repos = NULL) install.packages("/home/oracle/Desktop/server/OREeda_1.1_R_x86_64-unknown-linux-gnu.tar.gz", repos = NULL) install.packages("/home/oracle/Desktop/server/OREgraphics_1.1_R_x86_64-unknown-linux-gnu.tar.gz", repos = NULL) install.packages("/home/oracle/Desktop/server/OREstats_1.1_R_x86_64-unknown-linux-gnu.tar.gz", repos = NULL) install.packages("/home/oracle/Desktop/server/ORExml_1.1_R_x86_64-unknown-linux-gnu.tar.gz", repos = NULL) install.packages("/home/oracle/Desktop/supporting/ROracle_1.1-2_R_x86_64-unknown-linux-gnu.tar.gz", repos = NULL)

- Finally start a R session (ensure that
`$ORACLE_HOME/lib`

is in your`LD_LIBRARY_PATH`

before starting R session) and load ORE librarylibrary(ORE) Loading required package: OREbase Loading required package: ROracle Loading required package: DBI Attaching package: 'OREbase' The following object(s) are masked from 'package:base': cbind, data.frame, eval, interaction, order, paste, pmax, pmin, rbind, table Loading required package: OREstats Loading required package: MASS Loading required package: OREgraphics Loading required package: OREeda Loading required package: ORExml

## Create your Own R Server on Oracle Linux

It is very common to have people running R on their individual PCs. One major problem is the hardware limitations of your PCs will inhibit you to deal with large volumes of data.

Moreover if you wish to use Oracle R Enterprise you need a database connectivity and for some platforms like Mac there is no client available yet. In this post you will find how you can install R in a centralised fashion so that any individual can access it via their favorite browser.

## Preparing Oracle Enterprise Linux

- Ensure that ol5_u6_base (or a further release) and el5_addons (ol5_addons is also ok) repos are enabled in /etc/yum.repos.d/public-yum-el5.repo file(by setting enabled flag to 1)
- Issue
*yum install R.x86_64*(Notice that R package is in*el5_addons*and other dependendents from*el5_addons*and*ol5_u6_base*) - Download 64-bit RStudio-Server by issuing
*wget http://download2.rstudio.org/rstudio-server-0.96.331-x86_64.rpm* - Install RStudio-Server by issuing
*sudo rpm -Uvh rstudio-server-0.96.331-x86_64.rpm* - Start a browser and go to http://<rstudio-servername>:8787

- Provide your linux authentication details
- RStudio-Server is ready to use

- For more details on R Studio Server configuration refer to Management and Configuration documentations

## Line of Sight (LoS) Analysis: Optimizing the Observers for Best Coverage (Part 4)

*N*observers on terrain such that visible region (as many

*green*points as possible by our convention) is maximized ?”.

We first define a pseudo code in order to find the optimal (Not guaranteed. Keep in mind that optimization problems are usually NP-complete by their nature) layout of *N* observers. For simplicity we will assume that all observers have the same height (7 units) which can be relaxed later.

We will implement a constructive way of finding optimal layout for N observers. Here is the pseudo code:

- Find the optimal layout for 1 observer and compute coverage ratio (best coverage for one observer)
- Add another random observer ((
*uniform(-8,8),*) and compute the coverage for those two observers (random one and the best observer from Step 1).*uniform(-8,8))* - If the new coverage is better than the coverage in Step 1, use this as the input of optimization solver
- Otherwise repeat Step 2 to find a better coverage.
- For number of observers greater than 2 apply the idea in Step 2 recursively.

There are some blur points in this pseudo code. We will define those before moving further with the implementation.

### Coverage

The very first thing to be defined is the coverage idea. As you will remember from second post, we have defined our 3D terrain by evaluating our *height* function over outer product of *x & y* values varying over *[-8,8]* with a step size of *0.1* units. We have *1681* different* (x,y)* tuples. Here is the definition of coverage based on our conventions:

**Coverage Ratio**is the ratio of points within LoS of a given observer/group of observers (at least one of the observers mark those set of points as*green*) to the total number of points (1681)

### How to Find Optimal Coordinates of Observers ?

Optimality is a very common word used in place of many different concepts in real life or engineering. Let me define it once more for our purpose:

**Optimization**is the process of searching for an*N-dimensional vector*using a*technique*to maximize/minimize*a function of that N-dimensional vector*.

Now let’s substitute three italic words of definition for our problem:

**N-dimensional vector**in our problem is the vector of first to components of observer dimensions. Such as,*(x1,y1,x2,y2,…,xn,yn).***Technique**to be used is the Nelder and Mead Technique (A version of it implemented in R).**Function**to be maximized is the coverage function which we have defined for a given set of observers.

### Implementation

Let’s start by defining the function to be optimized that is *coverage* of terrain for a given set of observers.

targetfunc<-function(observer){ m <- matrix(data=observer,ncol=2,byrow=TRUE) # Compute merged status of all observers mergedstatus <- rep("red",length(terrain$height)) for(oidx in seq(1:dim(m)[1])){ terrain$dist2observer <- distance(terrain, c(m[oidx,],7)) status <- LoS(terrain,c(m[oidx,],7),maxVisibleDistance) mergedstatus <- updatestatus(mergedstatus,status) } sum(mergedstatus=="green")/1681 }

*matrix* routine allows us to create a table of two columns(first two dimensions of observers) and *length(observer)/2 *rows*. *We have used the technique discussed in part 3 to compute merged status of observers. *sum(mergedstatus==”green”)* call is used to count number of *green* points on terrain with respect to observers.

Next is the computation of first input to be given to optimization solver. That’s because for any optimization technique starting point is critical. Without any formal definition we will use our pseudo code to choose a *“good starting point/vector”.*

n <- 2 baselineValue <- 0.541344 previousObserver <- c(1.15861411217711, 1.1499851362913) observers <- c(previousObserver,runif(2,-8,8)) while(targetfunc(observers) <= baselineValue){ observers <- c(previousObserver,runif(2,-8,8)) } print(observers)

Above code is an example to initialize *observers* vector for searching best 2 observer layout. It uses the best coverage ratio for 1 observer case (*54.1344%*) and adds a new random observer next to best observer found for single observer case.

Final point is the optimization solver which is very simple and totally handled by R

optim <- optim(observers, targetfunc, control=list(fnscale=-1,trace=5,REPORT=1))

First parameter is the initial value for input vector (prepared by previous code piece). Second parameter is the name of the function to be maximized. *optim* function is implemented to solve minimization problems by default. Setting *fnscale* attribute of *control* parameter turns it to a maximization problem solver.

Now we can combine all to have our final script

library(rgl) ################## # Functions ################## # 3D Terrain Function height <- function (point) { sin(point$x)+0.125*point$y*sin(2*point$x)+sin(point$y)+0.125*point$x*sin(2*point$y)+3 } # Linear Function linear <- function (px, observer, target) { v <- observer - target y <- ((px - observer[1])/v[1])*v[2]+observer[2] z <- ((px - observer[1])/v[1])*v[3]+observer[3] data.frame(x=px,y=y, z=z) } # Linear Function distance <- function (terrain, observer) { sqrt((terrain$x-observer[1])^2+(terrain$y-observer[2])^2+(terrain$height-observer[3])^2) } LoS <- function(terrain, observer, maxVisibleDistance){ status = c() for (i in seq(1:nrow(terrain))) { if (observer[1] == terrain$x[i] && observer[2] == terrain$y[i]){ if(observer[3] >= terrain$height[i]){ if (terrain$dist2observer[i] > maxVisibleDistance){ status <- c(status,"yellow") }else{ status <- c(status,"green") } }else{ status <- c(status,"red") } }else{ # All points on line line <- linear(seq(from=min(observer[1],terrain$x[i]), to=max(observer[1],terrain$x[i]), by=0.1), observer, c(terrain$x[i],terrain$y[i],terrain$height[i])) # Terrain Height h <- height(line) # LoS Analysis aboveTerrain <- round((line$z-h),2) >= 0.00 visible <- !is.element(FALSE,aboveTerrain) if (visible){ # Second Rule if(terrain$dist2observer[i] <= maxVisibleDistance){ status <- c(status,"green") }else{ status <- c(status,"yellow") } }else{ status <- c(status,"red") } } } status } updatestatus <- function(status1,status2){ mergedstatus<-c() for(i in seq(length(status1))){ if (status1[i] == "green" || status2[i] == "green"){ mergedstatus <- c(mergedstatus,"green") }else if (status1[i] == "yellow" || status2[i] == "yellow"){ mergedstatus <- c(mergedstatus,"yellow") } else{ mergedstatus <- c(mergedstatus,"red") } } mergedstatus } ################## # Input ################## # Max visible distance maxVisibleDistance = 8 # Generate points with a step size of 0.1 x <- seq(from=-8,to=8,by=0.4) xygrid <- expand.grid(x=x, y=x) terrain <- data.frame(xygrid, height=height(xygrid) ) targetfunc<-function(observer){ #print(observer) m <- matrix(data=observer,ncol=2,byrow=TRUE) # Compute merged status of all observers mergedstatus <- rep("red",length(terrain$height)) for(oidx in seq(1:dim(m)[1])){ terrain$dist2observer <- distance(terrain, c(m[oidx,],7)) status <- LoS(terrain,c(m[oidx,],7),maxVisibleDistance) mergedstatus <- updatestatus(mergedstatus,status) } sum(mergedstatus=="green")/1681 } n <- 3 baselineValue <- 0.541344 previousObserver <- c(-1.32661956044593, 2.18870625357827) # List of observers (x1,y1,z1,x2,y2,z2) observers <- c(previousObserver,runif(2,-8,8)) while(targetfunc(observers) <= baselineValue){ observers <- c(previousObserver,runif(2,-8,8)) } print(observers) optim <- optim(observers, targetfunc, control=list(fnscale=-1,trace=5,REPORT=1))

### Results

Here is the coverage ratio for different number of observers after optimization

You can test covering more than 98% of whole terrain is not trivial by using only 6 random observers but requires “careful” choice of their layout.

Finally let’s check step by step improvement in coverage as we add more optimal observers.

## Single Observer

## Two Observers

## Three Observers

## Four Observers

## Five Observers

## Six Observers

## Line of Sight (LoS) Analysis: Multiple Observers (Part 3)

In this part of my LoS Analysis series, I will try to extend 3D LoS analysis for multiple observers. Assume that you drop multiple observers into a terrain with the aim of covering it perfectly (100% *green*).

We will reuse R codes used in Part 2. However we need to add a simple code piece to be used to merge Line of Sight results of multiple observers. If a point on terrain is visible by any of the observers that means point is visible, if the point is visible but far from all observers that means point is out of LoS due to distance (marked with *yellow*), for all other conditions point on terrain is *red*. *updatestatus* function is implemented for this purpose.

library(rgl) ################## # Functions ################## # 3D Terrain Function height <- function (point) { sin(point$x)+0.125*point$y*sin(2*point$x)+sin(point$y)+0.125*point$x*sin(2*point$y)+3 } # Linear Function linear <- function (px, observer, target) { v <- observer - target y <- ((px - observer[1])/v[1])*v[2]+observer[2] z <- ((px - observer[1])/v[1])*v[3]+observer[3] data.frame(x=px,y=y, z=z) } # Linear Function distance <- function (terrain, observer) { sqrt((terrain$x-observer[1])^2+(terrain$y-observer[2])^2+(terrain$height-observer[3])^2) } LoS <- function(terrain, observer, maxVisibleDistance){ status = c() for (i in seq(1:nrow(terrain))) { if (observer[1] == terrain$x[i] && observer[2] == terrain$y[i]){ if(observer[3] >= terrain$height[i]){ if (terrain$dist2observer[i] > maxVisibleDistance){ status <- c(status,"yellow") }else{ status <- c(status,"green") } }else{ status <- c(status,"red") } }else{ # All points on line line <- linear(seq(from=min(observer[1],terrain$x[i]), to=max(observer[1],terrain$x[i]), by=0.1), observer, c(terrain$x[i],terrain$y[i],terrain$height[i])) # Terrain Height h <- height(line) # LoS Analysis aboveTerrain <- round((line$z-h),2) >= 0.00 visible <- !is.element(FALSE,aboveTerrain) if (visible){ # Second Rule if(terrain$dist2observer[i] <= maxVisibleDistance){ status <- c(status,"green") }else{ status <- c(status,"yellow") } }else{ status <- c(status,"red") } } } status } updatestatus <- function(status1,status2){ mergedstatus<-c() for(i in seq(length(status1))){ if (status1[i] == "green" || status2[i] == "green"){ mergedstatus <- c(mergedstatus,"green") }else if (status1[i] == "yellow" || status2[i] == "yellow"){ mergedstatus <- c(mergedstatus,"yellow") } else{ mergedstatus <- c(mergedstatus,"red") } } mergedstatus } ################## # Input ################## # Observer location #observers<-c(0,0, 6,1,1,6) # Max visible distance maxVisibleDistance = 8 # Generate points with a step size of 0.1 x <- seq(from=-8,to=8,by=0.4) xygrid <- expand.grid(x=x, y=x) terrain <- data.frame(xygrid, height=height(xygrid) ) # List of observers (x1,y1,z1,x2,y2,z2) observers <- c(runif(2,-8,8),6,runif(2,-8,8),6, runif(2,-8,8),6,runif(2,-8,8),6, runif(2,-8,8),6,runif(2,-8,8),6, runif(2,-8,8),6,runif(2,-8,8),6) m <- matrix(data=observers,ncol=3,byrow=TRUE) # Compute merged status of all observers mergedstatus <- rep("red",length(terrain$height)) for(oidx in seq(1:dim(m)[1])){ terrain$dist2observer <- distance(terrain, m[oidx,]) status <- LoS(terrain,m[oidx,],maxVisibleDistance) mergedstatus <- updatestatus(mergedstatus,status) } # Set merged status as the ultimate status terrain <- data.frame(terrain,status = mergedstatus) rgl.open() rgl.surface(x, x, matrix(data=terrain$height,nrow=length(x),ncol=length(x)), col=matrix(data=mergedstatus,nrow=length(x),ncol=length(x)) ) bg3d("gray") # Mark all observers for(oidx in seq(1:dim(m)[1])){ spheres3d(c(m[oidx,1]), c(m[oidx,3]), c(m[oidx,2]), radius=0.25, color="white" ) } rgl.viewpoint(-60,30)

# A Few Examples

Here are a few examples. All those observers are uniformly distributed over terrain using *runif* function

### Trivial Case: Single Observer

### Two Observers

### Four Observers

### Eight Observers

## Line of Sight (LoS) Analysis: 3D Terrain Analysis (Part 2)

In my previous post on LoS Analysis, I have tried to explain briefly the basics of LoS in two dimensional space. Obviously real life problems are based on three dimensional terrains although basic concepts are all the same. In this second part I will try to adapt the same techniques with a few modifications for three dimensional terrains.

### 3D Terrain Visualization with R

One of the first differences in 3D LoS analysis is the terrain visualization. We can not use *plot* function for proper visualization is 3D. Fortunately R has all packages you need for any type of problem. I will use rgl package which can be downloaded using `install.packages("rgl")`

command.

Once you have the *rgl* package, generating pseudo 3D terrains as we did for 2D is a trivial thing.

You can use the following R script to generate your 3D terrains like above.

library(rgl) # 3D Terrain Function height <- function (x,y) { sin(x)+0.125*y*sin(2*x)+sin(y)+0.125*x*sin(2*y)+0.25 } # Terrain boundaries -8<=x<=8 and -8<=y<=8 boundary <- c(-8,8) # Terrain grid with a step size of 0.1 units xy<-seq(from=boundary[1],to=boundary[2],by=0.1) # Evaluate all heights for all grid points z<-outer(xy,xy,height) # A few visualization staff zlim <- range(z) zlen <- zlim[2] - zlim[1] + 1 colorlut <- terrain.colors(zlen) # height color lookup table col <- colorlut[ z-zlim[1]+1 ] # assign colors to heights for each point # Draw the terrain rgl.open() bg3d("gray") rgl.surface(xy, xy, z, color=col)

A new function in this script is *outer* function which generates the product of a vector and a *row-vector* to have a matrix (product of a *row-vector* with a *vector/column-vector* is obviously a scalar value and named to be *dot/inner product*). The third parameter of the function provides us the mechanism to apply a given function (*height* in our case) for each element of this matrix. Obviously you can play with *height *function to have fancier 3D terrains and to have best visualization you may need *viewpoint* routine in rgl package .

### LoS in 3D Terrain

Line of Sight analysis on 3D terrain uses the same principles as it does in 2D. Use the following R script to decide on status of a point (invisible, visible, visible but far away)

library(rgl) ################## # Functions ################## # 3D Terrain Function height <- function (x,y) { sin(x)+0.125*y*sin(2*x)+sin(y)+0.125*x*sin(2*y)+0.25 } # Linear Function linear <- function (x, observer, target) { v <- observer - target y <- ((x - observer[1])/v[1])*v[2]+observer[2] z <- ((x - observer[1])/v[1])*v[3]+observer[3] data.frame(x=x,y=y, z=z) } # Linear Function distance <- function (p0,p1) { sqrt(sum((p0-p1)^2)) } ################## # Input ################## # Observer location observer<-c(10,10,1) # Target on terrain target <- c(5, 5, height(5,5)) # Max visible distance maxVisibleDistance = 4 # Generate points with a step size of 0.1 x <- seq(from=min(observer[1],target[1]), to=max(observer[1],target[1]), by=0.1) # All points on line line <- linear(x, observer, target) # Terrain Height h <- height(line$x,line$y) # LoS Analysis aboveTerrain <- round((line$z-h),2) >= 0.1 # First Rule visible <- !is.element(FALSE,aboveTerrain) if (visible){ # Second Rule d <- distance(observer, target) if(d <= maxVisibleDistance){ status <- "LoS" }else{ status <- "non-LoS due to Distance" } }else{ status <- "non-LoS due to Blocking" }

Obviously there are a few changes in the script with compared to 2D version. The first one is *linear* function(Code Lines 10-18). New version not only evaluates second (*y*) but also the third dimension (*z*). Notice that *z* is our height dimension by convention. We have also utilized *data.frame* function to concatenate all dimensions to form a table of point dimensions

The second difference is on *height* function (Code Lines 5-8). It is no longer a mapping from *x* to *y* but a mapping from* x,y* to *z.*

Rest of the 3D version of script is pretty much the same or trivial to discuss more.

### Visualizing LoS on 3D Terrain

Until this point we have analyzed LoS of a single point on 2D-3D terrains. But usually network analists wish to know LoS map of the terrain with respect to a given observer. In other words we need to visually understand which regions on 3D terrain are visible by the *observer*, invisible by the *observer* due to blocking, or further than the limit from the *observer*.

Here the LoS map of our pseudo 3D terrain with respect to an observer with a given set of coordinates and maximum service range(*green* vs *yellow* regions).

You can obtain this visualization using following R script.

library(rgl) ################## # Functions ################## # 3D Terrain Function height <- function (point) { sin(point$x)+0.125*point$y*sin(2*point$x)+sin(point$y)+0.125*point$x*sin(2*point$y)+3 } # Linear Function linear <- function (px, observer, target) { v <- observer - target y <- ((px - observer[1])/v[1])*v[2]+observer[2] z <- ((px - observer[1])/v[1])*v[3]+observer[3] data.frame(x=px,y=y, z=z) } # Linear Function distance <- function (terrain, observer) { sqrt((terrain$x-observer[1])^2+(terrain$y-observer[2])^2+(terrain$height-observer[3])^2) } LoS <- function(terrain, observer, maxVisibleDistance){ status = c() for (i in seq(1:nrow(terrain))) { if (observer[1] == terrain$x[i] && observer[2] == terrain$y[i]){ if(observer[3] >= terrain$height[i]){ if (terrain$dist2observer[i] > maxVisibleDistance){ status <- c(status,"yellow") }else{ status <- c(status,"green") } }else{ status <- c(status,"red") } }else{ # All points on line line <- linear(seq(from=min(observer[1],terrain$x[i]), to=max(observer[1],terrain$x[i]), by=0.1), observer, c(terrain$x[i],terrain$y[i],terrain$height[i])) # Terrain Height h <- height(line) # LoS Analysis aboveTerrain <- round((line$z-h),2) >= 0.00 visible <- !is.element(FALSE,aboveTerrain) if (visible){ # Second Rule if(terrain$dist2observer[i] <= maxVisibleDistance){ status <- c(status,"green") }else{ status <- c(status,"yellow") } }else{ status <- c(status,"red") } } } status } ################## # Input ################## # Observer location observer<-c(0.835597146302462, -1.71025141328573, 6) # Max visible distance maxVisibleDistance = 8 # Generate points with a step size of 0.1 x <- seq(from=-8,to=8,by=0.4) xygrid <- expand.grid(x=x, y=x) terrain <- data.frame(xygrid, height=height(xygrid) ) terrain <- data.frame(terrain, dist2observer=distance(terrain, observer) ) terrain <- data.frame(terrain, status = LoS(terrain, observer, maxVisibleDistance)) rgl.open() rgl.surface(x, x, matrix(data=terrain$height,nrow=length(x),ncol=length(x)), col=matrix(data=terrain$status,nrow=length(x),ncol=length(x)) ) bg3d("gray") # Mark the observer spheres3d(c(observer[1]), c(observer[3]), c(observer[2]), radius=0.5, color="white" ) rgl.viewpoint(-60,30)

For a better visualization R allows you to implement spinning 3D terrains using *play3d* function and record it in gif format using *movie3d* function as I did below.

## Line of Sight (LoS) Analysis: Basics (Part 1)

# Introduction

Line of Sight analysis is a commonly used technique in telecommunication industry for A/I (Air Interface) equipment planning and allocation. With the simplest terms LoS is the question whether a point on N-dimensional space is visible by an other observer point. The question can be used to answer where to locate a transceiver on terrain so that it can serve customers on some region A.

Before relatively more complicated problems, let’s start with an easy example focusing on two dimensional terrains. Throughout the post, we will use R for coding which is my favorite option for any mathematical problem (statistics, plotting, linear algebra, optimization, etc.). But you can easily adapt coding material to Mathlab, Python,or your favorite language.

We will start by defining a mathematical function to be used to generate our pseudo terrains. For this purpose trigonometric functions (*sin*, *cos*) and polynomial functions are the best ones because of their wavy shapes. Here is an example of trigonometric terrain

**Figure 1 Trigonometric Terrain
**

In order to generate this two dimensional one use the following code piece

x <- seq(from=4,to=10,by=0.01) y <- sin(x)+cos(2*x)+sin(3*x)+cos(4*x)+3 windows() plot(x,y,'l', main="y=sin(x)+cos(2x)+sin(3x)+cos(4x)+3", ylab="height",col="blue")

**Figure 2 Polynomial Terrain
**

To obtain this terrain, use the following R script piece

x <- seq(from=0,to=6,by=0.01) y <- x*(x-1)*(x-2)*(x-3)*(x-4)*(x-5)*(x-6)+100 windows() plot(x,y,'l', main="y=x(x-1)(x-2)(x-3)(x-4)(x-5)(x-6)+100", ylab="height",col="blue")

Combining polynomial terrain functions with trigonometric ones will give you fancier ones.

# What is LoS ?

You can think that we have already answered this question but this was an informal try which is not very useful for solving the problem. In order to solve this problem methodically we need to understand what makes a *target* visible (within LoS) by the *observer*.

As you see on *Figure 3*, **green** point is within line of sight of *observer* (**blue** point). However there is pseudo hill between **red** point and *observer*. The difference is that the line connecting *observer* and **green** point is always greater than the terrain function whereas this is not valid for the line connecting *observer* and **red** point (for x ε [~2.5, ~3.5] red line is under the terrain curve).

**Figure 3 LoS vs non-LoS
**

This was the first point (blocking) we should define. The second one is an easier one related with maximum Euclidean distance between *observer* and *target*. The distance between *observer* and *target* may cause a phase shift in signal if the distance is sufficiently long or depending on weather conditions and terrain properties you may observer diffraction problems (actually there might be more than those). In return this will cause signal quality issues or call drops. On Figure 3, although *blocking* is not an issue between *observer* and **yellow** point, *target* is out of visible range (say 8 units) of *observer*.

You can generate *Figure 3* using the following R script

# Terrain Function height <- function (x) { x*x/3+sin(x)+cos(2*x)+sin(3*x)+cos(4*x)+sin(5*x)+cos(6*x)+3 } # Observer location observer<-c(1.5,8.9) # Generate terrain points with a tolerance of 0.1 x<-seq(from=-0.1,to=6.1,by=0.1) terrainHeight<-height(x) windows() # Draw terrain plot(x,terrainHeight,type='b', xlim=range(x),ylim=range(terrainHeight), main="Line of Sight (LoS)", ylab="Height",xlab="") # Not LoS points(x=c(observer[1],x[41]), y=c(observer[2],terrainHeight[41]), col="red",type='b') # LoS points(x=c(observer[1],x[5]), y=c(observer[2],terrainHeight[5]), col="green",type='b') # LoS but far points(x=c(observer[1],x[length(x)]), y=c(observer[2],terrainHeight[length(x)]), col="yellow",type='b') # Draw Observer points(x=c(observer[1]), y=c(observer[2]), col="blue",pch=10)

# Method to Decide LoS

Finally let’s define a method to find all visible, invisible, and “far” points on any terrain. Since it is not “easy” to decide analytically whether the line connecting *observer* and *target* “is above” the terrain for any terrain function, we will use a simple numeric method.

We will define a *step* size small enough (around *Spatial Tolerance*) to generate all *x* values between *observer* and *target*. *seq* function is a good choice for doing this (Code Lines 33-36). Evaluate these *x* values for line function connecting *observer* and *target *and terrain function. Evaluation is simple for terrain function using *height* function (Code Lines 4-7). Evaluation of line function is held by function *linear* using parametric definition of line function (Code Lines 9-14) . Next step is to search for any *x* value having a line evaluation less than terrain evaluation (Code Line 44-28). The rest is simple as to evaluate euclidean distance and assigning values to *status* variable.

################## # Functions ################## # Terrain Function height <- function (x) { x*x/3+sin(x)+cos(2*x)+sin(3*x)+cos(4*x)+sin(5*x)+cos(6*x)+3 } # Linear Function linear <- function (x, observer, target) { v <- observer - target ((x - observer[1])/v[1])*v[2]+observer[2] } # Linear Function distance <- function (p0,p1) { sqrt(sum((p0-p1)^2)) } ################## # Input ################## # Observer location observer<-c(1.5,9) # Target on terrain target <- c(5, height(5)) # Max visible distance maxVisibleDistance = 4 # Generate points with a step size of 0.1 x <- seq(from=min(observer[1],target[1]), to=max(observer[1],target[1]), by=0.1) # Terrain Height h <- height(x) # y Values y <- linear(x, observer, target) # LoS Analysis aboveTerrain <- round((y-h),2) >= 0.00 # First Rule visible <- !is.element(FALSE,aboveTerrain) if (visible){ # Second Rule d <- distance(observer, target) if(d <= maxVisibleDistance){ status <- "LoS" }else{ status <- "non-LoS due to Distance" } }else{ status <- "non-LoS due to Blocking" }