VIETNAM NATIONAL UNIVERSITY HANOI

UNIVERSITY OF ENGINEERING AND TECHNOLOGY

FACULTY OF ELECTRONICS AND TELECOMMUNICATIONS

------

STUDENT RESEARCH CONTEST 2012 – 2013

CQI-based scheduling algorithms in 3GPP LTE

Author: Đinh Việt Anh

D.o.B: 27 / 09 / 1991

ID No.: 09020006 Class: K54D

Advisor: Dr. Nguyễn Quốc Tuấn

Department: Telecommunications System

FET, UET, VNU-H

Ha Noi, March 2013

Page | 1

Abstract

3GPP Long Term Evolution (LTE) was developed based on 3GPP UMTS

(Universal Mobile Telecommunications System). LTE allows the

subscriber access the Internet from terminals with higher data rate and

lower latency.

LTE operates in many frequency bands but time and frequency are limited.

Therefore, like other network system being implemented, saving radio

resources in LTE is a considerable problem. Effective performance of the

scheduler in eNodeB of LTE certainly plays an important role in the overall

performance of the system. There are many scheduling algorithms was

implemented, overall, such scheduling algorithms based on the channel

quality indicator CQI is being used widely in large system.

In this report, I will take an overview on the system model of 3GPP LTE,

including resource allocation and technologies used in data transmission.

Then, I will focus on an important factor in shared radio resource allocation

in the LTE downlink: the scheduling algorithm.

The content of this report will take concentration on the operation of Best

CQI scheduling: describes, evaluates and compares with a normal CQI-

based scheduling algorithm in order to represent the advantages and also

disadvantages of these two scheduling algorithms due to the performance

of LTE system.

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1. Introduction

In this chapter, we will introduce the concept of LTE and its requirements

as well as the highlighted features of LTE network system in part 1.1. Then,

we will consider about the matter of radio resource allocation and

management in part 1.2. Finally, the work on this report will be mentioned.

1.1. LTE and its requirements

In the last few years, multimedia applications operate in user terminals

using the Internet are being well developed along with the improvement of

broadband mobile communications system. These types of applications

require higher data rate. The HSPA/UMTS system is being implemented to

meet this demand. 3GPP organization also keeps developing the

performance of network system.

LTE – Long Term Evolution, developed by the Third Generation

Partnership Project – 3GPP, is a standard for wireless communications with

high data rate for mobile and other types of terminals. The technologies

used in LTE are the improvement of GSM/EDGE and UMTS/HSPA, to

increase system throughput by using enhanced radio transmission interface

together with a number of improvements in the core network.

LTE can support subscribers with a maximum data rate of 100 Mb/s in the

downlink and 50 Mb/s in the uplink, corresponding to the spectral

efficiency and the bit rate in the downlink 3-4 times, in the uplink 2-3 times

greater than HSPA/UMTS system. LTE system has a high flexibility with

an operating bandwidth from 1.4 MHz to 20 MHz, supporting the user’s

movement speed up to 350 km/h, resulting the required user latency is 5ms

with 5 MHz or higher spectrum allocation and acceptably 10ms with

narrower bandwidth. System capacity is also increased significantly by

using MIMO transmission technique along with OFDM technology to save

channel bandwidth. LTE can have the best performance in coverage of 5

km radius and guarantee a connection in the radius of 30 km.

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1.2. What is scheduling?

Time and frequency is the two limited resources in any radio

communication system. Thus, using these resources effectively is main

factor that contributes to the success of any network system. In addition to

using multiplexing technology to save bandwidth, an effective scheduling

mechanism also maximize the usage of radio resources.

The scheduler with an optimized scheduling algorithm is considered as a

key element of the base station with functions of resource management and

distribution; decide which user will be assigned to the resource block.

There are many types of scheduling in wireless network, for example, Best

CQI, Round Robin, Proportional Fair, Fast Fair Throughput … But Best

CQI and Round Robin is two basic scheduling algorithms representing two

main factors of scheduling: fairness and throughput.

Round Robin has a simple principle of operation and is easy to implement

as it only polls over all users, user’s data will be assigned to the resource

block in a fixed interval, then, move to the next user. That, in turn, ensures

the fairness between users. Best CQI is a scheduling algorithm based on

channel quality. Each time CQI updated, user’s CQI will be calculated by

the base station. User with the best CQI, respectively the best channel, will

be chosen to assign its data to resource block. Thus, the channel capacity is

always in maximum status because the quality of transmission channel is

always the best. However, for users who stay far from base station or travel

with high speed, their channel quality is not guaranteed, then, the

permission for using system resource is really difficult.

1.3. Report’s goal

The main purpose of this report is simulating the operation and evaluating

the performance of Best CQI scheduling algorithm. In other hand, another

simple CQI-based scheduling algorithm is also proposed in order to

compare and analyze the advantages of Best CQI scheduling mechanism.

The measurement of performance is the total system throughput.

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2. System Model

In this chapter, we will provide a general view of LTE network and the

technologies used in LTE. Section 2.1 describes the allocation and

management of radio resource. Next, the Orthogonal Frequency Division

Multiplexing will be briefly introduced in section 2.2. An element which is

used to estimate the channel quality will be mentioned in section 2.3. In the

final section, we will describe a theoretical system capacity in LTE

2.1. Resource allocation in LTE

Each radio frame is ms 10307200 sf TT long and consists of 20 slots of

length ms 5.0T15360 sslot T , numbered from 0 to 19. A sub-frame is

defined as two consecutive slots where sub-frame i consists of slots i2

and 12 i . Each slot contains 7 or 6 OFDM symbol (depends on normal or

extended cyclic prefix).

Figure 1. Frame structure in LTE

[5]

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In LTE, radio resource in downlink can be imagined as a grid of resource

block (RB) in time - frequency domain.

Figure 2. Resource Block in “normal cyclic prefix” [5]

Each RB is a part of one slot with a bandwidth of 180 kHz. This bandwidth

is divided into 12 sub-carriers and the sub-carrier spacing is 15 kHz.

2.2. Orthogonal Frequency Division Multiplexing

OFDM has been adopted as the downlink transmission scheme for the

3GPP LTE. OFDM is a multicarrier transmission scheme because it splits

the input bit-stream signal into N parallel signals. These signals, then, are

modulated by N sub-carrier mutually orthogonal using different levels of

modulation such as QPSK, 16-QAM and 64-QAM. Finally, these sub-

carriers is multiplexed in OFDM symbol and transmitted on channels.

Orthogonal characteristic of sub-carrier allows signals to be modulated

overlap but also maintain the separating at the receiver because the peak at

central frequency of this sub-carrier locates exactly at the null of other sub-

carrier. Thus, sub-carriers wouldn’t be affected by Inter-Carrier

Interference. Furthermore, the overlap of sub-carrier also contributes to

bandwidth saving.

Figure 3. Spectrum of Orthogonal Sub-carriers

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2.3. Reference symbol in LTE downlink

Reference symbol (RS) is the symbol that both transmitter and receiver

already know. These symbols are put in a RB in order to estimate channel

quality.

Figure 4. Location of Reference symbols in sub-frame in normal CP

In time domain, these symbols are added to the OFDM symbol of each slot

in the first and the fifth position in the “normal CP”, or the first and the

fourth in the “extended CP”. In frequency domain, RSs are added every 6

sub-carriers. The unique positioning of the pilots ensures that they do not

interfere with one another and can be used to provide reliable channel

estimation.

All the RS found in a sub-carrier are time averaged across all OFDM

symbol, resulting in a column vector containing the average for each

reference signal sub-carrier. Thus, for each time slot, there is a fixed

number of reference signal sub-carrier transmitted creating a reference

signal xRS. The base station will receive an output signal of yRS. Then, with

yRS obtained, the BS will find out the channel characteristic by using:

From this characteristic, BS will calculate SNR of the channel and

determine the corresponding CQI to feedback to the UE to set the

modulation order and coding rate for UE signal transmission.

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2.4. Channel capacity

The capacity of an AWGN channel can be calculated by the Shannon

formula [11]

:

where C is channel capacity, B is bandwidth of the channel that occupied

by users, and SNR is Signal-to-Noise Ratio.

In each time slot of the LTE system, data is transmitted together with

Cyclic Prefix (CP) to avoid Inter-Symbol Interference (ISI) and Reference

Symbol (RS) to estimate the channel quality. Therefore, a correlative factor

F is given to represent the inherent loss of the system for CP and RS.

⏟

⏟

with Nsc is the number of sub-carrier in each time slot, Ns is the number

OFDM symbol in each slot, Tslot is the slot duration, and Tcp represents total

time for CP in all OFDM symbols in a frame.

Therefore, the channel capacity in LTE is represented by the modified

Shannon as followed:

However, this theoretical capacity is only the upper bound of practical

channel capacity. In this report, I will introduce an alternative formula to

calculate channel throughput.

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3. Fundamental Problem Simulation and Evaluation

In this chapter, a fundamental problem in LTE in particular and any

communication system in general is described in section 3.1. Section 3.2

will describe the principles and operation of the Best CQI scheduling

algorithm and a conventional CQI-based scheduling algorithm as well.

Then, we will present a simulation scenario for above scheduling

mechanisms in section 3.3. The next section will give more details about

the simulation source code. Finally, the simulation results will be analyzed.

3.1. Fundamental Problem

Time and frequency is limited resources, then, using these resources

effectively is a fundamental element contributes to the success of every

network system. These resources are shared by many users, so, in each

system, we need some techniques to control this sharing.

Figure 7. Medium Sharing Access Control

There are two types of controls: static and dynamic. Static channelization is

waste of resources and has low system performance. Then, nowadays, we

likely use Dynamic Medium Access Control. Dynamic Medium Access

Control has two types. Random Access is only used for the system in

which users are peer entities, for example ad-hoc system. But in

hierarchical system like LTE, scheduling is used in base station to control

user’s access.

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The purpose of scheduling is to avoid collision, maximize bandwidth usage

and improve system performance. As mentioned above, there are many

types of scheduling in wireless network, but in this report, I will take

concentration on Best CQI scheduling, simulate and evaluate its operation.

Furthermore, a normal CQI-based scheduling algorithm is built based on

Round Robin principles to compare with Best CQI.

3.2. Principles of CQI-based scheduling algorithms

In this report, two different types of CQI-based scheduling algorithm will

be examined. This section will describe the principles of operation for these

two algorithms.

After receiving reference signals from users, the BS will calculate SNR

based on the channel characteristic. Then, it will determine the

corresponding CQI standardized level due to the following model:

Figure 8. SNR-CQI mapping model

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From above SNR-CQI model, we can figure out that CQI is calculated as a

step function of SNR with SNR [dB] = -6 corresponding to CQI = 1 and SNR

[dB] = 20 with CQI = 15. Thus, we can express the relationship between

SNR and CQI according to the following formula:

{

[ ]

[ [ ]

] [ ]

[ ]

The bigger CQI is, the better channel is. Scheduler will then selects the

order of signal modulation and coding rate that the channel can support

based on the following table:

Table 3. CQI table

CQI index Modulation

Scheme

Code Rate

(× 1024)

Efficiency

(b/s/Hz)

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

QPSK

QPSK

QPSK

QPSK

QPSK

QPSK

16-QAM

16-QAM

16-QAM

64-QAM

64-QAM

64-QAM

64-QAM

64-QAM

64-QAM

78

120

193

308

449

602

378

490

616

466

567

666

772

873

948

0.1523

0.2344

0.3770

0.6016

0.8770

1.1758

1.4766

1.9141

2.4063

2.7305

3.3223

3.9023

4.5234

5.1152

5.5547

From the corresponding spectral efficiency of users’ CQI, the user’s

throughput will be given by:

where cj is the spectral efficiency at the CQI j.

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Best CQI scheduling algorithm

As its name, after having the users’ CQI for each time slot, the scheduler

scans the CQI of all users, selects users with CQI from the highest to the

lowest to assigned their data into channel such that the bandwidth for each

user’s service is maximize and the total bandwidth for all users is not

greater than channel bandwidth.

In summary, the scheduler will select user to schedule based on the

following criteria:

{ } if {

where buffer(u) is the data that user requested remaining in buffer, BWservice

is the maximum bandwidth for user’s service, remain BWchannel is the

remaining channel bandwidth and remain CQIs is the set of users which

haven’t been scheduled.

This criteria show that there’s very small chance for the user with low

channel quality to be scheduled, especially when the channel bandwidth is

small. Therefore, this scheduling algorithm is said to be unfair.

Figure 9. Best CQI scheduling

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For example, in figure 9, UE with best CQI is not definitely selected to

schedule because it might not have any data to receive. Then, the scheduler

will switch to the next user that has lower CQI level.

After a fixed interval of time, CQI will be updated; then, the scheduler

again sorts users’ CQI to schedule from user with the highest CQI

downward.

The operation of Best CQI scheduling is illustrated in the following

flowchart:

Figure 10. Best CQI scheduling flow-chart

Conventional CQI-based scheduling algorithm

This scheduling algorithm is relatively similar to the principles Round

Robin algorithm but it differs slightly by taking the channel quality into

account.

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Figure 11. Normal CQI-based scheduling

For each slot, scheduler will poll around users, assign users’ data to

considering time slot such that the total bandwidth of all users is not greater

than channel bandwidth and user’s bandwidth is maximized. Then,

scheduler will continue to the next user for the next slot. User throughput

will be calculated based on the user's CQI for each slot. The flowchart

below illustrates the operation of this scheduling mechanism:

Figure 12. Normal CQI-based flowchart

This report will implement, simulate and compare the operation of these

two scheduling algorithms in order to evaluate the performance and point

out the better feature of each.

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3.3. Simulation scenario

A simulation scenario is given with some main parameters as follow in

order to illustrate and compare the operation of the two CQI-based

scheduling algorithms:

Table 2. Simulation parameters

Number of base station 1

Channel bandwidth 1.4 MHz

Number of users 6

Max Bandwidth for Service

Voice service - 128 kHz

Data service - 384 kHz

Video service - 1MHz

CQI update time 8 sub-frames

Simulation time 500 sub-frames

Scheduling algorithms will be implemented in one cell with one base

station only.

Simulation program will monitor the overall system capacity in time

domain as well as throughput for each CQI and also the changes in users’

channel quality. The number of users is selected to 6 to maintain the system

stability and simplify the simulation program. The simulation results can

easily be evaluated on the graph also.

Users’ requested service (corresponding to the maximum bandwidth for

that user) and the amount of data that the user requested will be generated

randomly. Since the goal of this program is only interested in the operation

of scheduling algorithms, not the reliability of communication, we will not

mention the buffer size for each user. And to simplify, users would only be

allowed to request if its last request has been completed.

In fact, users’ SNR varies slowly, almost unchanged when the user is at

fixed position. Users’ movement and some impacts of the environment

(such as rain or large obstacles moving through) create some types of

fading. Similarly, users’ CQI also changes with the same trend as SNR.

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Simulation time is set to 500 sub-frames. Since LTE can support users’

movement speed up to 350 km/h, and user latency is 10ms for 1.4 MHz

bandwidth, from channel estimation point of view, this result in a block

length less than 10ms (10 sub-frames) for appropriate estimation purpose.

Thus, the update interval of CQI is set to 8 sub-frames to catch up with the

changes in channel quality. CQI will be updated enough times during

simulation time to ensure that the operation of the scheduling algorithms is

simulated correctly.

3.4. Program Analysis

Due to the operation principle of two algorithms described in the previous

section, we have built a program to simulate both algorithms at the same

time to ensure the same random factors for both.

At first, we create and initiate some main parameters of simulation.

nUE = 6; % number of UEs BW = 1.4*10^6; % bandwidth of channel t_slot = 4; % slot time interval (second) sim_time = 500; % simulation time (second) CQI_update_interval = 8; % CQI update interval (second) eff = [0.1523 0.2344 0.3770 0.6016 0.8770 1.1758 1.4766 1.9141 ... 2.4063 2.7305 3.3223 3.9023 4.5234 5.1152 5.5547]; % spectrum efficiency with CQI maxBW = zeros(1,nUE); % max BW for UE corresponding to its service % Variables in Best CQI scheduling buffer_best = zeros(1,nUE); % buffer for each UE sys_throughput_best = zeros(1,uint64(sim_time/t_slot)); % System Throughput % Variables in Normal CQI-based scheduling buffer_norm = zeros(1,nUE); % buffer for each UE sys_throughput_norm = zeros(1,uint64(sim_time/t_slot)); % System Throughput ue = 1; % the first polled UE % Load generated CQI CQI_update_time = 0; CQI = load('CQI.txt','-ascii');

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For each scheduling algorithm, in each slot, system capacity, buffer for

each UE as well as throughput corresponding to each CQI is calculated and

saved in variable buffer_(mode), sys_throughput_(mode) and

throughput_CQI_(mode) respectively (where mode is “best” for the Best

CQI scheduling, “norm” for the conventional CQI-based scheduling

algorithm). These values are initialized to 0.

CQI is generated before and loaded into database in order to represent the

changes in channel quality which are approximately the same as real

channel quality.

Users’ data will be generated randomly depend on their service. Users’ data

for both scheduling algorithms should be the same.

service = randi([1 3]); switch service case 1 % voice service buffer_best(i) = rand * 180; % (second) duration of call <= 3 mins maxBW(i) = 128*10^3; % (Hz) max throughput for voice service case 2 % data service buffer_best(i) = rand * (5*10^6*8); % (Mbits) data size <= 5 MB maxBW(i) = 384*10^3; % (Hz) max throughput for data service case 3 % video service buffer_best(i) = 10 + rand * (40*10^6*8); % (Mbits) video size = [10 50] MB maxBW(i) = 10^6; % (Hz) max throughput for video service end buffer_norm(i) = buffer_best(i);

Now, we will describe the operation of Best CQI scheduling algorithm in

the following part of simulation codes:

[sortedCQI, idx] = sort(CQI(:,CQI_update_time),'descend'); % find UE with best CQI for each RB for i = 1:nUE % scan all UE in the sorted CQI from highest to lowest if (buffer_best(i) ~= 0) && (maxBW(idx(i)) <= remain_BW_best) % if UE has data and enough free BW % UE throughput UE_throughput = eff(CQI(idx(i),CQI_update_time)) * reqBW(idx(i)); % System capacity sys_throughput_best(slot) = sys_throughput_best(slot) + UE_throughput; % Remaining bandwidth remain_BW_best = remain_BW_best - maxBW(idx(i));

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% Remaining buffer sent_data = UE_throughput * t_slot; switch maxBW(idx(i)) case 128*10^3 % voice service buffer_best(idx(i)) = buffer_best(idx(i)) - t_slot; otherwise % data and video service buffer_best(idx(i)) = buffer_best(idx(i)) - sent_data; end if buffer_best(idx(i)) < 0 buffer_best(idx(i)) = 0; end end end

First of all, we sort all the CQI descending and memorize the index of

corresponding UE to each value of CQI. Then, we will scan the sorted UEs

from the one with highest CQI to the one with lowest CQI; if a UE has data

and enough channel bandwidth for its service, it will be allowed to receive

its data. As long as a user’s data is transmitted, its throughput and system

capacity will be calculated; and also the remaining channel bandwidth for

other users will be decreased an amount of that user’s maximum service

bandwidth.

Next, we will illustrate the operation of conventional CQI-based as

followed:

scanned = zeros(1,nUE); % check whether all UE is polled or not full = 0; while ~full while (scanned(ue) == 0) && (buffer_norm(ue) == 0) % find an UE that hasn't polled and has no data scanned(ue) = 1; % mark this UE is polled to avoid infinite loop when no UE has data ue = mod(ue,nUE) + 1; end if (buffer_norm(ue) ~= 0) && (maxBW(ue) <= remain_BW_norm) % if UE has data and enough free bandwidth scanned(ue) = 1; % mark this UE is polled to avoid scheduling twice % UE throughput UE_throughput = eff(CQI(ue,CQI_update_time)) * reqBW(ue); % System capacity sys_throughput_norm(slot) = sys_throughput_norm(slot) + UE_throughput; % Remaining bandwidth remain_BW_norm = remain_BW_norm - maxBW(ue); % Remaining buffer sent_data = UE_throughput * t_slot; switch maxBW(ue) case 128 * 10^3 % voice service buffer_norm(ue) = buffer_norm(ue) - t_slot; otherwise

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buffer_norm(ue) = buffer_norm(ue) - sent_data; end if buffer_norm(ue) < 0 buffer_norm(ue) = 0; end end ue = mod(ue,nUE) + 1; % poll to the next UE full = 1; % check if there is any UE can be scheduled (BW is full) or not for i = 1:nUE if (maxBW(i) <= remain_BW_norm) && (scanned(i) == 0) full = 0; break; end end if full % there's no UE can be scheduled break; end end

We have to use the variable of scanned to check if all users were scanned

to avoid infinite loop when no user has data. And also, this variable is used

to mark if user was scheduled, so that, marked users will not be scheduled a

second time. Because each time the user is scheduled, the scheduler will

assign the maximum bandwidth for UE corresponding to its service and the

total bandwidth for a user cannot greater than maximum bandwidth of its

service.

The scheduler will start scheduling at the first UE (set is UE 1). If this UE

has data and there’s enough free bandwidth for its service, then it will be

scheduled, meaning that its related variables (system capacity, throughput

at its CQI, remaining bandwidth, and remaining buffer) will be calculated.

Then, the scheduler will schedule the next UE until channel bandwidth is

full - represented by the value of full (it means that no UE has enough

bandwidth for its service or all UE were scheduled).

The result of simulation program will be discussed in the next section.

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3.5. Results and Evaluation

The channel quality changing over time and the overall system capacity are

2 main factors which will be achieved in the results of the simulation.

Furthermore, two types of throughput will be compared between the two

scheduling algorithms those are both based on CQI but slightly different.

Channel quality is based on CQI level. Throughput will be represented in

Mbit/s.

Simulation results and comparisons are shown in the following figure:

Figure 13. Channel quality vs. time

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Figure 14. System capacity

The above graphs clearly show the superiority of the Best CQI scheduling

algorithm from normal CQI-based scheduling algorithm.

Figure 13 shows that there are two groups of CQI level (higher and lower

than 11) with the best CQI colored brown. In best CQI scheduling, 3 users

(2, 4, and 5) will be scheduled most of the time because of their good

channel quality. 3 other users can only be scheduled when there’s still

enough bandwidth for their services after UE2, UE4 and UE5 were

assigned. In contrast, normal CQI-based scheduling will schedule users one

by one in the same period of time, even with their low CQI.

This claim is made clearer in the simulation result in Figure 14. The overall

system capacity in Best CQI scheduling mechanism is much higher than in

normal CQI-based scheduling mechanism because the scheduler in Best

CQI always choose user with the best channel quality, meaning the highest

order of modulation and the highest coding rate, resulting in the highest

spectral efficiency. However, in some exceptional cases, when the higher

CQI user requests lower bandwidth service, then, its throughput can be

lower than lower CQI user which requests higher bandwidth service. This,

in turn, leads to decrease of system throughput of Best CQI, even lower

than normal CQI-based.

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Conclusion

This report presented an overview of LTE, model as well as the time -

frequency resources allocation in the LTE system. Besides, the report also

introduced briefly some technologies used in signal transmission and

MIMO channel capacity in LTE system.

The main objective of this report is to review some scheduling algorithms

based on channel quality. More specifically, we focused on describing and

analyzing the operation of the Best CQI scheduling algorithm, comparing it

with a conventional CQI-based scheduling algorithm.

Two scheduling algorithms have been implemented in a MATLAB-based

simulation program. Then, based on the results of the simulation, we have

given analytical evaluation and comparison between the two algorithms.

The superiority of Best CQI is evident in the overall throughput of the

system that most simulation time, Best CQI has greater system capacity

than normal CQI-based scheduling algorithm, except some special cases

when low CQI user requests service that requires much more bandwidth

than high CQI user’s service.

In conclusion, we have to admit that this article only involved in some

basic features of scheduling in LTE network. To have closer and more

detail point of view, we need to invest more time and effort in research. We

hope to widen the problem discussed in this report in the future.

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Acknowledgement

This report is the first achievement in my own way of research in

Telecommunication at the University of Engineering and Technology

(UET, VNU-H). This report was carried out in Department of

Telecommunications System, Faculty of Electronics and

Telecommunications (FET). I have been working on my project from

December 2012. While undertaking this project, I have received many

useful guidelines and a lot of encouragement from my friends and family. I

would like to express my deep gratitude to these supports.

First of all, I particularly give thanks to my daily advisor, Ph.D. Nguyen

Quoc Tuan. He was a great support when I had any misunderstanding in

theoretical problems and simulation troubles as well. With his dedicated

guide, I have overcome the difficult period, disoriented with unexpected

problems. I really appreciate his valuable advices.

Secondly, I would also like to thank my friends who helped and give me

useful suggestions. They also contributed a part of my project’s success.

Finally, I want to thank my family for their unwavering support.

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