Review Of a Book Entitled Computer Know

KNOW YOUR COMPUTER

BEFORE you use the computer, you should know first

advance what is called the computer. The introduction of this important

so that we can use the computer with the best possible way

highly efficient and effective.

Computer Systems

Generally, what people call a "computer" is a set of

tool consisting of CPU, monitor, and keyboard. There are also

which states that the CPU was the one who called the computer, while

Other tools are complementary, with its own name

such as monitors, keyboards, mouse, printer and others.

Actually, the notion of computers are more accurately called as

computer system. Computer system consists of three devices (devices),

namely:

􀂃 hardware (hardware),

􀂃 software (software), and

􀂃 Man (brainware).

The tools mentioned above is an example of the hardware.

Computer systems can not run only with one

device, but requires all three.

Chapter 1

Hardware

Computer hardware that we know can be classified into three

group:

􀂃 input devices (input devices);

􀂃 Devices processor (processing unit);

􀂃 The device output (output device).

Input device is equipment that we use to enter

data to a computer, such as keyboard, mouse, and scanner.

Processing devices are devices that perform data processing

into information, or who becomes "brains" of computers in the machine

execute the command given. Processing devices are often

called a CPU (central processing unit / central processing unit) or called

also the processor.

Output devices are devices used to display

information processed by computers, such as monitors, printers, and speakers.

From the above it can be concluded that the often mentioned

CPU is actually a part of computer processing

data (processor), a chip, and is known by the name

brands such as Intel Pentium, Intel Celeron, AMD Athlon, and so forth.

Figure 1. Computer hardware

The "body" (casing) computer which is called the CPU not only

contains a processor, but also contains the mainboard (or

also called mother-board), diskette drives, CD drives, hard drives, memory

(RAM), VGA cards, sound cards, LAN cards (also called Ethernet

cards), modems, and so forth.

to be continue.....

Workshop Wifi Alternatif 2

Workshop Wifi Alternatif

to be continue......

The Mobile Radio Propagation Channel Over Part

In general, it is imperative to understand the target operating environment in order

to optimize the transmitter and receiver design of the communication systems. There

is no such thing as a universal communication systems that is effective for all communication

environments. For examples, both wireless LAN systems and 3G networks

are designed to offer high bit rate. For IEEE 802.11g system, the highest bit rate

is 54Mbps. For UMTS systems (Re1 99), the highest bit rate is just 2Mbps. Obviously,

the cost of the UMTS system is much higher than the wireless LAN systems.

Hence, one might query about why we still require UMTS system when wireless

LAN systems cost only less than US$lOO. Figure 1.8 illustrates a more comprehensive

comparison between Wi-Fi and UMTS systems. We do not just look at the

maximum bit rate offered by both systems but we also have to compare the target

environment of operations. For instance, while Wi-Fi systems offer higher bit rate,

they are designed to operate at pedestrian mobility and indoor coverage. For 3G/4G

cellular systems, although their maximum bit rate is lower than Wi-Fi systems, they

are designed to operate at very high mobility and macroscopic coverage.

Figure 1.8 A comparison between WiFi systems and 3G cellular systems.

1.6 SUMMARY

In this chapter, we have elaborated on the modeling of wireless channels. We have

introduced the 3 levels of channel models, namely the large scale path loss model, the

medium scale shadowing model as well as the microscopic scale fading model. The

path loss model deals with the variation of received signal strength with respect to

variation of distance between the transmitter and the receiver and is focused on the time

scale of seconds. The path loss between a transmitter and a receiver is characterized

by a path loss exponent. In free space or line of sight propagation condition, the path

loss exponent is 2 meaning that the signal power will be reduced by 4 times for every

double in the distance separation. In non line-of-sight propagation environment, the

path loss exponent can reach 4 or above. The larger the path loss exponent, the faster

the signal attenuates as it propagates.

Shadowing model deals with medium scale variation of received signal strength

when the distance is fixed. This is contributed by the variation in the terrain profile

such as obstacles, hills and buildings. Due to law of large number, the shadowing

effects (received signal strength variations) can be modeled by log-normal shadowing

component and parameterized by the standard derivation a(dB).

Finally, microscopic fading deals with small scale variation of received signal

strength due to constructive and destructive multipath superpositions. The time scale

of interests can be of millisecond order. Microscopic fading can be parameterized by

the delay spread (coherence bandwidth) for the multipath dimension as well as the

Doppler spread (coherence time) for the time variation dimension. Note that the multipath

dimension and the time variation dimension are two independent dimensions.

The number of resolvable multipaths is given by L, = [Wtl/Bc]W. hen there is only

one resolvable multipath (Wt, < B,), the signal experiences flat fading channels.

Otherwise, the signal experiences frequency selective fading channels. Similarly,

when T, > T,, the signal experiences fast fading channels. Otherwise, the signal

experiences slow fading channels. Figure 1.9 summarizes the concept of frequency

flat fading, frequency selective fading, fast fading and slow fading channels.

PROBLEMS

1.1 A wideband signal is more difficult to transmit than a narrowband signal

because of the inter-symbol interference problem faced by the wideband signal.

[ TrueFalse]

1.2 In flat fading channels, there is no multipath effect at all as there is only a single

resolvable echo at the receiver. [TruelFalse].

Figure 1.9 Summary of classifications of fading channels.

The End

The Mobile Radio Propagation Channel Part 6

.

1.4.2 Fast Fading vs Slow Fading

Similarly, based on the Doppler spread and the transmitted bandwidth (or equivalently

the coherence time and the symbol / frame duration), the fading channel is classified

as fusr fading or slow fading. A fading channel is classified as f i s t fading if the symbol

duration T, is larger than the coherence time T,. Otherwise, the fading channel is

called slow fading. Note that whether a transmitted signal experiences fast fading

or slow fading depends on both the channel parameters (such as the Doppler spread

or coherence time) and the transmitted signal parameters (such as the transmitted

bandwidth or the symbol duration). Hence, for slow fading channels, the channel

fading coefficients hi(t) remains to be quasi-static within a symbol duration T,.

Note that sometimes, we define slow fading to be the case when T, (coherence

time) is larger than Tf (frame duration) instead of symbol duration.

1.5 PRACTICAL CONSIDERATIONS

In this section, we briefly discuss the implications of path loss, shadowing and microscopic

fading components on system design. In fact, the design and performance of

communication systems depends heavily on the underlying channels. For instance,

path loss exponent determines how fast the received signal strength attenuates with

respect to distance separation between the transmitter and receiver. For a point to

point digital link, higher path loss exponents results in faster signal attenuation and

therefore is undesirable. This is because for the same transmitter and receiver design,

a higher path loss exponent results in shorter communication range (for the same

transmit power) or higher required transmit power (for the same distance) to overcome

the higher path loss. This holds in general for noise-limited systems. However, if we

consider multicell systems such as cellular networks like GSM, or CDMA systems,

higher path loss exponent results in more confined interference (as the interference

signal cannot propagate very far) and this translates into higher system capacity because

more aggressive resource reuse can be realized. In general, higher path loss

exponent is desirable for interference-limited systems. We will elaborate the cellular

system designs in Chapter 3.

Shadowing introduces randomness in the coverage of cellular systems. For instance,

if the standard derivation of the shadowing component is large, the average

received signal power at the cell edge will have large fluctuations. To achieve a certain

Quality of Service (QoS) such as 90% probability that the received signal strength at

the cell edge is above a target threshold, higher power or shorter cell radius is needed

to allow for some shadowing margins to satisfy the QoS target. Hence, shadowing

with larger standard derivation is undesirable.

Finally, the effects of microscopic fading have high impact on the physical layer

design of communication systems. For instance, by common sense, it is more challenging

to setup a wireless link to transmit high quality video then audio signals. This

is because for the same environment, transmitting a video signal generally involves

a higher transmit bandwidth, Wtr, relative to an audio signal. From Section 1.4.1, it

is very likely that the video signal will experience frequency selective fading while

the audio signal will experience frequency flat fading. As we elaborate in Chapter 4,

frequency selective fading will introduce intersymbol interference (ISI) and this induces

irreducible error floor. Hence, complex equalization at the receiver is needed.

On the other hand, for audio signal, since Wt, << B,, the signal will experience

flat fading only and no equalization at the receiver is needed. Hence, this results

in more simple design. Another common-sense example is that it is easier to setup

a wireless link for indoor environment than an outdoor environment to transmit the

same signal. This again can be analyzed by the frequency selective or frequency flat

fading channels. For the same transmit signal, the indoor environment in general

has a larger coherence bandwidth and hence, the number of resolvable multipaths

in indoor environment will be small. On the other hand, the coherence bandwidth

for outdoor environment in general will be small and this results in larger number of

resolvable multipaths (frequency selective fading). Hence, more complicated designs

are needed in the latter case.

To be Continue,,,,

The Mobile Radio Propagation Channel Part 5

Time Varying dimension: To characterize the time variation dimension, we can

have either the Doppler spread or coherence time. Doppler spread [ 1671 refers

to the spread of frequency introduced by the fading channel when we transmit

a single tone signal (narrow pulse in frequency domain) as illustrated in

Figure 1.7(a). The spread in frequency is due to the mobility between the

transmitter and the receiver or the mobility of the surrounding obstacles. The

maximum Doppler spread is given by:

where ƛ is the wavelength of the signal and ti is the maximum speed between

the mobile and the base station. Typical values of Doppler spread for pedestrian

users (5km/hr) and vehicular users (100km/hr) at carrier frequency of 2.4 GHz

are given by 14 Hz and 300 Hz respectively.

Figure 1.6 Illustration of the delay spread and coherence bandwidth.

Figure 1.7 Illustration of the doppler spread and coherence time.

Similarly, an equivalent parameter to quantify the time variation dimension of

microscopic fading is given by the coherence time. Figure 1.7(b) illustrates the

concept of coherence time. Suppose we transmit two narrow pulses in time

domain at times t = tl and t = t 2 with unit energy. After channel fading, the

corresponding signals will be received at t = tl + 71 and t = t 2 + 7 2 with

energies a1 and a2 respectively. Similar to coherence bandwidth, if we separate

the two transmissions sufficiently far in time, (│ ț1 +ț2 │z  > Tc), the channel

fading a1 and a2 are uncorrelated. Hence, the coherence time is defined as

the minimum time separation at the transmitter in order to have uncorrelated

fading at the receiver. It is found that for 0.5 correlation coefficient between

a1 and a2, the coherence time T, is related to the Doppler spread f d by:

Note that Doppler spread and coherence time are two sides of the same coin to

characterize the time variation dimension of microscopic fading.

1.4.1 Fiat Fading vs Frequency Selective Fading

Based on the delay spread and the transmitted symbol duration, (or equivalently,

the coherence bandwidth and the transmitted bandwidth), the multipath fading can

be classified as frequencyjat fading or frequency selective fading [287]. A fading

channel is said to be frequencyjat if ~7~< T, or equivalently, B, > Wt, where T,

and W,, are the symbol duration and the transmitted signal bandwidth, oT and B, are

the delay spread and the coherence bandwidth, respectively. Otherwise, the fading

channel is said to be frequency selective. Note that whether the transmitted signal

will experience frequency flat fading or frequency selective fading depends on both

the channel parameters (such as oT,B ,) as well as the transmitted signal parameters

(such as T, and Wt,).

When a transmitted signal experience frequency flat fading, the received signal

consists of superposition of multipath signals but the multipaths have delays much

smaller than the symbol duration T, and hence, the multipaths are unresolvable. As

a result of the unresolvable multipath, the received pulse shape will be different from

the transmitted pulse shape but the received pulse appears as one single pulse at the

receiver. The received signal is modeled as:

where h(t) is the time-varying channel fading which is modeled as complex Gaussian

random variable with zero mean and unit variance, z ( t ) is the complex AWGN noise

and z ( t ) is the low-pass equivalent complex transmitted signal.

On the other hand, when a transmitted signal experience frequency selective fading,

the received signal consists of multipaths with delay >> symbol duration (T,). Hence,

the received signal consists of pulses at resolvable deZays (integral multiples of T,),

In general, the number of resolvable multipaths is given by:

The received signal for frequency selective fading channel is modeled as:

where hi ( t )is the time varying channel fading for the i-th resolvable multipaths which

is modeled as complex Gaussian random variable with zero mean and unit variance.

to be continue....

The Mobile Radio Propagation Channel Part 4

1.4 SMALL SCALE MULTIPATH FADING EFFECTS

The third component of variation of received signal strength is called the small scale

fading or microscopicfading. In the small scale fading component, the signal strength

fluctuates over 30dB within a very short time scale of the order of milli-second as

illustrated in Figure 1.4. There are two independent dimensions in microscopic fading,

namely the multipath dimension and the time-varying dimension. They are elaborated

below.

Figure 1.5 illustrates the concept of multipath dimension and time varying dimension

of microscopic fading. For example, a narrow pulse transmitted at t = t o results

in one resolvable received pulse. The received pulse in general has a different shape

compared with the transmitted pulse because of multipaths propagation. At t = t l ,

the same transmit pulse results in two resolvable pulses with the first pulse stronger

than the second pulse. At t = t 2 , the same transmit pulse results in two resolvable

pulses with the first pulse weaker than the second pulse. Note that the multipath

dimension and the time-varying dimension are two independent dimensions. We

quantify the two dimensions below.

Multipath dimension: To quantify the multipath dimension in microscopic fading,

we can either look at the delay spread or coherence bandwidth. Delay spread

Figure 1.4 Illustration of the envelope and phase variations in microscopic fading.

Figure 1.5 Illustration of the multipath and time-varying dimensions in microscopic fading.

is defined as the range of multipath components with significant power when

an impulse is transmitted as illustrated in Figure 1.6 In Figure 1.6(a), a narrow

pulse (in time) at the transmitter results in a spread of energy across the delay

dimension. The graph of received power versus delay is called the powerdelay

profile and is used to measure the delay spread experimentally. Typically,

the delay spread in enclosed indoor environment is smaller than the outdoor

environment because the range of delays in the multipaths are more contained

in the indoor environment.

On the other hand, to quantify the multipath dimension [167], an equivalent

parameter, namely the coherence bandwidth, can be used. Figure 1.6(b) illustrates

the concept of coherence bandwidth. Suppose at the transmitter, we

transmit two single tone signals (impulse in frequency domain) with unit power

and frequencies fi and f2 respectively. At the receiver, the two single tones will

be received with received powers a1 and a2 respectively. cq and a2 represent

the random channel attenuation (or fading) experienced by the two single-tone

signals. It is found that if If1 - f2l > B,, the attenuation a1 and a2 are uncorrelated.

Otherwise, the attenuation a1 and a2 are correlated. Hence, the

minimum separation of frequency such that uncorrelated fading is resulted is

called the coherence bandwidth, B,. It is found that for 0.5 correlation coefficient

between al and a2, the coherence bandwidth is related to the delay

spread oT by:

Typical values of the coherence bandwidth for indoor and outdoor environment

are lMHz and lOOkHz respectively. Note that delay spread and coherence bandwidth

are two sides of the same coin to characterize the multipath dimension

of microscopic fading.

to be continue........

The Mobile Radio Propagation Channel Part 3

The free space path loss equation is not accurate to model the path loss in terrestrial

wireless communications with multipaths from the transmitter to the receiver. In

general, the path loss model can be represented by the equation:

PL(dB) = PL(d0) + 10nloglo(d/do)

where n is the path loss exponent which depends on the environment. Table 1.1

illustrates the values of path loss exponents under various environment.

Table 1.1 Path loss exponents.

Environment	Path Loss Exponent
In building line-of-sight Free space Obstructed in factories Urban area cellular Shadowed urban cellular Obstructed in building	1.6-1.8 2 2-3 2.7-3.5 3-5 4-6

The higher the path loss exponent is, the faster the signal strength drops with respect

to the increase in distance. For example, a commonly assumed path loss exponent in

non line-of-sight environment is n = 4. In this case, doubling the distance separation

between the transmitter and the receiver will result in 16 times reduction in received

signal strength.

In some more complicated propagation environments such as irregular terrain and

cities, there is no simple analytical path loss model. Empirical models based on

extensive channel measurements are used to model the path loss versus distance in

such complex environments. Examples are the Okumura model [261], Hata model

and COST 231 extension to the Hata model [316] for cellular systems simulations

and link budget analysis.

1.3 SHADOWING EFFECTS

In the shadowing model, we are interested to study the medium term variation in

received signal strength when the distance between the transmitter and receiver is

fixed. For example, image a mobile station is circling around a base station with radius

T . As the mobile moves, one expects some medium term fluctuations in the received

signal strength but the variation is not due to the path loss component because the

distance between the transmitter and the receiver is not changed. This signal variation

is due to the variations in terrain profile such as variation in the blockage due to trees,

buildings, hills, etc. This effect is called shadowing. Consider a signal undergoing

multiple reflections (each with an attenuation factor ai) and multiple diffractions (each

with an attenuation factor bi) as illustrated in Figure 1.3

Figure 1.3

hills, etc.

Shadowing model-variations in path loss predication due to buildings, trees,

The received signal strength is given by:

where N, and Nt denote the number of obstacles with reflecting and diffracting the

signal respectively. Expressing the received power in dB, we have:

where ơ~i is the attenuation coefficient due to reflections or diffractions. Each of

the term ơ!i (dB) represents a random and statistically independent attenuation. As

the number of reflectors and diffractors increase, by central limit theorem, the sum

S(dB)= ∑I a;i (dB)a pproaches a Gaussian random variable. Hence, the received

signal power can be expressed as:

where S(dB) N ( ṵ s , 02) and X ( d B ) N(0, 02). The mean shadowing ps is usually

included into the path loss model and that is why the path loss exponent can be larger

than 2. Hence, without loss of generality, we move the term ps(dB) to the path

loss model and consider only the shadowing effect X ( d B ) . Expressing the received

power in linear scale, we have:

where A, =10 ˣ̷¹º is the power attenuation due to shadowing and is modeled by

the log-normal distribution with standard derivation o (in dB).

Combining the path loss model and the shadowing model, the overall path loss is

given by:

where PL,, is the path loss component obtained from the large scale path loss model

and X ( d B ) is the shadowing component which is modeled as a zero-mean Guassian

random variable with standard derivation 0 in dB.

to be continue...