I'm watchinng in youtube : Waching daily Oct 19 2018

How to draw Richard Watterson

Don't miss any Blabla Art episode

Subscribe! (◕‿◕)

For more infomation >> HOW TO DRAW RICHARD WATERSON from THE AMAZING WORLD OF GUMBALL | Blabla Art - Duration: 10:14.

-------------------------------------------

PA PA BUM BUM BUM REMIX, (nEW Break Mix) - By Mrr Thea Ft Mrr Chav Chav And Mrr Dii [TCD] - Duration: 3:28.

For more infomation >> PA PA BUM BUM BUM REMIX, (nEW Break Mix) - By Mrr Thea Ft Mrr Chav Chav And Mrr Dii [TCD] - Duration: 3:28.

-------------------------------------------

(Hindi)50k+ Yo Yo Fans Joined| Thank you guys| YYHS BACK AGAIN - Duration: 1:10.

For more infomation >> (Hindi)50k+ Yo Yo Fans Joined| Thank you guys| YYHS BACK AGAIN - Duration: 1:10.

-------------------------------------------

Humpty Dumpty | Kindergarten Nursery Rhymes for Children | Kids Songs & Cartoons by Little Treehouse - Duration: 1:02:25.

Humpty Dumpty sat on a wall,

Humpty Dumpty had a great fall;

All the king's horses and all the king's men

Couldn't put Humpty together again.

Humpty Dumpty sat on a wall,

Humpty Dumpty had a great fall;

All the king's horses and all the king's men

Couldn't put Humpty together again.

Humpty Dumpty sat on a wall,

Humpty Dumpty had a great fall;

All the king's horses and all the king's men

Couldn't put Humpty together again.

Humpty Dumpty sat on a wall,

Humpty Dumpty had a great fall;

All the king's horses and all the king's men

Couldn't put Humpty together again.

For more infomation >> Humpty Dumpty | Kindergarten Nursery Rhymes for Children | Kids Songs & Cartoons by Little Treehouse - Duration: 1:02:25.

-------------------------------------------

Gorilla finger family nursery rhymes songs for kids | learn 3d rhymes for children,lion,NASH TOON Tv - Duration: 3:38.

finger family nursery rhymes

For more infomation >> Gorilla finger family nursery rhymes songs for kids | learn 3d rhymes for children,lion,NASH TOON Tv - Duration: 3:38.

-------------------------------------------

The Polar Code for 5G Communication - Duration: 15:03.

Telecommunication technology today

is playing an increasingly important role in the whole industry,

especially the coming generation of cellular mobile communication: 5g

It will boost up many other domains.

such as truly immersive AR and VR applications

Automated driving vehicles

ultimately the tactile internet for 5g communication.

so, in order to achieve

one aspect that of 5g communication

: the enhanced mobile broadband communication service

the polar code has been chosen as channel coding algorithm for the control channel

by the 3GPP conference.

The main purpose of our project is to explore and

inspect the properties and performance of the polar code.

Furthermore grab a perspective of the channel coding algorithm

In our system a code construction an encoder

and adecoder algorithms were desinged on Matlab.

for the code construction algorithm

It is just combining the binary tree and the Bhattacharyya bounds of

AWGN channel. As for the encoder it is mainly based on the Kronecker product

As for decoder the SCD algorithm was used

since it is the most fundamental

and important decoding method of polar codes which achieves very decent

time complexity of nearly N*log(N) it can be separated into two parts one is log

likelihood calculation the other one is bit broadcasting.

they are interacting with each other, the decoding root bits are actually what we want.

you will be demonstrated as following

After completing the polar code program

we found that the channel performance will be affected by

varying the code rate, code length and design SNR.

The longer code length or smaller code rate will result in the better

performance and the best design SNR was found to be zero in our project

we would like to give an overview of the significant contribution.

Firstly, we simulated and evaluated the polar code for the AWGN channel.

Then we implemented and evaluated the code construction algorithm

using the Bhattacharyya parameters

After that we implemented the channel encoder using the Kronecker product.

Moreover we derived a logarithmic likelihood ratio by channel observation

Then, we implemented the successive cancellation algorithm into decoding procedure.

Next we evaluated the Polar code performance in terms of different design SNR

Then we evaluated the polar code performance in terms of different code rate code length.

Lastly we reconstructed a noisy image by implementing the polar code.

Lets start with the code construction

Firstly,here are two bits channel specifically

the binary erasable channel with an erasure probability e.

Then we combine them using mode two operation

This can be seen in the lower figure.

Therefore at the decoder side

The received bits are supposed to be exactly X1, X2.

However due to the property of BEC

there is probability e that the transmitted information will be erased

therefore there are four possible situations as a decoder side for y 1 y 2

These are the four situations.

The question mark means the information is erased already.

In order to derive the polarization outcome

one of the important properties of successive cancellation decoding algorithm is used here

: the outputs of these two channel are (y1, y2) and( y1, y2, u 1) which means that

the extra information u1 is needed to decode y2.

It can be noticed that only in s4 where the knowledge about

(u1 +u2) mod 2 and u 2 is known simultaneously

in other words, neither X1 nor X2 are not erased the Y1 can be decoded correctly.

Consequently, the probability of the error occurring in the upper channel is E1 equals to 2*e-e^2.

However due to a piece of extra information is added to decode Y 2

there are 2 more situations where Y2 can be decoded successfully.

These are the three situations

Consequently, the error probability of the lower channel is E2 which is e^2.

It can be proven that the E1 is always larger than E2

given that the error probability e is a number within range from 0 to 1

with performing this operation recursively the better channel becomes

better marked by more '+' sign, the worse channel even becomes worse marked by more '-' sign.

Consequently a portion of the channel

will become nearly completely noisy while some other channels become nearly

noseless which exactly clarifies some Matthew effect in communication aspect.

The channel evolution can be summarized as a binary tree the initial value is a

Bhattacharyya bounds of a AWGN channel. We stop the iterative

process until the last stage hold N values which is the code length. Then we select

out the leaves holding K least values and it is the number of information bits.

After that we output the indices of these leaves, the rest indices are the

frozen bits indices. These 3 figure illustrates the channel capacity

when thestage equals 3, 6, 11, corresponding to the theory before more channels are polarized

with increasing the code length , Consequently the Shannon capacity will be achieved

when the code length equals infinity

Let's move on to the encoder now. The polar coder is parameterized the by

the code length: N, the information bits length: K, and the indices of information bits: I

Once we have known them, a transmitted vector can be formed

in this vector the information bits are stored in the leaves which holds the K least values,

the complementary indices are frozen bits indices, this is corresponding to the code construction

Since the polar code is one type of linear block code

a generator matrix is needed.

In order to describe the operation in the

butterfly diagram.

The Kronecker product is used to generate a generator matrix

Once we have it,

the code word is generated by multiplying the transmitted

vector and the generator matrix,

this slide gives an example for the code length equals to 8.

The butterfly diagram is a basic unit of decoding algorithm

the u 1, u 2 y 1, y 2 are the bits and L1 L2 function f and g are likelihood ratios

the bits are determined by likelihood ratios,

here is a formula for function G and F.

The proof process of this formula are demonstrated on Arikan's paper and

our final report.

So it is very reliable

the time complexity for decoding is N

times( Log(N)+1), since there is N binary tree and each binary has

(logN)+1 layers.

The channel observations are given here

likelihood ratio can be derived by channel observation,

here is a result

bits and likelihood ratios

they are set as negative 1 as initial value.

The right most likelihood ratios are based on channel observation

which is given

and the frozen bit are picked because of the lowest channel capacity that has stated previously

they are forced to be 0 and initial and both decoder and encoder would notice

then the index 0 can be calculated recursively by using function f

after root likelihood ratio is calculated here the bit can be decided

if likelihood ratio is larger than 1 then the bit is 0, otherwise the bit is 1

then the likelihood ratio of index 4 can be decided by function G

when u1 here equals to 0, G equals to L 1 times L2,

when u 1 equals to 1, G equals to L 2 divided by L1

Then u 1 and u 2 can be broadcasted right towards together.

Once bits reached

a layer here the process stopped because the layer branch bits have not decided

so the G function would be a applied here

since the bit is known upper branch

then using these two likelihood ratios,

likelihood ratio of index 2 can be calculated

using function f,

then it bit can be decided

same procedure can be implemented as the following

function G can be applied here determine the bits

broadcasting tell the upper branch

One trick made in our program is we assign the search order as bit reverse sequence

It needs to be noticed that the B reverse permutation in our case that not

rearranged our original bid order

it just changed the searching sequence of decoder

in order to improve the searching efficiency

so the encoder does not need to do anything with it

finally all bits are decided

the bits at the leftmost here are the decoded bits.

The yellow frame indicated that

there are some conflict between likelihood ratio and real bits

because of the existence of frozen bits

And when longer code length is implemented

log likelihood ratio always performs better than likelihood ratio.

It should be noticed that

During the whole decoding process the bit decision can only happen the root index here,

because the root index get all information from the nodes

which would result in better bit prediction than other nodes

this is also one reason why longer code length has better performance which is a beauty of polar codes

it is time to evaluate the performance after converting all the

theory and knowledge about polar code into MATLAB coding,

as we can see here

we dynamically changing design SNR, in the end we obtain the decent bit error rate

which is up to 10 to the power minus 4 after that due to the

non-universality of polar code

the different polar code are generated by the

different design SNR.

In this section we are committed to select out of the

best design SNR for the code length equals 1024 and 2048.

The results shows that the best design SNR is around 0 dB for both cases

these two graphs illustrate the influence of code on channel performance.

So smaller code rate has best performance in testing samples.

It is reasonable since the redundancy in the polar code is assigned to be frozen bits .

Fixed values won't be wrongly decoded.

Besides they would affect and

correct the decoding procedure comparing to the last redundancy situation.

As we stated previously longer code length has better performance because of the channel polarization.

Furthermore in the bad channel condition the performance of all

code length tend to have the same value, at 0.5 dB when R equals to 0.1.

1.5 dB when R equals to 0.5.

After these points

the trend of the performance of the code length versus SNR is becoming more obvious

In the end we would like to thank our supervisor Dr. Yi Hong for giving us an opportunity to complete

such an interesting project, also thanks Harish for giving us a lot of help

in terms of future work we have more students and put efforts on implementing

different code construction such as Monte Carlo and more complex decoding algorithms

such as BP and folded SCD to explore more properties of polar codes.

Thank you

For more infomation >> The Polar Code for 5G Communication - Duration: 15:03.

-------------------------------------------

How to update Google play store forcefully if it is not updating - Duration: 0:42.

If you want to force the Google Play store to check for the new update and update it

if available then open Play store.

Tap on this more option.

Then go to the settings.

Now scroll down and go to Play store version.

Now if there is any update available it will automatically find and update it.

This is how you can force the Google Play store to check for

the update.

For more infomation >> How to update Google play store forcefully if it is not updating - Duration: 0:42.

-------------------------------------------

กลั้นใจ (MEME) - Duration: 1:02.

For more infomation >> กลั้นใจ (MEME) - Duration: 1:02.

-------------------------------------------

Azadari Kharmang Khas Gilgit Baltistanعاشورہ باغیچہ کہرمنگ - Duration: 11:14.

For more infomation >> Azadari Kharmang Khas Gilgit Baltistanعاشورہ باغیچہ کہرمنگ - Duration: 11:14.

-------------------------------------------

Free Registered Wondershare Filmroa Free 100% Working (Life Time Use) - Duration: 4:02.

I'm watchinng in youtube

Thứ Năm, 18 tháng 10, 2018

Waching daily Oct 19 2018

Không có nhận xét nào:

Đăng nhận xét

Lưu trữ Blog