Wireless Interference

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Adaptive Signal Processing for Optimal Wireless Networking

by

Tanooj Luthra

January 30, 2008


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Adaptive Signal Processing for Optimal Wireless Networking

Tanooj Luthra

ABSTRACT

Wireless networking today is ubiquitous and its usage will continue to accelerate

in future. With increasing dependence on wireless networking, it has become necessary

to have a reliable and high speed networking scheme. A major problem in a wireless

networking system is that many interfering waves can hinder the speed and quality of

network connection. These interfering waves can come from common household objects

such as cordless phones, microwave ovens, and even the neighbors home networking

system. This limits the speed and the distance over which the networking can be done.

The focus of this project is to invent a method to cancel the interference from unwanted

sources.

A new fast adaptive algorithm is developed and exploits the fact that the signals received

from the direction of a desired source and the direction of an interferer have different

characteristics across the antenna phased array. This algorithm adaptively adjusts the

array pattern to automatically cancel the interference without knowing its location. It uses

only one coefficient to be adapted, which makes it fast. A computer simulation using C

programming language was then completed to conduct simulated experiments and

characterize the new algorithm. The new technique developed here could reduce

interference on average by a factor of more than 100 times greater than currently used

beam forming methods. Thus, this will significantly increase the speed of a wireless

network without increasing the cost associated with increasing the antenna elements

needed to achieve similar interference reduction.


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1. Introduction

The use of wireless networking has skyrocketed in the past years, and will

continue to do so in the upcoming months and years. With wireless networking getting

used so commonly everywhere, its necessary to have a flexible and reliable networking

scheme. Home users increasingly are having more than one computer, and more than one

laptop, and the need of accessing each other, and the internet is substantial. Having a

wireless home networking system is the easiest and most efficient way. Around the

world, the use of internet is increasing exponentially and wireless networking is one of

the most common way to access the internet. On Tuesday, September 17, 2002,

Birmingham International Airport announced that it has become the first airport in the

UK to setup a wireless network for the public use to access internet. (Wearden) Today,

many airports have started accommodating their passengers with complimentary wireless

connection to surf on the web. Many companies have also started to provide wireless

services to their customers. Starbucks around the country has started charging 10$/hr to

use their wirelessly connected computers located in the cafe. Hotels, book stores, coffee

houses, and others are all following suit as the wireless age progresses, and becomes

ubiquitous. There are 34,457 places world wide, 9555 in the United States, 1730 in

California, and 100 in San Diego alone which are Wi-Fi locations. (Wi-Fi.org)

The Wi-Fi is the most commonly used standard for wireless networking. The

basic standards related to Wi-Fi are developed by IEEE 802.11 committee. A consortium

of companies, called Wi-Fi Alliance, works on improving and testing the interoperability

of wireless local area network products and devices developed by various companies. A

nonprofit organization, the Wi-Fi Alliance was formed in 1999 to certify interoperability

of wireless Local Area Network products based on IEEE 802.11 specification. Currently


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Wi-Fi Alliance has more than 300 member companies from around the world, and more

than 3,500 products have received Wi-Fi certification since certification began in March

of 2000. The Wi-Fi Alliance's goal is to enhance the user experience through product

interoperability. In August 1999, Wi-Fi Alliance founding members Cisco, Conexant,

Agere, Nokia and Symbol united to drive the adoption of one globally accepted standard

for high-speed, wireless, local area networking: the IEEE 802.11 standard. Membership

has grown to over 200 members since Wi-Fi Alliance's inception. Current Sponsor

members Apple, Broadcom, Cisco, Conexant, Dell, Intel, Microsoft, Motorola, Nokia,

Sony Corporation and Texas Instruments have representatives on the board of directors.

(Wi-Fi.org)

IEEE 802.11 is a set of standards for wireless local area network (WLAN)

computer communication, developed by the IEEE LAN/MAN Standards Committee

(IEEE 802) in the 5 GHz and 2.4 GHz public spectrum bands. The 802.11 family

includes over-the-air modulation techniques that use the same basic protocol. The most

popular are those defined by the 802.11b and 802.11g protocols, and are amendments to

the original 802.11a standard. IEEE 802.11b was the first widely accepted one, followed

by 802.11g and the most current standard that is getting deployed is known as IEEE

802.11n. Security was originally purposefully weak due to export requirements of some

governments, and was later enhanced via the 802.11i amendment after governmental and

legislative changes. 802.11n is a new multi-streaming modulation technique that is still

under draft development, but products based on its proprietary pre-draft versions are

being sold. Other standards in the family (cf, h, j) are service amendments and

extensions or corrections to previous specifications. (Hoskin)


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802.11n builds on previous 802.11 standards by adding multiple-input multiple-

output (MIMO) to the physical (PHY) layer. This is the first standard that uses antenna

arrays with multiple transmitter and receiver antennas to improve the system

performance. The transmitter and receiver use precoding and postcoding techniques,

respectively, to achieve the capacity. Precoding includes spatial beamforming and spatial

coding, where spatial beamforming improves the received signal quality at the decoding

stage. Spatial coding can increase data throughput via spatial multiplexing and increase

range by exploiting the spatial diversity, through techniques such as Alamouti coding.

The number of antennas relates to the number of simultaneous streams: two receivers and

two transmitters (2x2) or four receivers and four transmitters (4x4). The standards

requirement is a 2x2 with a maximum two streams, but allows 4x4. More recently,

products are getting introduced that contain larger number of antennas. (Karnath)

IEEE 802.11n has immense benefits over the older editions, i.e. 802.11 a/b/g.

Users will notice two things about this new and improved wireless technology:

significantly greater speed and range. Both Intel Corp., which has a vested interested in

802.11n because it manufactures wireless chip sets, and independent reviews indicate that

the claims of greater speed and range for 802.11n are true. Main reason for greater speed

and range is the use of antenna array instead of single antenna element used in 802.11 b

and g standards. (Karnath)

Specifically, 802.11g products, which have a theoretical maximum throughput

speed of 54Mbit/sec., typically provide real-world speeds of 22Mbit/sec. to 24Mbit/sec.

In contrast, Intel says it's seeing real-world speeds of 100Mbit/sec. to 140Mbit/sec. for

802.11n equipment.


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Range is harder to quantify because it's affected by many variables, such as

barriers that could block the signal. However, Intel reports that 802.11n equipment

typically delivers more than twice the range of 802.11g equipment, at any given

throughput speed.

At the very end of an open field with no interference, where you could get

1Mbit/sec. with g equipment, you'll net 14Mbit/sec. to 16Mbit/sec. with n

equipment, reports Intel corporation. (Gupta)

However, in real world applications networking is rarely applied in an open field.

The problems come when the strengths of these signals go down due to the presence of

external signals or the signal strength goes down due to obstructions. They cause the

network to become slow. In any home networking system, natural obstructions, i.e.

walls, can cause the signal strength to be attenuated, and restricting the distance the signal

can be sent to maintain the same quality. Any household items which emit any radiation,

such as a microwave, cell phone, or even cordless phones, add a lot of extra interference,

or digital noise, to the network.

Both, noise and the interference add errors in the received information and limit

the distance over which one can communicate and causes the speed of communication to

drop. In my project I am focusing on how to cancel out the signals received from an

interferer while letting the signals from the desired source pass through. I developed a

new processing algorithm in which the computer automatically cancels the interference

while still maintaining the integrity of the wanted signals. I wrote the computer program

using C programming language to simulate my algorithm. This computer program is then

used to show how well my method works irrespective of the direction of the interferer


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and compare the performance of my method against regular beamforming currently used

in many of the 802.11n or other wireless networking schemes.


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3. Purpose

With increasingly large number of laptops, computers, printers, and cable

modems in a house, the need to be connected becomes vital. Having a home networking

system is not only the fastest, but also the most convenient is highly desirable. One of the

most popular ways to do so is by means of a Wireless Local Area Network (WLAN), in

which all the devices emit signals which are picked up and transferred through a router or

hub. The problems come when the strengths of these signals go down due to their

distance or the presence of external signals causes the network to become slow. In any

home networking system, natural obstructions, i.e. walls, can cause the signal strength to

be attenuated, and restricting the distance the signal can be sent to maintain the same

quality. Any household items which emit any radiation, such as a microwave, cell phone,

or even cordless phones, add a lot of extra interference, or digital noise, to the network.

Both, noise and the interference add errors in the received information and limit

the distance over which one can communicate and causes the speed of communication to

drop. In my project I am focusing on how to cancel out the signals received from an

interferer while letting the signals from the desired source pass through. I developed a

new processing algorithm in which the computer automatically cancels the interference

while still maintaining the integrity of the wanted signals. I wrote the computer program

using C programming language to simulate my algorithm. This computer program is then

used to show how well my method works irrespective of the direction of the interferer

and compare the performance of my method against currently used method.


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4. Hypothesis

I predict that by using the adaptive interference reduction method, I will be able to

decrease the amount of interference received at any angles away from the source by a

factor of 10. By using this method I will be able to halve the number of antennae

elements needed in an array.

5. Procedure

In my project, I used the mathematical models of antennae array, electromagnetic

waves captured by the array, radiation from an unwanted source and the signal processing

to be done by the array in wireless networking environment. Based on these models, I

developed an advanced adaptive algorithm to cancel out the interfering signal. I then

developed a software program using C programming language to simulate the

mathematical models and my new adaptive signal processing method.

5.1 Theory of Antennae Arrays

Equation of an electromagnetic wave e(t,r) is given: (Steinberg).

e(t,r) = A(t) sin (2 t 2 r /) (1)

where is the frequency of the wave, is the wavelength, t is the time, r is distance

traveled by the wave, A(t) is the amplitude corresponding to the information carried by

the sine wave (Steinberg). For simplicity and without loss of generality, A(t) is assumed

to be 1 for the desired source here in this project. The amplitudes for interferences are

relative to the desired source. Frequency and the wavelength are related by

c = (2)


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where c is speed of the wave given approximately by 3x1010 cms/sec.

Let the first antenna element be at coordinate (0, 0). The wave received at the first

element is given by substituting r = 0 in equation (1), thus giving us:

e(t,0) = sin (2 t) (3)

The electromagnetic wave arriving at second element travels r0 cm more (see Fig. 1).

Therefore, the wave received at the second element is given by: (Steinberg)

e(t, r0) = sin (2 t 2 r0 /) (4)

Let d be the distance between two antennae elements. From Fig. 1, r0 is given:

(Steinberg).

r0 = d sin (5)

in which d is the spacing between the elements and is the angle of arrival (which is

same the direction angle of the source) relative to the normal to the line containing the

antennae array. From equations (4) and (5), we get the equation of the wave received at

the second element as: .

e(t,d) = sin (2 t 2 d sin /) (6)

Now if we combine the output of these two elements, we get (Steinberg)

e(t,0) + e(t,d) = sin (2 t) + sin (2 t 2 d sin /) (7)

In general, the output y(t) of N antennae element array will be .


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y(t) = i = 0, N-1 w(i)*e(t, i*d)

= i = 0, N-1 w(i)*sin (2 t 2 i d sin /) (8)

where, w(i) is the weighting applied to the signal received at ith antenna element before

adding it. In my experiment w(i) is a raised cosine equation as given by:

w(i) = 1 + cos ( -pi + ( 2 * pi * (i + 1) ) / ( n+1 ) ) (9)

Beam Forming

If a source is in direction other than = 0 then the signal coming from that source will

not add in phase and the signal at the output of the array will be smaller in comparison to

our source signal . If we have the desired source in the direction of and an undesired

source (referred as interferer) in = 0 then the at the output of the array signal

corresponding to the desired source will increase linearly with the number of elements

but the signal corresponding to the interferer will not increase in the same way . This

process is also called beam forming where we are able to look in one given direction

while reducing the signal coming from other direction . Beam forming is used in 802.11n

standard.

Therefore, having an antenna array helps in reducing the signals coming from various

interferers (e.g., microwave ovens, cordless phones etc.).


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y(t)

x xxx

+

d

Antenna

Array

Raysr

w1w2w3wN

Weights W

Out ut

Fig.1

Conventionalarray

proce

ssing


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5.2 New Algorithm

My adaptive algorithm is based on the observation that when various antenna

elements pick up the signal from an interferer, the phases of the received electromagnetic

waves are different than those corresponding to the wave received from the desired

source. This is due to the fact they are in different directions. Therefore, if the signals

received at each element are weighted before summing them together to produce an

output, those weightings impact the signals from the desired source and the interferer

differently. Using this to my advantage, I apply two different weightings W and V to

received signals, as shown in Fig. 2 and given by the equation:

y(t) = i = 0, N-1 w(i)*sin (2 t 2 i d sin /) + a*i = 0, N-1 v(i)*sin (2 t

2 i d sin /)

Phase of the signal from the desired source in the look direction remains the same

for both the weightings. However, the phase and amplitude of the interfering signal is

different. Therefore, these two weighted outputs now can be manipulated and added such

that the interfering signals present in these outputs cancel each other out. One of the

weighted output is multiplied by a factor a before the addition. The factor a is

adjusted adaptively to provide maximum rejection. This value of a adaptively changes

depending on the direction of the interference. In my algorithm I used weights W = [w0,

w1, wN-1] to be as a raised cosine function where different weights wi are shown in

Graph 1. The weights V = [v0, v1, vN-1]were all taken to be 1.

To find the optimal a, I put the array in the training mode where the desired

source is temporarily and momentarily turned off. The optimal a is then found by


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finding the value of a that minimizes the output signal shown in Fig. 2. Once that a is

found it is then applied with the desired source turned on.

I also developed C programming methods to characterize the newly created

algorithm and study the impact of number of elements and directions of interfere. I also

conducted several experiments to compare how much improvement my adaptive

processing algorithm provides over the conventional fixed processing. I plotted and

tabulated the results.


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y(t)

x

x

x

x

+

d

Antenna

Array

Rays

r

w1

w2

w3

wN

Weights

W

A

daptive

W

eight

Opt

imization

Output

+

v1

v2

v3

vN

+

a

WeightsV

Fig.1A

daptiveAntenna

Arr

ayGeometry

y(t)

x

x

x

x

+

d

Antenna

Array

Rays

r

w1

w2

w3

wN

Weights

W

A

daptive

W

eight

Opt

imization

Output

+

v1

v2

v3

vN

+

a

WeightsV

Fig.1A

daptiveAntenna

Arr

ayGeometry

Fig.2

Adaptivea

rrayprocessing

ofmy

method


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6. Simulation Experiment Results

I wrote a program using c programming language to simulate various experiments

described. Results obtained by conducting simulated experiment are provided below. The

program is given in Appendix A of this document.

Graph 2 shows the desired signal at the output of an antenna array with 5

elements without any interference. The desired signal is simulated to be a sine wave.

Graph 3 shows output signal of the same array in the presence of interference. The

interference amplitude was 10 times the amplitude of the wanted signal and it was located

at 30 degrees. Clearly, interference significantly distorts the desired signal. It will not be

possible to send data from the desired source in this case. Graph 4 shows the output of the

same antenna array after using my adaptive algorithm. The value of the optimum a in

this case was 0.79. Comparing Graph 2, 3 and 4, we can clearly see that my adaptive

interference rejection algorithm performs significantly better than conventional array

method that is used today. Graph 5 shows the output signal of an antenna array with 11

elements but using the conventional methods. The distortion in the received signal is still

visible.

Graph 6 shows a time plot at the output of array in the absence of interference

with information bits modulating the sine wave carrier. The information bits being

carried are 0, 1, 0, 1, 0 Graph 7 shows a time plot at the output of array in the presence

of interference using the conventional method with information bits modulating the sine

wave carrier. Note that due to interference the 0 bits in 0, 1, 0, 1 would be received as

1 because the signal crosses the half of peak threshold. This corrupted signal would

make the transmitter resend the data. This repetition of data sending slows down a

network, because the bites must first be able to be read in order to carry out the process,


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e.g. go to a webpage, upload or download files. Graph 8 shows a time plot at the output

of array in presence of interference using my adaptive method with information bits

modulating the sine wave carrier. In this graph, note that the bites are near perfect, and

everyone is being read as what it is supposed to be.

Graph 9 shows the array patterns for the conventional array and after using my

processing for 5 element array. The attenuation at the 30 degrees, in the direction of the

interferer, is 0.0005216. Graph 10 shows the array patterns for both the conventional and

adaptive methods for a 5 element array, however the interferer location has changed to 50

degrees. As it can clearly be seen the adaptive method moves the null to the location of

the interference. In the conventional method 55 degrees is at the height of the beam, and

thus my method proves to be around 6500 times better. The effect of my processing is

such that it automatically creates a null in the direction and attenuates the signal coming

from that direction by an average factor of 100 more than the conventional array.

Table 1 shows the value of a obtained for various locations of the interferer.

Table 2 shows the interference rejection as function of angle for conventional and my

adaptive algorithm. It also shows that when compared to the conventional method my

adaptive signal processing method on average reduced interference by a factor of over

100. Graph 7 plots the optimal a values obtained at various locations of the interferer.

Graph 8 plots the results comparing the interference rejection of the conventional and my

new adaptive algorithm for various angular locations of the interferer.


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Graph 1. Displays the Raised Cosine weighting along with the equal weighting

Graph 2. Displays the time plot at the output in the absence of any interference for a 5antenna array

0

0.5

1

1.5

2

2.5

0 1 2 3 4 5 6

Antennae Elements

Weights

Raised Cosine

Equal

-1.5

-1

-0.5

0

0.5

1

1.5

0 50 100 150 200

Time Plot

Am

plitude


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Graph 3. Displays the time plot at the output in the presence of interference for a 5antenna array using the conventional method

Graph 4. Displays the time plot at the output in the presence of interference for a 5antenna array using my signal processing method

-1.5

-1

-0.5

0

0.5

1

1.5

0 50 100 150 200

Time

Amp

litude

-1.5

-1

-0.5

0

0.5

1

1.5

0 50 100 150 200

Time

Amplitude


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Graph 5. Displays the time plot at the output in the presence of interference for an 11antenna array using the conventional method

Graph 6. Time plot at the output of array in the absence of interference with informationbits modulating the sine wave carrier. Information bits shown are 0,1,0,1,0

-1.5

-1

-0.5

0

0.5

1

1.5

0 50 100 150 200

Time

Amplitude

-1.5

-1

-0.5

0

0.5

1

1.5

0 50 100 150 200Amplitude


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Graph 7. Time plot at the output of array in the presence of interference using theconventional method with information bits modulating the sine wave carrier. Information

bits shown are 0,1,0,1,0

Graph 8. Time plot at the output of array in presence of interference using my adaptivemethod with information bits modulating the sine wave carrier. Information bits shown

are 0,1,0,1,0

-1.5

-1

-0.5

0

0.5

1

1.5

0 20 40 60 80 100 120 140 160 180 200Amplitude

-1.5

-1

-0.5

0

0.5

1

1.5

0 50 100 150 200Amplitude


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Graph 9. Comparing the Beam pattern for my method against that of conventionalmethod for interferer at 30 degrees

Graph 10. Comparing the beam pattern for my method against that of conventionalmethod for interferer at 55 degrees.

Interference Cancellation

-3.5

-3

-2.5

-2

-1.5

-1-0.5

0

-100 -50 0 50 100

Degrees

Gain My Method

ConventionalMethod

Location of Interferer

Interference Cancellation

-3.5

-3

-2.5

-2

-1.5

-1-0.5

0

-100 -50 0 50 100

Degrees

Gain

My Method

Convention

al Method

Location of Interferer


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Table 1- Optimal a value for various locations

Degrees a Value

30 1.00

35 0.33

40 0.07

45 -0.11

50 -0.44

55 0.69

60 0.14

65 0.05

70 0.02


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Table 2 Interference reduction in conventional and my method

Degrees ConventionalMethods My Method

Ratio of

Conventional toMy Method

30 0.143156 0.0005216 274.4519

35 0.069445 0.0007516 92.83219

40 0.01244 0.0004606 27.00649

45 0.013785 0.0002780 49.59859

50 0.0207058 0.0001605 128.9874

55 0.0179408 0.0000026 6781.642

60 0.0119875 0.0001619 74.02433

65 0.0064258 0.0002888 24.17334

70 0.002875 0.0001025 28.06

Average 0.033233 0.0003032 109.5905


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Graph 7: Optimal a values for various locations of interference

Graph 8: Leftover interference strength after using my adaptive method and theconventional method

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

25 35 45 55 65 75

Degrees

Optimal"a"value

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

25 35 45 55 65 75

Degrees

Interfere

nceStrength

Conventional

Method

My Adaptve

Method


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6. Conclusion

The objective of my project was to develop and simulate an adaptive processing

in which an antennae array can automatically provide the optimum interference rejection

without specifically knowing from which direction the interference is coming. This was

done by simulating a second, self-referencing, array which was given different weights,

and multiplied by an adaptive weight optimizer which when added to the original antenna

array cancelled the interference. Computer software was written, using C programming

language, to simulate the antennae array and conduct the experiments.

My hypothesis that by using the adaptive signal processing method I will be able

to decrease the interference more than the current fixed method was correct. The data

clearly shows that after this new developed algorithm the interference in the 5 antenna

array decreased on average by more than a factor of 100 when compared to the same

antenna array without my adaptive method.

My hypothesis that we will be able to greatly reduce the number of antennae

elements in an array was also correct. The data showed the signal we received and the

amount of interference we rejected without using this adaptive algorithm. As shown in

the data, by using my new adaptive signal processing, we obtained the better results while

using only 5 elements as opposed to 11.

Therefore, my design is much more versatile and robust to diverse wireless

networking environments where the interferers can be located in wide range of directions.

As it is more efficient than traditional antennae array processing, it can either be used to

increase the effective distance of operation of the network or to reduce the cost by

reducing the number of antennae elements required to achieve a desired level of

interference rejection.


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This technology can be applied to anything around the world which requires any

sort of wireless communication, or any transmission of information from a source to a

receiver, such as cellular networks, home networks, and wide and local area networks.


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Works Cited

Apple. 802.11n. 2008. Online. Internet. 1 January, 2008.

http://www.apple.com/wifi/80211/

Gupta, Ashish. 802.11n Wireless Networking. 11 October, 2007. Online. Internet.

Griffith, Eric. Hotels and Intel to Market Wi-Fi. 27 December, 2007. Online. Internet. 2October, 2007.

Haskin, David. FAQ: 802.11n wireless networking. 16 May 2007. Online. Internet. 15November, 2007

Haykin, S. Adaptive Filter Theory, New Jersey: Prentice Hall, 1991.

Heltzel, Paul. Practical Home Wireless Networking. Los Angeles: Prentice Hall TradeComputing, 2003.

HomeNetHelp.com. http://www.homenethelp.com/web/review .

Hoskin, Robert. 802.11g Wireless Internet Access 2006. Online. Internet. 15 December,2007.

Karnath, John-Paul. 802.11n to overtake wired Ethernet. 08 October 2007. Online.Internet. 24 November 2007.

Kuc, Roman. Introduction to Digital Signal Processing, New York: McGraw-Hill BookCompany, 1988.

Luthra, Ajay. Personal Interview. 28 November, 2007.

Luthra, Tarang. Personal Interview, 1 September 2007 and 26 December, 2007.

Meyer, Cheryl. Mobile Hotspots on Campus. 10 December, 2002. Online. Internet. 1January, 2007. < http://www.wifi-planet.com/columns/ >

Murch, Ross. Antenna Systems for Broadband Wireless Access.IEEECommunications Magazine. 1 April, 2002: 76-83.


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Wearden, Graeme. Wi-Fi hot spot lands at UK Airport. 17 September 2002.

Prata, Stephen. C Primer Plus, Indiana: Howard W. Sams & Co., Inc, 1984

Smith, Ken. Airport Gets Neutral-Host Wi-Fi. 10 April, 2002. Online. Internet. 28

September, 2007.

Steinberg, Bernard. Principles of Aperture and Array System Design. New York: Wiley-Interscience, 1976.

Thurber, James. Today Wireless, TomorrowWho Knows?Electronic Design, 50th

Anniversary Commemorative Issue. 21 October, 2002: 94-98.

Tyson, Jeff. How Wireless Networking Works. 23 August, 1998. Online. Internet.January 28, 2003. http://www.howstuffworks.com/wireless-network.htm/printable>

Wilson, James M. Quadrupling Wi-Fi speeds with 802.11n. 9 August 2004. Online.Internet. January 4, 2008.http://www.deviceforge.com/articles/AT5096801417.html

Wi-Fi Alliance. Online. Internet. November 2007 < http://www.wi-fi.org>

Wu, Tilden. 802.11 on the Desktop An Analysis of the Benefits and Issues of BringingWireless to the Desktop. 16 July, 2002. Online.ftp://download.intel.com/design/motherbd/articles/wireless.pdf

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