Download - CQI-based Scheduling Algorithms in 3GPP LTE
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.
Page | 2
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.
Page | 3
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.
Page | 4
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]
Page | 5
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
Page | 6
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.
Page | 7
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.
Page | 8
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.
Page | 9
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
Page | 10
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.
Page | 11
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
Page | 12
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.
Page | 13
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.
Page | 14
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.
Page | 15
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');
Page | 16
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));
Page | 17
% 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
Page | 18
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.
Page | 19
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
Page | 20
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.
Page | 21
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.
Page | 22
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.
Page | 23
Reference
[1] Tshiteya Dikamba, “Downlink Scheduling in 3GPP Long Term
Evolution (LTE)”, Delft University of Technology, March 2011
[2] Motorola, “Long Term Evolution (LTE): Overview of LTE Air-
Interface”, Technical White Paper, January 2008.
[3] Jim Zyren, “Overview of the 3GPP Long Term Evolution Physical
Layer”, freescale semiconductor, July 2007
[4] E. Dahlman, S. Parkvall, J. Skold, P. Beming, “3G Evolution HSPA
and LTE for Mobile Broadband”, Elsevier, 2008
[5] (doc file) “Chương 3: Kỹ thuật OFDM”, AVAILABLE ONLINE at
http://read.pudn.com/downloads164/doc/751381/LYTHUYET/CHUONG
%203.doc
[6] Farooq Khan, “LTE for 4G Mobile Broadband, Air Interface
Technologies and Performance”, Cambridge University Press, 2009
[7] C. Mehlfuhrer, M. Wrulich, J. C Ikuno, D. Bosanska, and M. Rupp,
“Simulating the long term evolution physical layer”, in Proc. of the 17th
European Signal processing conference (EUSIPCO 2009), Glasgow,
Scotland, Aug. 2009
[8] Tse, D. and Viswanath, P.,“Fundamentals of Wireless
Communication”, Cambridge University Press, 2005
[9] Olav Østerbø, “Scheduling and Capacity Estimation in LTE”,
Advances in Electronics and Telecommunications, Vol. 2, No. 3, Sep. 2011
[10] M. T. Kawser, N. I. Bin Hamid, M. N. Hasan, M. S. Alam, and M. M.
Rahman, “Downlink SNR to CQI Mapping for Different Multiple Antenna
Techniques in LTE”, International Journal of Information and Electronics
Engineering, Vol. 2, No. 5, September 2012
Page | 24
[11] C. E. Shannon, “A Mathematical Theory of Communication”, the Bell
System Technical Journal, Vol. 27, pp. 379–423, 623–656, July, October,
1948
[12] Lathaharan Somasegaran, “Channel Estimation and Prediction in
UMTS LTE”, Institute of Electronic Systems, Aalborg University, 2007
[13] A. Mehmood, W. A. Cheema, “Channel Estimation for LTE
downlink”, Blekinge Institute of Technology, September 2009.