Communication Techniques for Autonomous(self-driving

THE HONG KONG POLYTECHNIC UNIVERSITY

DEPARTMENT OF ELECTRICAL ENGINEERING

Project ID: FYP_94

Communication Techniques for Autonomous(self-driving)

Vehicle Systems

by

TSE Yip Ming

14068068D

Final Report

Bachelor of Engineering (Honours)

in

Transportation Systems Engineering

Of

The Hong Kong Polytechnic University

Supervisor: Dr Alan P.T. Lau Date: 28 March 2018



Abstract

Wireless technology has been part and parcel of everyone’s life. Nonetheless, on top of

applying such a technology on the small gadgets, autonomous vehicle system for self-

driving can benefit from Wi-Fi communication to be more technologically mature.

Researchers have adopted various technique to develop this industry, yet not solve the

particular issue on autonomous vehicle system, especially on moving vehicles. The

distance value between vehicle and nearby roadside router determines the quality of

data transmission (described inversely by the Bit Error Rate) which depends on

different types of modulation (a process to transmit the signal through a communication

channel effectively by changing the properties of its own). Therefore, the proposed

method is to maximize transmission speed in an autonomous vehicle communication

by exploiting different types of modulations corresponding to the various distance

between transmitting end and receiving end. A systematic series of tasks with MATLAB

are used to demonstrate the feasibility of the method. For both Rayleigh Fading Model

and Rician Fading Model, the proposed method is found to outperform the traditional

method of using one type of modulation only in case of the received total bit at receiver

end against different vehicle speeds. After the recognition of the performance of the

proposed method, it is then put into practice by adopting adjustments for the purpose

of designing a well-rounded vehicle communication system through a new time-

division multiple access method such that every vehicle receives the same amount of

data. The result of simulation of applying the design vehicle communication system in

a more realistic road environment reflects that the design system is appropriate to be

utilized in reality. The optimal antenna distance obtained in the simulation provided the

best indication for placing the antenna, reducing the social cost of and time required for

numerous trials of various distances in practice.



Contents

1 Introduction ....................................................................................................................... 1

1.1 Objectives ................................................................................................................. 3

2 Literature Review .............................................................................................................. 4

3 Technical Skills and Methodology ..................................................................................... 6

3.1 The general process of WIFI communication .......................................................... 6

3.2 Noise Signal and Additive White Gaussian Noise (AWGN) ...................................... 8

3.3 Modulation ............................................................................................................. 10

3.3 Quadrature Amplitude Modulation (QAM) ........................................................... 11

3.4 Constellation diagrams for QAM............................................................................ 13

3.5 The wireless channel and Fading Model ................................................................ 15

3.6 Path Loss ................................................................................................................ 16

3.7 Shadowing .............................................................................................................. 18

3.8 Multipath ............................................................................................................... 19

3.9 Fading .................................................................................................................... 20

3.10 Fading Model ........................................................................................................ 22

3.11 Multiplexing and Multiple Access ......................................................................... 24

4 Progress and Simulation Results ..................................................................................... 28

4.1 BER against SNR for 4-QAM, 16-QAM and 64-QAM .............................................. 28

4.2 BER against SNR for 4-QAM, 16-QAM and 64-QAM with Fading Model ............... 29

4.3 Simulation of A Roadside Antenna Environment................................................... 31

4.4 The Proposed Method in multiple car situation .................................................... 41

4.5 The Proposed Method in multiple car situation with many antennas .................. 45

5 Conclusion and Future Development .............................................................................. 48

5.1 Conclusion .............................................................................................................. 48

5.2 Limitation ............................................................................................................... 49

5.3 Future Development .............................................................................................. 49

6 References ....................................................................................................................... 50



7 Appendix .......................................................................................................................... 52

7.1.1 Generation of BER against SNR for 4-QAM, 16-QAM, 64-QAM ............................. 52

7.1.2 Plot BER against SNR for 4-QAM, 16-QAM, 64-QAM ............................................. 54

7.2.1 Generation of BER against SNR for 4-QAM, 16-QAM, 64-QAM (Fading Model) ... 55

7.2.2 Plot BER against SNR for 4-QAM, 16-QAM, 64-QAM ( Fading Model ) ................. 58

7.3.1 Distance Function................................................................................................... 58

7.3.2 SNR dB Function ..................................................................................................... 58

7.3.3 BER Function .......................................................................................................... 59

7.3.4 QAM Order Function .............................................................................................. 61

7.3.5 Bits Rate Function .................................................................................................. 62

7.3.6 Total Bits Function ................................................................................................. 62

7.3.7 Plot Total Bits against Speed Function ................................................................... 63

7.3.8 Plot ALL Type Total Bits against Speed Function ................................................... 64

7.4.1 Create Car Start Point Function ............................................................................. 65

7.4.2 Distance km/hr to m/s unit Function ..................................................................... 65

7.4.2 Check Car QAM Order Function ............................................................................. 66

7.4.3 Traditional Time Division Method Function .......................................................... 67

7.4.4 Designed Time Division Method Function ............................................................. 67

7.4.5 Plot Traditional against Designed Time Division Method Function ....................... 68

7.5.1 Advanced Car QAM Order with Many Antennas Function .................................... 72

7.5.2 Travel with Many Antennas Function .................................................................... 75

7.5.3 Optimal Antenna Distance Function ...................................................................... 77

7.5.4 Plot Optimal Antenna Distance against Speed Function ....................................... 78



1

1 Introduction

With wireless technology developing in leaps and bounds, there is no one but utilizes

Wi-Fi on his or her smartphone to directly connect to the internet, so that everyone can

readily enjoy watching videos, playing games, and the list goes on. Nonetheless, never

should the pace of such a state-of-the-art technology stops on the small gadgets. In fact,

wireless communication is capable of being applied to a larger field – autonomous(self-

driving) vehicle systems. Notwithstanding the benefits, viz. improved road safety,

diminished emissions and enhanced mobility [1], brought by autonomous vehicle

systems, self-driving is technologically viable yet not sufficiently mature to be

prevalent.

Attracted by the advantage of autonomous vehicle system, scholars have adopted

different approaches such as applying training signal, coding technique and high-order

modulation to increase the performance of data transmission. There is no doubt that

their effort has a large contribution to the development of wireless communication, but

the problem of dealing with a particular area especially on self-driving system, the

moving vehicles issue, is still not solved. The distance value between vehicle and

vehicle or nearby roadside router is, in fact, an important factor to the quality of data

transmission which is described inversely by the Bit Error Rate (BER). At the same

time, the quality of data transmission in terms of the data rate also depends on different

types of modulation which are a process to transmit the signal through a communication

channel effectively by changing the properties of its own [2].

Maximization of the transmission speed of wireless communication in autonomous

vehicle network to the fullest is one of the possible approaches contributing to the



2

advancement in self-driving technology, which can be achieved by taking advantage of

different types of modulations corresponding to the various car-to-roadsides distance.

In signal processing, there exist modulation types from low-order to high-order in order

to transmit the signal effectively. A high-order modulation provides a high data rate

only at a short distance, but it lifts the Bit Error Rate at a long distance, while a low-

order modulation is capable maintaining a low Bit Error Rate in long-distance

communication, yet it is restricted by a low data rate.

Under the circumstance that vehicles are moving on a road, if the contemporary fixed

modulation is applied, either the data rate will be low, or Bit Error Rate will be high,

resulting in poor wireless communication. Hence, the proposed method is to harness

different modulations corresponding to various car-to-roadsides distances.

The proposed idea is achieved by a systematic series of tasks. MATLAB is used in

simulating vehicle communications. Rayleigh Fading Model and Rician Fading Model

are applied to imitate the authentic road environment. Several simulations are carried

out to figure out the different modulations at various Bit Error Rates to distances, to

design an optimal system to maximise the transmission speed in autonomous vehicle

network, and finally to compare the proposed method with the conventional method.



3

1.1 Objectives

1. To analyze the bit error rate against the distance between vehicles in different

modulation types.

2. To introduce the concept of correlation between Bit Error Rate in different

modulation types respect to various car-to-roadsides distance.

3. Use MATLAB stimulation to demonstrate the concept experiments and to compare

the proposed method with the conventional method.

4. Design an optimal system to maximize the transmission speed in autonomous

vehicle network by finding the optimal antenna distance for different vehicular

speed



4

2 Literature Review

The data transmission rate is high at a low-level Bit Error Rate in multiple-antenna

wireless communication. Hassibi and Hochwald [3] investigated the method of

adjusting the amount of training symbol to increase the data transferred. In general,

well-defined training signals are required to learn the knowledge of channel by sending

it from transmitter antenna to receiver antenna in some fraction of the transmission

period. This process is called training which has an influence on the capacity of the

fading channel. If training is not enough, the knowledge of the channel cannot be

obtained. If the training is excessive, the result will be an insufficient time for data

transmission before switching to the next channel. Therefore, the amount of training

symbol determines the data rate. In their study, there is an investigation into the

relationship between the amount of training and the number of transmit antenna. From

the experiment which is based on a satisfactory minimum requirement on the amount

of training for obtaining the capacity of the channel, the research team found that the

optimal solution is that the required number of training symbols should be the same as

the number of antennas, such that the training time in the transmission period is at a

minimum.

Currently, the bandwidth for wireless communication is limited which makes it difficult

to achieve a speedy data transmit. Tarokh et al. [4] adopted an approach which deals

with the physical layer through coding for improving the data transmission. They used

a multiple transmit antennas model and a space-time code to demonstrate their idea.

The complexity of the coding is similar to the trellis codes in the Gaussian channels.

The encoded data is divided into n streams and transmitted by n number of antennas at



5

the same time; then it is received by the receiver in terms of a superposition of n divided

streams with the additive noise. A delay diversity scheme is proposed to perform a copy

of the transmit signal which is less suffered from attenuation. In this scheme, the signal

is transmitted from multiple antennas but with a delay of one symbol rate. This method

ensures a high-quality signal in wireless communication without increasing the

bandwidth. In the modulation of 4-PSK and 8-PSK, this coding in their research has a

good performance with a low-level outage when using 2 bits per symbol with 64 state

encoders.

Another possible way to enhance the data transmission is adopting a high-order

modulation technique with a specified error-correcting code in WIFI communication.

This method can increase the amount of data transmitted per second. In general, a high-

order modulation 16-PSK is better than low-order modulation 4-PSK which is proposed

in [5]. However, the tradeoff increases the power input for the same Bit Error Rate. As

a result, a coding is required to deal with the power efficiency problem, but at the same

time, it increases the bandwidth by adding these codes into a transmitted symbol

sequence, lowering the bandwidth efficiency. In this situation, the research team

suggests increasing coded symbols for the redundancy of their proposed error-

correcting coding process. This code could be used to improve the power efficiency in

the AWGN channel without increasing the bandwidth. They have carried out several

experiments on the high-order modulation with this coding, 16-PSK, 16-QAM, and a

16-APK, under a limited band channel. The study pointed out that 16-PSK has an

excellent performance in data transmission. The 16-PSK modulation requires 5dB less

in signal to noise ratio than the 8-PSK modulation at the same symbol rate.



6

3 Technical Skills and Methodology

Few necessary relevant theories and terminologies are introduced here to illustrate the

proposed vehicle communication technique in the next chapter.

3.1 The general process of WIFI communication

A WIFI communication consists of several processes which include coding, modulation,

demodulation and decoding as illustrated in [6, Fig.1]. In this report, the focus only lies

on modulation, so it will be discussed in the later part.

In general, the information source is transmitted from the transmitter to the receiver,

and then the information is achieved in the destination such as computers in the vehicle.

The physical transmission medium which this signal passes through is called “channel”.

During this transmission, the noise will be introduced into the system as seen in [7,

Fig.2].

Figure 1 Flow Diagram of Information Processing.



7

Figure 2 General communications system.

For example, a sin wave source signal is to be transmitted from the transmitter to the

receiver in a communication system. The receiver end is supposed to receive a sin signal

identical to the source signal at the transmitter end as seen in [8, Fig.3], which is

represented by the dashed line. Under the introduction of noise in the communication

system, the received signal will mix up the source signal and the noise which distributes

the signal from the original, which is represented by the solid line. As a result, if the

amount of noise is too large, the source signal will be greatly affected, and it is difficult

for the receiver end to figure out if it is receiving a sin wave signal.

Figure 3 Received Signal with different amount of noise.



8

3.2 Noise Signal and Additive White Gaussian Noise (AWGN)

Noise is an unwanted signal that will cause interference with the source signal, and yet

it is usually unavoidable in the communication system [8]. There are a variety of noise

sources ranging from the external sources of the system to the internal sources of it, for

example, atmospheric noise caused by natural atmospheric processes and thermal noise

in the electrical circuit [9]. In general, Additive White Gaussian Noise (AWGN) model

is used to simulate the noise signal. In AWGN model, the mean is zero, and the variance

is the same as the average noise power with a Gaussian distribution of amplitude as

seen in Figure 4.

Figure 4 Gaussian distribution with mean zero.

Hence, Additive White Gaussian Noise (AWGN) = variance * 𝑍𝑖~𝑁(0, 𝜎) [10],

Sigma, σ = √(𝑃𝑁

2)

Nose Power, PN = k To BRF (NF) [11]



9

Symbol Notation

k Boltzmann Constant

To Temperature in Kelvin

BRF Bandwidth

NF Noise Figure

This property helps to simulate the effect of the “random process” of noise in nature.

In general, Signal-to-Noise Ratio SNR which is the ratio of Signal Power to the Noise

Power is used to express the quality of signal [11]. The higher the SNR value, the higher

quality of signal will be

SNR =𝑃𝑆

𝑃𝑁

Where 𝑃𝑇𝑋 refers to Signal Power. In general, SNR is always measured in dB unit.

Therefore, the formulas are

SNR (dB) = 10 log SNR

Sigma = √(𝑃𝑇𝑋

2 ∗ SNR)

Sigma = √(𝑃𝑇𝑋

2 ∗ 10𝑆𝑁𝑅𝑑𝐵

10

)



10

3.3 Modulation

Figure 5 Flow Diagram of Information Processing.

It is a process to transmit the signal through a communication channel effectively by

changing the properties of its own and is an important part in the stream of the

communication system as seen in [2, Fig.5]. For example, a sin wave signal x(t) is

changed and modified into a new form of signal SPM(t) or SFM(t) through two different

modulation techniques as seen in [12, Fig.6].

Figure 6 Phase Modulation and Frequency Modulation Note6.



11

There are three types of fundamental digital modulation method which are Amplitude-

Shift Keying (ASK), Frequency-Shift Keying (FSK), and Phase-Shift Keying (PSK),

as well as a complex modulation type so-called Quadrature Amplitude Modulation

(QAM) [10].

In this project, the modulation type QAM will be focused. The idea of this project is

how to take advantage of the different type of QAM, 4-QAM, 16QAM and 64QAM, in

order to maximise the transmission speed.

3.3 Quadrature Amplitude Modulation (QAM)

Quadrature Amplitude Modulation (QAM) is a process of transmitting two Double

Sideband (DSB) signal as illustrated in [2, Fig.7] with same frequency simultaneously.

Figure 7 Double Sideband Signal.

One of the signals will have a phase different by 90 degrees to the other, thus achieving

the name Quadrature Modulation. Therefore, if m1(t) and m2(t) are the original signals,

then they will become m1(t)cos(Wct) and m2(t)sin(Wct) respectively while the

trigonometric function serves as a carrier with carrier frequency Wc to carry those

signals.



12

At the sending end, m1(t)cos(Wct) and m2(t)sin(Wct) will be added together to form

QAM(t) for sending [2]. Sending Signal:

QAM(t) = m1(t)cos(Wct)+m2(t)sin(Wct)

where m1(t) and m2(t) are the original signals

Figure 8 Quadrature Amplitude Modulation (QAM).

At the receiving end, the QAM signal will be demodulated by multiplying the term

2cos(Wct) and 2sin(Wct), that is

x1(t)= QAM(t) x 2cos(Wct) = m1(t) + m1(t) cos(2Wct) + m2(t)sin(2Wct)

x2(t)= QAM(t) x 2 sin(Wct) = m2(t) − m2(t) cos(2Wct) + m1(t)sin(2Wct)

Then, the signals are filtering by a suitable low-pass filter to remove the last two term

of x1(t) and x2(t). As a result, the receiver end could return the original signals m1(t)

and m2(t) as illustrated in [2, Fig.8].



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3.4 Constellation diagrams for QAM

QAM(t) = m1(t) cos(Wct) + m2(t) sin(Wct) can also be written as

QAM(t) = R{ [ m1(t)- j m2(t) ] e jWct

} ,

hence s(t) = m1(t)- j m2(t) is obtained [10].

For 4-QAM, s(t) = ±1 ± 1 j ,

the 4-QAM constellation diagram is shown as bellow in Figure 9.

Figure 9 4-QAM constellation diagram.

For 16-QAM, s(t)= ±1 ± 1 j, ±1 ± 3 j ,

±3 ± 1 j , ±3 ± 3 j

the 16-QAM constellation diagram is shown as below in Figure 10.




14

For 64-QAM, s(t)= ±1 ± 1 j, ±1 ± 3 j , ±1 ± 5 j , ±1 ± 7 j ,

±3 ± 1 j, ±3 ± 3 j , ±3 ± 5 j , ±3 ± 7 j ,

±5 ± 1 j, ±5 ± 3 j , ±5 ± 5 j , ±5 ± 7 j ,

±7 ± 1 j, ±7 ± 3 j , ±7 ± 5 j , ±7 ± 7 j

the 16-QAM constellation diagram is shown as below in Figure 11.


As the purpose is to compare the efficiency of 4-QAM, 16-QAM and 64-QAM. The

transmitted power of all these types are same and are set as 1W. Therefore, s(t) should

be multiplied by a scaling factor for achieving N order of QAM in the condition of 1W

transmitted power. Let b be the Scaling Factor.

For 4-QAM,

(1b)2 × 2 × 1 × 4

4= 1W,

hence the Scaling Factor is 1

2 .



15

For 16-QAM,

[(1b)2 + (3b)2] × 2 × 2 × 4

16= 1W,


10 .

For 64-QAM,

[ (1b)2 + (3b)2 + (5b)2 + (7b)2] × 2 × 4 × 4

64= 1W,


42 .

3.5 The wireless channel and Fading Model

Wireless channel is the path of an electromagnetic wave through radiation process from

the transmitter antenna to the receiver antenna. The channel strength varies over time

and frequency as illustrated in [13, Fig.12].

Figure 12 Channel quality over time.



16

In general speaking, it can be determined by the distance and path between the

transmitter antenna and the receiver antenna. If the information of the wireless channel

is well-known, the figure of the received signal can be achieved. There are three

fundamental components to describe the characteristic of the wireless channel, path loss,

shadowing and multipath.

3.6 Path Loss

Path loss is caused by transmitted power dissipation with propagation channel effect

which expresses in Friis Transmission Equation [14] as shown in [15, Fig.13],

PL =𝑃𝑇𝑋

𝑃𝑅𝑋=

(4𝜋𝑑)2

𝐺𝑇𝑋𝐺𝑅𝑋𝛌𝟐

Symbol Notation

PL Path Loss

PTX Transmitted Power

PRX Received Power

d Distance between transmitter and

receiver

GTX Transmit Antenna Gain

GRX Receive Antenna Gain

λ Wavelength



17

Figure 13 Path Loss in a system.

In technical speaking, the received signal power decreases drastically with the

increase of the distance between the transmitter antenna and the receiver antenna.

With previous equation, path loss can be written as

PL =𝑃𝑇𝑋

𝑆𝑁𝑅 𝑅𝑋 ∙ 𝑃𝑁

=(4𝜋𝑑)2


PL =𝑃𝑇𝑋

𝑆𝑁𝑅 𝑅𝑋 ∙ k ∙ To ∙ BRF ∙ (NF)=

(4𝜋𝑑)2


PL =𝑃𝑇𝑋

𝑆𝑁𝑅 𝑇𝑋 ∙ k ∙ To ∙ BRF =

(4𝜋𝑑)2


Therefore, SNR is proportional to 1

𝑑2 .



18

3.7 Shadowing

Shadowing can be resulted in when there exist obstacles along the direction of the

signal path between the transmitting end and receiving end. In this case, the signal

power will be diminished due to a loss of some transmitted signal through a chain of

process of absorption, reflection, scattering, as well as diffraction [14].

As shown in [16, Fig.14], the signal is originally transmitted from the Transmitter (Tx)

to the receiver (Rx), however, as there is an obstacle in cylinder shape situated in-

between the two antennas; as a result, the signal will undergo reflection and diffraction,

which are signified as reflected ray and diffracted ray. This is so-called shadowing.

Figure 14 Shadowing in a system.

It should be noted that shadowing can be very problematic when there is an extremely

strong debilitating effect imposed by an obstacle since the transmitting signal can

probably be entirely blocked.



19

In contrast with shadowing, variation of the signal strength in the wake of path loss

will usually occur in a very long distance, such as 100 to 1,000 metres. Instead,

variation that is resulted from shadowing will occur in a distance which is in

proportion with the length of the obstruction, which is usually 10 to 100 metres [14].

3.8 Multipath

Based on the aforementioned sections of path loss and shadowing, since it is

inevitable to get rid of the objects and obstacles on the road in reality; thus, there are

bound to be objects situated surrounding the path of signal between the transmitter

and receiver, such that the signal will be reflected ultimately. Consideration will be

taken only on the successfully received signal which is reflected to the receiver.

As the [14, Fig.15] demonstrates that every signal is taking a different path from one

and other; as a consequence, the amplitude and phase of reflected signals will be

ultimately different as well. It can be concluded that whether the summation of these

multiple reflected signal, as well as the direct path signal will result in an increased or

decreased received signal power is in fact dependent on the phase.

Figure 15 Multipath in a system.



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It should be noted that any changes in the positions of the transmitter, receiver,

objects or obstacles, no matter how slight it is, can sufficiently give rise to a

considerable variation in received signal phases, thereby exerting impacts on the

received power as well.

3.9 Fading

Fading is generally referred to the recognizable impacts of changing received signal

strength over time. It is assumed that the result of these changes is initiated by the

variation in propagation effects from the transmitting end to the receiving end. It

should be noted that the result of these changes is not resulted from the variation in

the output power of transmitter [17].

The variation of channel strength due to the three fundamental components that are

mentioned above can be classified into two types: Large-scale fading and Small-scale

fading.

3.9.1 Large-scale fading

Large-scale fading is due to path loss caused by transmitted power dissipation with

propagation channel effect in large distance and shadowing caused by large obstacles

that absorbing, reflecting, scattering, and diffracting the transmitted power and is

usually frequency independent. The large-scale fading arises because of the

extraordinary influence on the received signal power imposed by the path loss and

shadowing as mentioned above [14].



21

3.9.2 Small-scale fading

Small-scale fading is due to the constructive and destructive interference of multipath

signal between the transmitter antenna and the receiver antenna and is frequency

dependent [14]. The difference between small-scale and large-scale fading is the much

slighter impact on the received signal power imposed by the multipath signal as

compared with the greater impact by path loss and shadowing.

The effect on channel imposed by path loss, shadowing, and multipath are shown in

[14, Fig.16] in detail, where the received power with consideration only to path loss is

represented by a thick solid line; while the received power taken account of the effect

of both path loss and shadowing is denoted by a thick dashed line; finally, the received

signal that has taken consideration of path loss, shadowing, as well as multipath is

described by a thin dashed line.

Figure 16 Path loss, shadowing, and Multipath.



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3.10 Fading Model

In general, Rician fading model and Rayleigh fading model are widely used to simulate

the wireless communication channel for which channel strength vary in an

unforeseeable way and can only be characterised statistically.

Rician fading model is used when there is a dominant Line-Of-Sight (LOS) path in

between the transmitter and the receiver as seen in [18, Fig.17]. In this situation, there

is a non-significant scattered field such that the reflected paths are weaker than the LOS

path. As a result, Rician fading mode is suitable for simulating an open area like rural

environment.

In contrast, Rayleigh fading model is used when there are large number paths scattered

by many small reflectors such that Line-Of-Sight (LOS) path is not dominant. In this

situation, there is a significant scattered field such that the reflected paths are stronger

than the LOS path. As a result, Rayleigh fading model is suitable for simulating an

urban area with a lot of buildings [13].

Figure 17 Multipath Propagation.



23

In rural environment, Rician fading model is applied and is expressed by following

equation

where KLOS is the ratio of energy in the LOS to those scattered paths, HLOS is a purely

deterministic matrix, and Hres has zero-mean Gaussian entries. If the distance between

transmitter and receiver is large, HLOS becomes one [19].

In urban environment, Rayleigh fading model is adopted and therefore KLOS is zero. It

is because there is no dominant Line-Of-Sight (LOS) path. Hence the first term of the

equation becomes zero, and the second term becomes Hres [13]. As a result, the

mathematic expression of Rayleigh fading model is

Considering the fading model, now the stimulation model’s equation becomes

𝒚[𝑚] = H ∙ 𝒙[𝑚] + 𝒘[𝑚]

where 𝒚[𝑚] is received power spectrum matrix, 𝒙[𝑚] is transmit power spectrum

matrix and 𝒘[𝑚] is AWGN spectrum matrix.



24

3.11 Multiplexing and Multiple Access

Both multiplexing and multiple access refer to the sharing of a communication resource

which can be described as follows. Whenever a given system is linked with a

communication signal, the bandwidth and time that are available for the above linkage

are communication resources. Graphically speaking, time can be represented by the

abscissa, while frequency can be shown by the ordinate in a plane which including code

could generating three sharing technique as shown in [20, Fig.18] In order to

efficaciously develop a communication system, a well-planned allocation of resources

among the users of the system shall be a prerequisite. Under this circumstance, there

will be neither wastage of block of time or frequency; hence, resources can be shared

in a fair manner among the users [21].

Figure 18 Sharing Technique.

It comes to our attention that multiple access and multiplexing are actually the same,

which can also be referred to the sharing of a communication resource. In fact, in spite

of the same meaning, there is a difference in disguise between multiple access and

multiplexing. As to the latter, there are almost slowly changing, if not fixed, sharing of

communication resource among the users’ plans or requirements. The allocation of

resources is assigned through deductive reasoning from rather than based on facts. In

addition, a local site is confined for the sharing process to take place in, where a circuit



25

board is a telling example. In contrast, with regard to multiple access, remote sharing

of the resource is very often involved. The remoteness can be as far as satellite

communications; as a result, the multiple access scheme is changing dynamically, in

which the needs of communication resource from each user had to be beware by a

system controller. In light of it, an overhead is given rise by the extra requirement of

the amount of time for such an information transfer, which in turn constitutes a

constraint on the efficiency of the utilization of communication resource, such that an

upper limit is required to be drawn [21].

Time Division Multiple Access

Time division multiple access is defined as multiplexing the division of time in

accordance with the number of channels set by oneself [22].

Figure 19 Schematic of T1 carrier system.

For example, according to the schematic of T1 carrier system as shown in [22, Fig.19],

time is division multiplexed with reference to 24 channels, while a TDM PAM signal

represents the sampler output. The Signal is firstly taken and then quantized by an



26

encoder which will then encode the signal into eight-bit pulses which is described in

[22, Fig.20] in detail.

Figure 20 Encode signal into 8-bit pulses.

After that, a digital processor will digitize the signal and send it through a channel. In

a bid to refresh these pulses, in every 6,000 feet, there will exist a repeater which can

also decode the pulses at the receiver. Demultiplexing of these samples will then takes

place, and a reconstruction of audio signal will be adopted, in which the samples are

passed via a low-pass filter in each of the 24 channels [22].

With the above equations, this chapter describes the idea used in this project to achieve

a more efficient in Vehicle communication. As the vehicle is a moving object, the data

communication will be mainly affected by distance. Therefore, the first investigation is

to examine the Bit Error Rate against the distance involved in transmit antenna and

receive antenna.

To imitate the road environment in the city, Rayleigh Fading Model and Rician Fading

Model are adapted to reflect the situation. Rayleigh Fading Model is used for



27

considering the non- Line-Of-Sight (LOS) environment which is suitable for urban area

with a lot of buildings and vehicles [23]. Autonomous vehicle network may not be

directly performed if there is a physical obstacle in between the transmitter on the

vehicle and receiver at the nearby roadside. For example, a car in between the

communication device. Therefore, this kind of non-direct connecting communication

can be regarded as under LOS environment. By contrast, Rician Fading Model can

represent and reflect the direct Line-Of-Sight communication which is suitable for rural

environment like the countryside road. [24].

MATLAB is used to simulate the road environment for wireless communication for

different level modulations from a low-order modulation such as 4-QAM to a high-

order modulation like 64-QAM. A graph of Bit Error Rate against Distance in various

modulation cases will be generated for analysis.

As expected in the concept idea, the low-order modulation should perform well in long

distance, but it allows only small bit rate for data transmission. The high-order

modulation performs well only in short distance, but it offers a high bit rate due to its

structure. A study is made to investigate the optimal range for each modulation and

hence design an optimal system for better data transmission.

Base on the idea, this optimal system is devised to maximise the transmission speed in

autonomous vehicle network by adopting a different level of modulation for various

distance. The criteria for switching the optimal modulation will refer to a factor of BER

limit, SNR value and distance between transmit antenna and receive antenna. When the

BER value exceeds the critical BER limit, high-order modulation will be changed to a

low-order modulation for better performance.



28

4 Progress and Simulation Results

The results are simulated by MATLAB, and the source code can be found at the

appendix page in the last chapter.

4.1 BER against SNR for 4-QAM, 16-QAM and 64-QAM

The simulation result of BER against SNR for 4-QAM, 16-QAM and 64-QAM is

shown in Figure 21.

Figure 21 BER against SNR simulation for 4-QAM, 16-QAM

and 64-QAM.

Due to the constellation structure, the low-order modulation 4-QAM performs well in

long distance as the BER is still in low value for small SNR. At the same time, the

high-order modulation 64-QAM performs well only in short distance as the BER

value is relatively high even for large SNR.



29

4.2 BER against SNR for 4-QAM, 16-QAM and 64-QAM with Fading Model

The project aims at simulating the performance of the proposed method by exploiting

different types of modulations corresponding to the distance between the transmitting

end and receiving end in the vehicle communication system. Therefore, using the result

from the previous simulation definitely does not suffice. Instead, it will be much better

to make the simulation more realistic by adopting the Rayleigh Fading Model and

Rician Fading Model. Rayleigh Fading Model and Rician Fading Model are best suit

the road environment, in which Rayleigh Fading Model represents the road

environment in urban area with no dominant Line-Of-Sight(LOS) path between two

antennas, whereas Rician Fading Model demonstrates the road environment in rural

area with dominant Line-Of-Sight(LOS) path.

For urban environment, Rayleigh Fading Model is simulated with KLOS =0, the BER

against SNR for 4-QAM, 16-QAM and 64-QAM is shown in Figure 22.

Figure 22 BER against SNR simulation in Rayleigh fading

for 4-QAM, 16-QAM and 64-QAM.



30

For rural environment, Rician Fading Model is simulated with KLOS dB=3dB [25],

hence KLOS =2. Hence, the simulation result of the BER against SNR for 4-QAM, 16-

QAM and 64-QAM is shown in Figure 23.

Figure 23 BER against SNR simulation in Rician fading for

4-QAM, 16-QAM and 64-QAM.

The result is showing that situation in a vehicle communication system requires a

higher SNR value than the original case to obtain the same level of BER value in both

Rayleigh Fading Model and Rician Fading Model, which is reasonable for a realistic

road environment because of the fading effect around the transmitter and receiver.



31

4.3 Simulation of A Roadside Antenna Environment

Figure 24 Roadside Antenna Environment.

A roadside antenna environment is simulated in MATLAB as illustrated in Figure 24.

In the traditional method, assuming only one modulation type is used for wireless

communication. In the condition of room temperature, To=290K [19]. Antenna gain is

GTX = GRX =1 for nondirectional antennas [14]. Assuming the transmitter has a distance

2 m from the road centre and the signal is 5.925GHz with bandwidth 75 MHz which is

assigned by the US Federal Communications Commission (FCC) [26]. Hence, the

wavelength is 0.05m. The simulation results of the received total bits at the receiver end

for both traditional method, and the proposed method are shown below in the next

section. To ensure the signal is received in the receiver end, BER value should be less

than 10-3 [27].



32

Traditional Method

Figure 25 Using 64-QAM only for wireless communication.

The order of modulation is keeping the same for all distances between transmit antenna

and receive antenna in the traditional method. As illustrated in Figure 25, when using

the traditional method, if a vehicle reaches the transmitting range of a transmitter, only

one QAM type of modulation will be adopted, and it will never change to other types

of modulation anymore. To put it in another way, no matter how far the distance from

the transmitter, which the blue vehicle has travelled through, 64-QAM modulation.

Regardless of the simple circuit that is achieved by harnessing a fixed order of QAM

modulation, such a traditional method has a limitation, such that no one order QAM

modulation can perfectly fit in any situations. For example, although a significant

amount of information can be transmitted through high-order 64-QAM modulation, the

transmitting range is very short. In contrast, low-order 4-QAM modulation can be able

to achieve a very lengthy transmitting range, yet the amount of information transmitted

will be smaller. As a consequence, there is no order QAM modulation but is a double-

edged sword.

64-QAM

64-QAM

64-QAM



33

Proposed Method

Figure 26 Using Proposed Method for wireless

communication.

In light of the aforementioned constraint of the traditional method, the proposed method

applies the advantages of a variety of order QAM modulation into a wide range of

distances between the transmitter end and receiver end. When a vehicle is at the closest

distance with the transmitter, then, 64-QAM modulation will be adopted to transmit the

information. The criteria for switching to an optimal modulation will be based on a

factor of BER limit, such that when the BER value exceeds the critical BER limit, high-

order modulation will be changed to a low-order modulation for better performance.

As shown in Figure 26, when the blue vehicle is very far away from the transmitter, it

is advised to adopt an order of modulation that has a long transmitting range, which in

other words, should be a low-order modulation, say 4-QAM. After that, when the

vehicle moves even closer or the closest to the transmitter at a shorter transmitting range,

then a higher-order modulation, viz. 16-QAM and 64-QAM should be adopted

respectively since it can allow a greater amount of information transmission.

4-QAM

16-QAM

64-QAM



34

4.3.1 Traditional Method – Using one type QAM only for wireless communication

After obtaining the valuable information of BER against SNR in Rayleigh Fading

Model from the above simulation, the information can be used to analysis the number

of total bits received in a vehicle when it passes through a transmitting antenna using

a traditional method as seen in Figure 27.

Figure 27 Using 64-QAM only in Rayleigh Fading.

Serval simulations are performed to demonstrate the performance of the number of bits

received in a traditional method with a different order of QAM modulations in a

Rayleigh Fading Model. That is simulating the road conditions of a city’s urban area

with no dominant Line-Of-Sight(LOS) path between transmitter and receiver. Also,

different vehicle’s speed is adopted to investigate the effect of speed on the transmitting

performance. The result is shown in Figure 28.

64-QAM

64-QAM

64-QAM



35

Figure 28 Total Bits against Speed in Rayleigh Fading,

1 type QAM only.

Besides simulating the road environment in urban area, the simulation in rural area’s

road condition is also considered. The information of BER against SNR in Rician

Fading Model simulation in chapter 3.2 is also used to analyze the number of total bits

received in the same condition but in a rural area environment as seen in Figure 29.

Figure 29 Using 64-QAM only in Rician Fading.

64-QAM

64-QAM

64-QAM



36

Several simulations are performed to demonstrate the traditional method in a Rician

Fading Model, which represent the road conditions of a city’s rural area. The results are

shown in Figure 30.

Figure 30 Total Bit against Speed in Rician Fading,

1 type QAM only.

From the results of simulation in urban area and rural area. It is found that the total

bits received of a vehicle with same speed level are similar no matter which order

type of QAM modulation is adopted in the traditional method. There is no superiority

on using the particular order of QAM modulation to increase the transmitting

performance. As a result, let us investigate the transmitting performance of using the

proposed method.



37

4.3.2 The Proposed Method for wireless communication

In this section, the transmitting performance of the proposed method is investigated.

The information of BER against SNR in Fading Model simulation in chapter 3.2 is

also used for comparison on the analysis the number of total bits received in a vehicle

when it passes through a transmitting antenna. In this time, proposed method is

adopted as seen in Figure 31. The blue vehicle will adopt different order of QAM

modulation according to distance between the transmitting end and recovering end.

Figure 31 Using proposed method in Rayleigh Fading.

Same logic of simulation is performed to investigate the proposed method in the

Rayleigh Fading Model, which simulates the road conditions of a city’s urban area. The

result combing with the previous result of using traditional method in Rayleigh Fading

model is shown in Figure 32, where the black line representing the proposed method.

64-QAM

16-QAM

4-QAM



38

Figure 32 Total Bit against Speed in Rayleigh Fading,

Proposed Method.

The Figure 32 shows that when a vehicle is at a speed of 20 km/hr, the received bits in

proposed method is larger than that at a speed of 80 km/hr. This is because the time of

connecting the transmitting end and receiving end will be decreased with increasing

vehicle’s speed, hence a lower received total bits at a speed of 80 km/hr. Despite this,

it is observed that no matter which speeds the vehicle is at, the proposed method still

outrivals the traditional method, be them 64-QAM, 16-QAM, or 4-QAM modulation.

Recall the previous observation in the result of using traditional method, there is no

superiority on using particular order of QAM modulation to increase the transmitting

performance. However, if the advantages of a variety of order QAM modulation are

applied to a wide range of distances between the transmitter end and receiver end.

There is a significant improvement in terms of total received bits.

It is concluded that proposed method is better than traditional method in urban area’



39

road environment. Now the performance of using proposed method in rural area’s

road condition is also investigated. Therefore, the information of BER against SNR in

Rician Fading Model simulation in chapter 3.2 is used to analyze the number of total

bits received in a rural area environment through proposed method as seen in Figure

33.

Figure 33 Using the proposed method in Rician Fading.

As same as the simulation of the above algorithm in the Rayleigh Fading Model, a

simulation of the proposed method in Rician Fading Model is performed to demonstrate

rural area’s road environment. The result combing with the previous result of using

traditional method in Rician Fading Model is shown in Figure 34, where the black line

is representing the proposed method.

64-QAM

16-QAM

4-QAM



40

Figure 34 Total Bit against Speed in Rician Fading,

Proposed Method.

As seen from the above result, it is noted that the proposed method at all the vehicle’s

speed still outrivals than the traditional method, be them 64-QAM, 16-QAM, or 4-

QAM modulations.

The total received bits, in this case, is higher than previous one in Rayleigh Fading

Mode. This is because rural area’s road environment provides a dominant Line-Of-

Sight (LOS) path, such that the vehicle still keeps connecting with the transmitter at a

longer distance between them.

With both results in Rayleigh Fading Model and Rician Fading Model, it is concluded

that proposed method outperforms the traditional method. In the next section, the

transmitting technique of the vehicle communication system in the proposed method is

modified in accordance with the changes from traditional method into proposed method.



41

4.4 The Proposed Method in multiple car situation

The previous simulation has proved that the proposed method outperforms the

traditional method. Since it is sufficiently reliable to prove the above statement even

when taking account of merely one vehicle existing on a road for the purpose of a simple

illustration during simulation as shown in Figure 35; as for further simulations which

have to be made as similar to the authentic road conditions as possible at the later stages,

a considerable amount of 500 vehicles present themselves on the same road as displayed

in Figure 36 will be taken into consideration during the process simulation. In this case,

multiplexing or multiple access shall be adopted in order to achieve such a hectic traffic

condition.


As there are uncertainty of the amount of vehicles present in a road environment, such

that sometimes traffic can be congested with a sudden presence of plenty of vehicles in

one road, or sometimes very few vehicles are using the same road; thus, based on this

fluctuating variation, multiple access which scheme is usually changing dynamically

has been taken reference to rather than multiplexing.

Car 1

Car 1

Car 1



42


Nonetheless, when opting to utilize the traditional time division multiple access method,

notwithstanding a time division in an equal manner, since the proposed method has

adopted QAM4, QAM16, and QAM64 respectively for maximizing the efficiency of

the communication, as a consequence, this multiple access method is not appropriate to

apply to the proposed method due to the unfair receival of data among the vehicles on

the same road in a specific time frame with reference to each of their distance from the

road-base transmitter, such that the closer the distance between the vehicle and the

transmitter, the greater the amount of information that the vehicle will receive, and the

vice versa.

In light of it, the weakness of the traditional method resulting in an unfairness received

of data among the vehicles will be overcome by a tailor-made method proposed in this

report. In the specifically designed method, time will be allocated according to the

relative proportions of QAM4, QAM16, and QAM64 respectively and the

corresponding number of vehicles under QAM4, QAM16, and QAM64 respectively.

Car 1

Car 2

Car 3

Car 4

Car 5



43

The equation is as follows.

Assumption 1: The time allocated for and number of vehicles under 4-QAM

transmission are A and X respectively


transmission are B and Y respectively


transmission are C and Z respectively

For one second time slot,

A × X + B × Y + C × Z = 1

For equal data received

Log2(4) × A = Log2(16) × B = Log2(64) × C

Therefore,

Time allocated for vehicles under 4-QAM = 2

2X+Y+2

3Z


2X+Y+2

3Z


6X+3Y+2Z



44

Through using the designed method, every vehicle exists on the same road can be able

to receive the same amount of data regardless of the variations in the distance between

the vehicle and the transmitter when compared with the conventional method. The

difference between the designed method and traditional method is clearly shown in

Figure 37.

When there are 500 vehicles passing through the same road in order to receive the data

from the transmitter, yet only the vehicles in the middle of the road can reflect the

conditions of the traffic stream, therefore, merely 45 vehicles situated in the middle will

be taken into account for the simulation, then the data that these 45 vehicles have

received from the transmitter in one second when they are in the transmitting range of

the transmitter are recorded.

It should be noted that the first and last few vehicles out of the 45 vehicle sample do

not receive any data since they are without the transmitting range.

Figure 37 Received Data among 45 cars for a time slot.



45

4.5 The Proposed Method in multiple car situation with many antennas

In all the previous sections, one road-base transmitter is enough for analysis the

performance of the proposed method and design method. However, if it is significant

to take a further step which is to determine the optimal distance for the entire design of

vehicle communication system, one road-base transmitter is not enough.

In this section, a more realistic version of simulation is demonstrated as seen in Figure

38 to figure out the required optimal antenna distance for the entire design of vehicle

communication system. The simulation has achieved a break-through in which it allows

taking into account a great number of vehicles together with many road-base antennas

“at the same time” in Fading Model together with the previous designed method to

allocate the transmitting channel with the variation number of cars at a time.

Figure 38 Multiple Car Situation with Many Antennas,

Urban Environment.



46

The result of the simulation for different vehicular speeds with required data is shown

below in Figure 39 with an assumption of 10 MB received data in 1000m long vehicle

travel distance in Rayleigh Fading Model or just say in urban area.

Figure 39 Optimal Antenna Distance in Rayleigh Fading.

It is observed that the optimal antenna distance decreases with increasing vehicular

speed. When it comes to zero, that means the entire vehicle commutation system cannot

provide the required data at such vehicular speed. As a result, if drivers want to receive

such an amount of data, they have to slow down the vehicular speed.

Despite the fact that the system cannot meet the requirement at 70km/hr or above, it is

noted that in general vehicular speed is not as high as 70 km/hr in urban area excluding

highway. Therefore, it is still appropriate to adopt the designed system in reality.



47

The same applies to Rician Fading Model or rural areas as seen in Figure 40.

Figure 40 Multiple Car Situation with Many Antennas,

Rural Environment.

The result in Figure 41 shows that the design system is appropriate in reality for rural

case including highway with high vehicular speed.

Figure 41 Optimal Antenna Distance in Rician Fading



48

5 Conclusion and Future Development

5.1 Conclusion

From above simulation results, it is concluded that the proposed method is superior to

traditional methods in both Rayleigh Fading Model and Rician Fading Model. Under

the same transmit power, there is a significant improvement in total bits at the receiver

end for varying speeds, which means moving vehicles can obtain more information

from the network through the proposed method.

After the recognition of the performance of the proposed method, it is then put into

practice by adopting adjustments for the purpose of designing a well-rounded vehicle

communication system. The adjustment is allocating the transmitting channel through

a new time-division multiple access method such that every vehicle exists on the same

road can be able to receive the same amount of data regardless of the variations in the

distance between the vehicle and the transmitter when compared with the conventional

method.

Last but not least, the result of simulation of applying the design vehicle communication

system in a more realistic road environment reflects that the design system is

appropriate to be utilized in reality. The optimal antenna distance obtained in the

simulation provided the best indication for placing the antenna, which is conducive to

reducing the social cost of and time required for numerous trials of various distances in

practice.



49

5.2 Limitation

The KLOS factor varies with the road environment, while it exerts a significant impact

on the path channel loss, thus affecting the design of the system. Since there exists a

large variation in the KLOS factor corresponding to different road conditions, only two

assumptions of KLOS factor are adopted in this report for simulation purpose. It is

recommended that if this system is to be applied in the real situation, independent

measurement of KLOS factor for each road should be conducted.

5.3 Future Development

As the simulation reflected that the proposed method is definitely feasible, it provides

a basis for a future experiment taken account of factors and variation aplenty existed in

an authentic environment with the aid of raw Wi-Fi transmitters and receivers, so that

the proposed method can be further examined whether it will be viable in practice.



50

6 References

[1] M. Sivak and B. Schoettle, “Road safety with self-driving vehicles: General

limitations and road sharing with conventional vehicles,” pp. 1–13, Jan. 2015.

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Hall, 1997.

[3] B. Hassibi and B. Hochwald, “How much training is needed in multiple-

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[4] V. Tarokh, N. Seshadri, and A. Calderbank, “Space-time codes for high data

rate wireless communication: performance criterion and code construction,”

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[5] E. Biglieri, “High-Level Modulation and Coding for Nonlinear Satellite

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[6] J. Christopher L, “Method of adding encryption/encoding element to the

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[7] S. Claude Elwood, “Communication in the presence of noise,” Proceedings of

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[8] Chapter 5 of S. Haykin and M. Moher, Communication systems. New York:

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[10] Chapter 7,8 of S. Haykin and M. Moher, Communication systems. New York:


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[15] Chapter 4 of A. Aragon-Zavala, Indoor wireless communications: from theory

to implementation. Hoboken, NJ: John Wiley & Sons, Inc., 2017.



51

[16] K. Chetcuti, C. J. Debono, and S. Bruillot, “The effect of human shadowing on

RF signal strengths of IEEE802.11a systems on board business jets,” 2010 IEEE

Aerospace Conference, 2010.

[17] Chapter 2 of J. J. Carr and G. W. Hippisley, Practical antenna handbook. New

York: McGraw-Hill, 2012.

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Performance Limit of BCH and RS Codes Using Multipath Diversity,”

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[19] Chapter 20 of A. F. Molisch, Wireless communications. Chichester: John Wiley

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[20] Chapter 8 of A. Weintrit, Navigational problems: marine navigation and safety

of sea transportation. Leiden: CRC Press/Balkema, 2013.

[21] Chapter 11of B. Sklar, Digital communications: fundamentals and applications.

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[22] Chapter 6 of B. P. Lathi and Z. Ding, Modern digital and analog communication

systems. New York: Oxford Univ Press, 2010.

[23] M. Hasna and M.-S. Alouini, “End-to-end performance of transmission systems

with relays over rayleigh-fading channels,” IEEE Transactions on Wireless

Communications, vol. 2, no. 6, pp. 1126–1131, 2003.

[24] Y.-D. Yao and A. Sheikh, “Outage probability analysis for microcell mobile

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[27] “ALLOWABLE ERROR PERFORMANCE FOR A HYPOTHETICAL

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S!!PDF-E.pdf.



52

7 Appendix

7.1.1 Generation of BER against SNR for 4-QAM, 16-QAM, 64-QAM

function [y ] = QAM(Order)

SNR_dB=0:40;

NumberSNR=length(SNR_dB);

BER=zeros(1,NumberSNR);

N=10^6; % No. of Sample

Scale=2*(Order-1)/3;

PositionFactor=sqrt(1/Scale);

Qam4=[-1,1];

Qam16=[-3,-1,1,3];

Qam64=[-7,-5,-3,-1,1,3,5,7];

if Order==4

Alphabet=Qam4;

Upperlimit=1*PositionFactor;

Lowerlimit=-1*PositionFactor;

a='mx-';

elseif Order==16

Alphabet=Qam16;



a='bx-';

elseif Order==64

Alphabet=Qam64;



a='gx-';

end

Signal=PositionFactor*(randsrc(1,N,Alphabet)+1i*randsrc(1,N,Alphabet));

SignalRealPart=real(Signal);

SignalImagPart=imag(Signal);

NoiseSource=randn(1,N)+1i*randn(1,N);



53

for i=1:NumberSNR

SNR=10^(SNR_dB(i)/10); % SNR_dB -> SNR

Sigma=sqrt(1/(2*SNR)); % Sigma for Complex Case

Noise=Sigma*NoiseSource; % Additive White Noise

y=Signal+Noise;

yRealPart=real(y);

yImagPart=imag(y);

DistanceReal=abs(SignalRealPart-yRealPart);

DistanceImag=abs(SignalImagPart-yImagPart);

count=0;

for n=1:N

if yRealPart(1,n)>Upperlimit || yRealPart (1,n)<Lowerlimit

if yImagPart(1,n)>Upperlimit ||yImagPart(1,n)<Lowerlimit

else

ImagExistError=DistanceImag(1,n)==PositionFactor ||

DistanceImag(1,n)>PositionFactor;

if ImagExistError==1

count=count+1;

else

RealExistError=DistanceReal(1,n)==PositionFactor ||

DistanceReal(1,n)>PositionFactor;

if RealExistError==1

count=count+1;

end

end

end

end

end

NoError=count;

BER(1,i)=NoError/N;

end

semilogy(SNR_dB,BER,a)

xlabel('SNR (dB) ');

ylabel('BER');

title('BER against SNR simulation for 4-QAM, 16-QAM and 64-QAM');



54

if Order==4

legend('4-QAM');

elseif Order==16

legend('16-QAM');

elseif Order==64

legend('64-QAM');

end

hold on

end

7.1.2 Plot BER against SNR for 4-QAM, 16-QAM, 64-QAM

function [] = Plot(Number)

%UNTITLED Summary of this function goes here

% Detailed explanation goes here

if Number==1

QAM(4);

legend('4-QAM');

elseif Number==2

QAM(4);

QAM(16);

legend('4-QAM','16-QAM');

elseif Number==3

QAM(4);

QAM(16);

QAM(64);

legend('4-QAM','16-QAM','64-QAM');

end

end



55

7.2.1 Generation of BER against SNR for 4-QAM, 16-QAM, 64-QAM (Fading

Model)

function [y ] = QAM_Fading(Order,K)

SNR_dB=0:70;

NumberSNR=length(SNR_dB);

BER=zeros(1,NumberSNR);




Qam4=[-1,1];

Qam16=[-3,-1,1,3];

Qam64=[-7,-5,-3,-1,1,3,5,7];

if Order==4

Alphabet=Qam4;



a='mx-';

elseif Order==16

Alphabet=Qam16;



a='bx-';

elseif Order==64

Alphabet=Qam64;



a='gx-';

end




if K==0



56

RayleighFading=1/sqrt(2)*(randn(1,N)+1i*randn(1,N));

Fading=RayleighFading;

else

RicianFading=sqrt(K/(K+1))+sqrt(1/(K+1))*1/sqrt(2)*(randn(1,N)+1i*randn(1,N));

Fading=RicianFading;

end


for i=1:NumberSNR

SNR=10^(SNR_dB(i)/10); % SNR_dB -> SNR



% Received Signal

y=Fading.*Signal+Noise;

%Equalization: Remove Fading Effects

y=y./Fading;

%Demodulation

yRealPart=real(y);

yImagPart=imag(y);



count=0;

for n=1:N



else




count=count+1;

else






57

count=count+1;

end

end

end

end

end

NoError=count;

BER(1,i)=NoError/N;

end

semilogy(SNR_dB,BER,a)

xlabel('SNR (dB) ');

ylabel('BER');

if K==0

title('BER against SNR simulation in Rayleigh fading for 4-QAM, 16-QAM and

64-QAM');

else

title('BER against SNR simulation in Rician fading for 4-QAM, 16-QAM and 64-

QAM');

end

if Order==4

legend('4-QAM');

elseif Order==16

legend('16-QAM');

elseif Order==64

legend('64-QAM');

end

hold on

end



58

7.2.2 Plot BER against SNR for 4-QAM, 16-QAM, 64-QAM ( Fading Model )

function [] = Plot_Fading(Number,K)

if Number==1

QAM_Fading(4,K);

legend('4-QAM');

elseif Number==2

QAM_Fading(4,K);

QAM_Fading(16,K);

legend('4-QAM','16-QAM');

elseif Number==3

QAM_Fading(4,K);

QAM_Fading(16,K);

QAM_Fading(64,K);


end

end

7.3.1 Distance Function

function distance = distance(kmPerHour,time)

NormalDistance=2;

mPerS=kmPerHour*1000/3600;

TravelDistance=mPerS*time;

distance=sqrt(TravelDistance^2+NormalDistance^2);

end

7.3.2 SNR dB Function

function SNRdB = SNRdB(distance)

Wavelength=0.05;

Bandwidth=75*10^6;

Ptx=1; %

T=290; %for room temperatere

k=1.38*10^(-23); %Boltzman Constant

Gtx=1; % Transmiter antenna gain for isotropic

Grx=1; % Reciever antenna gain for isotropic



59

SNR=(Ptx*Gtx*Grx*Wavelength^2)/(k*T*Bandwidth*(4*pi*distance)^2);

SNRdB=10*log10(SNR);

end

7.3.3 BER Function

function BER = QAM_BER(Order,K,SNR_dB)




Qam4=[-1,1];

Qam16=[-3,-1,1,3];

Qam64=[-7,-5,-3,-1,1,3,5,7];

if Order==4

Alphabet=Qam4;



elseif Order==16

Alphabet=Qam16;



elseif Order==64

Alphabet=Qam64;



end




if K==0

RayleighFading=1/sqrt(2)*(randn(1,N)+1i*randn(1,N));



60

Fading=RayleighFading;

else

RicianFading=sqrt(K/(K+1))+sqrt(1/(K+1))*1/sqrt(2)*(randn(1,N)+1i*randn(1,N));

Fading=RicianFading;

end


SNR=10^(SNR_dB/10); % SNR_dB -> SNR



% Received Signal

y=Fading.*Signal+Noise;

%Equalization: Remove Fading Effects

y=y./Fading;

%Demodulation

yRealPart=real(y);

yImagPart=imag(y);



count=0;

for n=1:N



else




count=count+1;

else




count=count+1;

end



61

end

end

end

end

NoError=count;

BER=NoError/N;

end

7.3.4 QAM Order Function

function Order = QAM_Order_ForGivenSNR(K,SNR_dB,Method)

BerLimit=10^-3;

if Method==0

BER64=QAM_BER(64,K,SNR_dB);

if BER64<BerLimit || BER64==BerLimit

Order=64;

else



Order=16;

else



Order=4;

else

Order=0;

end

end

end

elseif Method==4



Order=4;

else

Order=0;

end



62

elseif Method==16



Order=16;

else

Order=0;

end

elseif Method==64



Order=64;

else

Order=0;

end

end

7.3.5 Bits Rate Function

function BitRate = Bit_Rate(Order)

if Order==0

BitRate=0;

else

Bandwidth=75/2*10^6;

BitRate=log2(Order)*Bandwidth/10^9;

end

7.3.6 Total Bits Function

function TotalBit = Total_Bit(kmPerHour,K,Method)

Bit=0;

for time=1:120

Distance= distance(kmPerHour,time);

SNR_dB = SNRdB(Distance);

Order = QAM_Order_ForGivenSNR(K,SNR_dB,Method);

if Order==0

break



63

else

count = Bit_Rate(Order);

Bit=Bit+count;

end

end

TotalBit=Bit*2;

End

7.3.7 Plot Total Bits against Speed Function

function [] = Plot_TotalBit_Against_Speed(K,Method)

a=zeros(1,7);

b=20:10:80;

for kmPerHour=20:10:80

TotalBit = Total_Bit(kmPerHour,K,Method);

a(kmPerHour/10-1)=TotalBit;

if Method==0

if K==0

title('Total Bits against Speed in Rayleigh Fading');

else

title('Total Bits against Speed in Rician Fading');

end

elseif Method==4

if K==0

title('Total Bits against Speed in Rayleigh Fading, 4-QAM only');

else

title('Total Bits against Speed in Rician Fading, 4-QAM only');

end

elseif Method==16

if K==0


else

title('Total Bits agaisnt Speed in Rician Fading, 16-QAM only');

end

elseif Method==64

if K==0



64


else

title('Total Bits against Speed in Rician Fading, 64-QAM only');

end

end

end

if Method==0

plot(b,a,'-kx');

elseif Method==4

plot(b,a,'-mx');

elseif Method==16

plot(b,a,'-bx');

elseif Method==64

plot(b,a,'-gx');

end

axis([15,85,1.5,25]);

xlabel('Speed (km/h)');

ylabel('Total Bits (Gbits)')

set(gca,'fontsize',15)

hold on

end

7.3.8 Plot ALL Type Total Bits against Speed Function

function [] = Plot_ALL_TotalBit_Against_Speed(K,Number)

if Number==3

Plot_TotalBit_Against_Speed(K,4)



if K==0

title('Total Bits against Speed in Rayleigh Fading for 4-QAM, 16-QAM, 64-

QAM');

else

title('Total Bits against Speed in Rician Fading for 4-QAM, 16-QAM, 64-

QAM');

end




65

elseif Number==4





if K==0

title('Total Bits against Speed in Rayleigh Fading for Proposed Method');

else

title('Total Bits against Speed in Rician Fading for Proposed Method');

end

legend('Proposed Method','4-QAM','16-QAM','64-QAM');

end

end

7.4.1 Create Car Start Point Function

function CarStartPoint = Create_CarStartPoint(CarNumber,CarBetweenDistance)

StartPoint=-2000;

CarStartPoint=zeros(1,CarNumber);

CarVariations=zeros(1,CarNumber);

for n=1:CarNumber

while CarVariations(1,n) <5

CarVariations(1,n)=randn(1)*CarBetweenDistance;

if CarVariations(1,n)==0

CarVariations(1,n)=CarBetweenDistance;

end

end

end

for n=1:CarNumber

CarStartPoint(1,n)=StartPoint-CarVariations(1,n);

StartPoint=CarStartPoint(1,n);

end

end

7.4.2 Distance km/hr to m/s unit Function

function distance = distance_kmTOmUnit(kmPerHour,time)

mPerS=kmPerHour*1000/3600;



66

distance=mPerS*time;

end

7.4.2 Check Car QAM Order Function

function CarQAM =

Advanced_Car_QAM(Location,QAM4Distance,QAM16Distance,QAM64Distance)

CarQAM=zeros(1,3);

QAM4CAR=0;

QAM16CAR=0;

QAM64CAR=0;

if (Location>-QAM4Distance || Location==-QAM4Distance) && Location<-

QAM16Distance

QAM4CAR=1;

elseif (Location>-QAM16Distance || Location==-QAM16Distance) && Location<-

QAM64Distance

QAM16CAR=1;

elseif (Location>-QAM64Distance || Location==-QAM64Distance) &&

(Location<QAM64Distance || Location==QAM64Distance)

QAM64CAR=1;

elseif Location>QAM64Distance && (Location<QAM16Distance ||

Location==QAM16Distance)

QAM16CAR=1;

elseif Location>QAM16Distance && (Location<QAM4Distance ||

Location==QAM4Distance)

QAM4CAR=1;

end

CarQAM(1,1)=QAM4CAR;





67

7.4.3 Traditional Time Division Method Function

function BitCountPerCar =

Traditional_Time(CarInsideRange4QAM,CarInsideRange16QAM,CarInsideRange64

QAM)

BitCountPerCar=zeros(1,3);

CarInsideRange=CarInsideRange4QAM + CarInsideRange16QAM +

CarInsideRange64QAM;

AllocatedTime=1/CarInsideRange;

if CarInsideRange4QAM==0

BitQAM4countPerCar =0;

else

BitQAM4countPerCar = Bit_Rate(4)*AllocatedTime;

end



else


end



else


end

BitCountPerCar(1,1)= BitQAM4countPerCar;



7.4.4 Designed Time Division Method Function

function BitCountPerCar =

Designed_Time(CarInsideRange4QAM,CarInsideRange16QAM,CarInsideRange64Q

AM)

BitCountPerCar=zeros(1,3);



68



else

QAM4AllocatedTime = 2/(2*CarInsideRange4QAM + CarInsideRange16QAM +

2/3*CarInsideRange64QAM);

BitQAM4countPerCar = Bit_Rate(4)*QAM4AllocatedTime;

end



else

QAM16AllocatedTime = 1/(2*CarInsideRange4QAM + CarInsideRange16QAM +

2/3*CarInsideRange64QAM);


end



else

QAM64AllocatedTime = 2/(6*CarInsideRange4QAM + 3*CarInsideRange16QAM

+ 2*CarInsideRange64QAM);


end




7.4.5 Plot Traditional against Designed Time Division Method Function

function []= Plot_Traditonal_To_Design()

Data=load('UrbanArea.mat');

QAMAntennaDistance=Data. DifferetQAMAntennaDistance;

QAM4Distance=QAMAntennaDistance(1,1);



TimeSet=1000;



69

CarNumber=200;

CarBetweenDistance=20;

kmPerHour=20;

CarStartPoint = Create_CarStartPoint(CarNumber,CarBetweenDistance);

Location=CarStartPoint;

TimePeriod=1;

Distance = distance_kmTOmUnit(kmPerHour,TimePeriod);

Data=zeros(TimeSet,CarNumber);

for time=1:TimeSet

NumberOfCarForQAMPerTimeSlot=zeros(1,3);

for n=1:CarNumber

Location(1,n)=Location(1,n)+Distance;

CarCount=Advanced_Car_QAM(Location(1,n),QAM4Distance,QAM16Dista

nce,QAM64Distance);

if CarCount(1,1)>0

Data(time,n)=4;

elseif CarCount(1,2)>0

Data(time,n)=16;


Data(time,n)=64;

end

NumberOfCarForQAMPerTimeSlot= NumberOfCarForQAMPerTimeSlot +

CarCount;

end

CarInsideRange4QAMPerTimeSlot =NumberOfCarForQAMPerTimeSlot(1,1);



BitCountPerCar =

Designed_Time(CarInsideRange4QAMPerTimeSlot,CarInsideRange16QAMPerTime

Slot,CarInsideRange64QAMPerTimeSlot);

for n=1:CarNumber

if Data(time,n)==4

Data(time,n)=BitCountPerCar(1,1);

elseif Data(time,n)==16





70


end

end

end

% Measure 45 Cars for Comparing

NthCar=zeros(45,2);

for time=1:TimeSet

if Data(time,100)>0

for n=1:45

Car=60+n;

NthCar(n,1)=Data(time,Car)*1024^2/8;

end

break

end

end

title('KiloBytes against Car in Design VS Traditional System, 45 Cars');

xlabel('Nth Car');

ylabel('KaloByes(KB/s)')


hold on


TimePeriod=1;



for time=1:TimeSet


for n=1:CarNumber


CarCount=Advanced_Car_QAM(Location(1,n),QAM4Distance,QAM16Dista

nce,QAM64Distance);

if CarCount(1,1)>0



71

Data(time,n)=4;


Data(time,n)=16;


Data(time,n)=64;

end


CarCount;

end




BitCountPerCar =

Traditional_Time(CarInsideRange4QAMPerTimeSlot,CarInsideRange16QAMPerTim

eSlot,CarInsideRange64QAMPerTimeSlot);

for n=1:CarNumber

if Data(time,n)==4






end

end

end

for time=1:TimeSet

if Data(time,100)>0

for n=1:45

Car=60+n;

NthCar(n,2)=Data(time,Car)*1024^2/8;

end

break

end

end

bar(NthCar);



72

%axis([60,110,0,0.008]);

xlabel('Nth Car');

ylabel('KiloBytes(KB/s)')


legend('Designed Method','Traditional Method');

hold on

7.5.1 Advanced Car QAM Order with Many Antennas Function

function CarQam =

Advanced_CarQAM_WithManyAntenna(Location,AntennaDistance,QAM4Distance,

QAM16Distance,QAM64Distance)

CarQam=zeros(1,3);

NumberOfAntenna=10;

LastAntennaLocation=AntennaDistance*NumberOfAntenna;

QAM4CAR=0;

QAM16CAR=0;

QAM64CAR=0;

Run=1;

if (Location>-QAM4Distance || Location==-QAM4Distance) && Location<-

QAM16Distance

QAM4CAR=1;

elseif (Location>-QAM16Distance || Location==-QAM16Distance) && Location<-

QAM64Distance

QAM16CAR=1;

elseif (Location>-QAM64Distance || Location==-QAM64Distance) && Location<0

QAM4CAR=1;

end

if (Location==0|| Location>0) && (Location<AntennaDistance/2 ||

Location==AntennaDistance/2)

if (AntennaDistance/2 >0 ) && (AntennaDistance/2 <QAM64Distance ||

AntennaDistance/2==QAM64Distance )

if (Location==0 || Location >0 ) && (Location <AntennaDistance/2 ||

Location==AntennaDistance/2 )

QAM64CAR=1;

end



73

elseif AntennaDistance/2>QAM64Distance && (AntennaDistance/2

<QAM16Distance || AntennaDistance/2==QAM16Distance )

if (Location==0 || Location >0 ) && (Location <QAM64Distance ||

Location==QAM64Distance )

QAM64CAR=1;

elseif (Location >QAM64Distance ) && (Location <AntennaDistance/2 ||


QAM16CAR=1;

end

elseif AntennaDistance/2>QAM16Distance

if (Location==0 || Location >0 ) && (Location <QAM64Distance ||


QAM64CAR=1;

elseif (Location >QAM64Distance ) && (Location <QAM16Distance ||


QAM16CAR=1;

elseif (Location >QAM16Distance ) && (Location <AntennaDistance/2 ||


QAM4CAR=1;

end

end

end

if Location>AntennaDistance/2 && (Location<LastAntennaLocation ||

Location==LastAntennaLocation)

while Run==1

Difference=abs(Location-AntennaDistance);

if Difference<AntennaDistance/2 || Difference==AntennaDistance/2

if (AntennaDistance/2 >0 ) && (AntennaDistance/2 <QAM64Distance ||

AntennaDistance/2==QAM64Distance )

if (Difference==0 || Difference >0 ) && (Difference <AntennaDistance/2 ||

Difference==AntennaDistance/2 )

QAM64CAR=1;

end

elseif AntennaDistance/2>QAM64Distance && (AntennaDistance/2

<QAM16Distance || AntennaDistance/2==QAM16Distance )

if (Difference==0 || Location >0 ) && (Difference <QAM64Distance ||

Difference==QAM64Distance )



74

QAM64CAR=1;

elseif (Difference >QAM64Distance ) && (Difference <AntennaDistance/2

|| Difference==AntennaDistance/2 )

QAM16CAR=1;

end

elseif AntennaDistance/2>QAM16Distance

if (Difference==0 || Difference >0 ) && (Difference <QAM64Distance ||


QAM64CAR=1;

elseif (Difference >QAM64Distance ) && (Difference <QAM16Distance ||


QAM16CAR=1;

elseif (Difference >QAM16Distance ) && (Difference <AntennaDistance/2

|| Difference==AntennaDistance/2 )

QAM4CAR=1;

end

end

Run=0;

else

Location=Location-AntennaDistance;

Run=1;

end

end

end

if Location> LastAntennaLocation &&

(Location<(LastAntennaLocation+QAM64Distance) ||

Location==(LastAntennaLocation+QAM64Distance))

QAM64CAR=1;

elseif Location>(LastAntennaLocation+QAM64Distance) &&



QAM16CAR=1;

elseif Location>(LastAntennaLocation+QAM16Distance) &&





75

QAM4CAR=1;

end

CarQam(1,1)=QAM4CAR;



7.5.2 Travel with Many Antennas Function

function AverageBitPerCar=

Travel_WithManyAntenna(CarNumber,CarBetweenDistance,kmPerHour,

AntennaDistance,QAM4Distance,QAM16Distance,QAM64Distance,TimeMethod)

CarNumber=CarNumber+100;

TimeSet=5000;

CarStartPoint = Create_CarStartPoint(CarNumber,CarBetweenDistance);


TimePeriod=1;



DataMeasureRange=zeros(TimeSet,CarNumber);

DataBit=zeros(1,CarNumber);

TotalBit=0;

for time=1:TimeSet


for n=1:CarNumber


if (Location(1,n)==0 || Location(1,n)>0 ) && (Location(1,n)<1000 ||

Location(1,n)==1000)

DataMeasureRange(time,n)=1;

end

CarCount=Advanced_CarQAM_WithManyAntenna(Location(1,n),AntennaDi

stance,QAM4Distance,QAM16Distance,QAM64Distance);

if CarCount(1,1)>0

Data(time,n)=4;


Data(time,n)=16;



76


Data(time,n)=64;

end


CarCount;

end




if TimeMethod==1

BitCountPerCar =

Traditional_Time(CarInsideRange4QAMPerTimeSlot,CarInsideRange16QAMPerTim

eSlot,CarInsideRange64QAMPerTimeSlot);

for n=1:CarNumber

if Data(time,n)==4






end

end

elseif TimeMethod==0

BitCountPerCar =

Designed_Time(CarInsideRange4QAMPerTimeSlot,CarInsideRange16QAMPerTime

Slot,CarInsideRange64QAMPerTimeSlot);

for n=1:CarNumber

if Data(time,n)==4






end

end

end

end

for n=1:CarNumber



77

for time=1:TimeSet

if DataMeasureRange(time,n)==1

DataMeasureRange(time,n)=Data(time,n);

end

end

end

for n=1:CarNumber

for time=1:TimeSet

DataBit(1,n)=DataBit(1,n)+DataMeasureRange(time,n);

end

end

StartCar=50;

EndCar=CarNumber-50;

for n=StartCar:EndCar

TotalBit=TotalBit+DataBit(1,n);

end

AverageBitPerCar=TotalBit/CarNumber*1024/8; %In MegaBytes Unit

7.5.3 Optimal Antenna Distance Function

function OptimalAntennaDistance =

Advanced_Optimal_AntennaDistance(CarBetweenDistance,kmPerHour,

OrignialAntennaDistance,TimeMethod,RequiredBit,K)

if K==0


elseif K==2

Data=load('RuralArea.mat');

end





CarNumber=100;

AntennaDistance=OrignialAntennaDistance;

Run=1;

while Run==1

AverageBitPerCar=

Travel_WithManyAntenna(CarNumber,CarBetweenDistance,kmPerHour,

AntennaDistance,QAM4Distance,QAM16Distance,QAM64Distance,TimeMethod);



78

if AverageBitPerCar>RequiredBit || AverageBitPerCar==RequiredBit

OptimalAntennaDistance=AntennaDistance;

Run=0;

else

if AntennaDistance>10

AntennaDistance=AntennaDistance-10;

Run=1;

else

OptimalAntennaDistance=0;

Run=0;

end

end

end

7.5.4 Plot Optimal Antenna Distance against Speed Function

function DataAntenna=

Plot_Advanced_OptimalAntennaDistance_Against_Speed(RequiredMegaBytes,K)

DataAntenna=zeros(2,7);

if K==0



elseif K==2

Data=load('RuralArea.mat');


end



OrignialAntennaDistance=QAM4Distance*2;

TimeMethod=0;

b=20:10:80;

OptimalAntennaDistance=zeros(1,7);

for kmPerHour=20:10:80

OptimalAntennaDistance(1,kmPerHour/10-1) =

Advanced_Optimal_AntennaDistance(CarBetweenDistance,kmPerHour,

OrignialAntennaDistance,TimeMethod,RequiredMegaBytes,K);

OrignialAntennaDistance=OptimalAntennaDistance(1,kmPerHour/10-1);

end



79

plot(b,OptimalAntennaDistance,'-bx');

title(['OptimalAntenna Distance against Speed in Design System, Require Data =

',num2str(RequiredMegaBytes),'MB in 1000m distance']);

axis([15,85,0,1000]);

xlabel('Speed (km/h)');

ylabel('Optimal Antenna Distance (m)')


hold on

x=10;

count=0;

for n=1:7

speed=20+count;

DataAntenna(1,n)=speed;

DataAntenna(2,n)=OptimalAntennaDistance(1,n);

count=count+x;

end

Documents

Communication Techniques for Autonomous(self-driving