Quality of service parameter centric resource allocation for lte advanced

IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 03 Issue: 02 | Feb-2014, Available @ http://www.ijret.org 330
QUALITY OF SERVICE PARAMETER CENTRIC RESOURCE
ALLOCATION FOR LTE-ADVANCED
Nilam Dhameliya1
, Rajni Bhoomarker2
, Sameena Zafar3
1
Student, 2
Assistant Professor, 3
H.O.D, E.C. Department, Patel College of Science and Technology, Bhopal, India
Abstract
Efficient radio resource allocation algorithm is proposed in this paper. Proportional fair algorithm gives favorable performance
compared to two classical algorithms max-C/I and RR. But PF algorithm does not consider QoS parameters while allocating
resources to the users. Moreover with less transmission power utilization Fairness disappears in PF algorithm. A PFQoS-Centric is
proposed in this paper. PFQoS-Centric gives better performance. In this proposed algorithm Data rate fairness and Allocation Fairness
increase with increase in time jitter threshold values or increase in number of active users increases. Thus Data rate fairness and
Allocation fairness is becoming more as compared to that of proportional algorithm. Here Throughput is also maintained during
increase in fairness. QoS parameters like Data rate Fairness, Allocation Fairness Delay is most important for next generation of
mobile communication. Propose Algorithm (PFQoS-Centric) gives guarantee to satisfy the QoS parameters.
Keywords: Quality of Service (QoS), Packet scheduling, Proportional fair (PF), Radio resource allocation (RRA), Data
rate Fairness, Allocation Fairness, Long term evolution-advanced (LTE-A).
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1. INTRODUCTION
The integration of wireless and internet communications and
rapid development of next generation of mobile
communications is expected to support the outburst of high-
speed packet based application. These applications have large
variety of QoS requirements. Radio resource Allocation faces
challenges when come across such large variety of QoS
requirements. This is because of Limited radio resources,
rapidly changing wireless channel condition and ever
increasing number of mobile users [1].
In this paper the QoS parameter is taken into consideration for
the purpose of PF scheduling. This includes the required
scheduling activity for a user to fulfill the QoS requirements.
In this paper the scheduling for non-QoS users is also taken
into consideration. Instead of selecting users based on the ratio
of future data-rate to past achieved data-rate, here it is taken as
the ratio of the minimum required data-rate to instantaneous
throughput for that scheduling interval. For feasible load case
it is satisfying the minimum required data-rate to the users
demanding QoS services then providing access to users
requiring non-QoS services, and for congested case it’s
maintaining the fairness to all users demanding QoS service
with same relative degradation for all active users [2].
In this paper, it tries to maintain the QoS in terms of maximum
tolerable latency. In this case the HoL delay is taken into
consideration to take care of latency. And also buffer value is
taken in to consideration, so that there should not be any loss
of data for a user due to buffer overflow [3].
Mainly three techniques are used for resource allocation.
These are Max-C/I, PF and RR. Examples of these three
different scheduling behaviors for two users with different
average channel quality are shown in fig 1(a), fig 1(b) and fig
1(c).
Fig -1(a): scheduling behaviors for two users with different
average channel quality for max C/I. [5]
Allocating resources to the users with instantaneously best
radio link conditions is known as MAX-C/I scheduling. The
data-rate fairness disappears. Allocation of the resources
among the existing active users in a manner, such that all the
users will get access to equal amount of resources over certain
duration of time The requirement of the user is not taken into
consideration

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Fig -1(b): scheduling behaviors for two users with different
average channel quality for RR. [5]
Fig -1(c): scheduling behaviors for two users with different
average channel quality for PF. [5]
But among three Max-C/I and RR does not give optimum
performances for throughput as well as QoS parameters.
Moreover PF also gives poor performance compared to RR
algorithms for Allocation Fairness.
In this paper problem formulation is to increase allocation
fairness as well as data rate fairness and maintain other
parameters. As the Fairness is one of the most important
parameter among QOS parameters. Here we also study
performance as the number of user increases.
The rest of paper is formed as follows. Section –II represents
an idea about packet scheduler, section –III represents
simulation algorithm and environment, section –IV represents
simulation result and analysis, and section-V represents final
conclusion.
2. PACKET SCHEDULING
In fig 2 a block diagram of packet scheduling is shown.
2.1 Scheduler
Here in the first stage the scheduler selects the schedulable
users. The rest user will be inactive. This function is known as
Schedulability check.
Fig -2: Block Diagram of Packet Scheduling. [3]
2.2 QoS Time Domain Scheduler
The time domain scheduler provides needed QoS, with regards
of time jitter for packet delivery to the user. The users are
splits into two classes according to delay is experienced by
user. The users with time jitter value below the threshold time
are grouped to in to class-1 and a prioritization is calculated
according to formula given below.
( )
max 1,
( )
EBR n
LP PR n
Max
 
  
 
(1)
The users with jitter value more than threshold value are in
class-2. Priority equation is
  ( )
max max 1,
( )
EBR n
HP LP PR n
Max Max
 
   
 
(2)
Here, MaxHP is the priority metric of higher priority users (the
users of class-2), MaxLP is the priority metric of lower priority
users (the users in class-1). EBR(n) is the expected bit rate of
user ’n’ and PR(n) is the achieved past average bit rate over a
certain time period.
2.3 QoS Frequency Domain Scheduler
In this algorithm selects users based on their priority value and
assigns a resource so that user’s throughput can be maximized.
If sorted users do not have good channel condition then
remaining users are taken into consideration. Thus it can be
given chance to the users having higher EBR to access the
PRBs to achieve throughput with higher priority and it can
improve its throughput. It will help in maintaining the fairness,
in terms of corresponding of required data-rate, among users.
Scheduler
QoS Time
Domain
Scheduler
QoS Freq.
domain
scheduler
Buffer
QoS Measures
Sched-
ulable
user
Nmux
Users
Sub
frame
builder
CQI Reports

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3. SIMULATION ALGORITHM &
ENVIORNMENT
Simulation algorithm and simulation environment are as
follows.
The Simulation parameters are set according to the values
specified by 3GPP-LTE [6]
Table -1: Simulation Environment
Parameter Value
Carrier Frequency 2GHz
Delay Spread 0.001ms
LA per frame 1
Sub-frame duration 1ms
OFDM symbols per sub-
frame
14
BW 5MHz
FFT size 512
MIMO 1X1
Target FER 0.1
Sub-frame duration 1ms
Inter Site Distance 1732mtrs
Number of active users 30 & 40
Mobility of the users 30kmph
Per user file size 2Mb
Nmux 10
Delay Threshold 5ms
4. SIMULATION RESULT AND ANALYSIS
In this paper, Outputs are compared for Classical PF and
Proposed PFQoS-Centric. Outputs also compared for Number of
active users like 30 & 40. The other parameters consider for
proposed algorithm are 5ms Delay, 1 X 1 MIMO, 1732mtrs
Inter site distance, 2Mb Per user file size, 5MHz Band Width,
2 GHz Carrier Frequency, 14 OFDM symbols per sub-frame,
512 FFT size, 0.1, 1ms Sub frame duration, Target FER,
0.001ms Delay Spread, 30kmph Mobility of users, 10 Nmux.
Data Rate Fairness for Proposed algorithm is shown in fig-3.
This new algorithm is still able to provide reciprocal data-rate
fairness, among users, of about more than 80% with increase
in time. This little fall in fairness is because of consideration
of time jitter while scheduling. Though there is a drop in data
rate fairness in case of new proposed scheme, this drop is not
significant.
Fig -3: Data Rate Fairness
Allocation Fairness is shown in fig.4. The figure says that, in
case of proportional fair, with increase in number of active
users in the system the allocation fairness decreases. But in
case of proposed scheme with increase in number of users in
system the fairness in allocation is increasing. It is also clear
from the figure that with increase in number of users in the
system the allocation fairness is becoming more as compared
that of proportional fair algorithm.
PFQoS-Centric Algorithm
1. Initialization: N=Number of users
2. If UE delay< Delay threshold
Calculate Priority according to equation (1)
Else
Calculate Priority according to equation (2)
3. Sort all the users according to their priority value
in decreasing order.
4. Select frist Nmux users according to the priority
value who can be provided some data rate at
particular time instance.
5. Select resource block of Maximum gain for
particular user thus user throughput can be
maximized and set allocation flag of that
particular PRB to 1.
6. Repeat for Nmux users.
7. If some resource blocks are not allocated and
sorted users do not have good channel condition
then consider rest of the users for resource
allocation.

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Fig -4: Allocation Fairness
5. CONCLUSIONS
Here we proposed such an algorithm which provides data rate
allocation is more with increase in time. Proposed algorithm
also gives better performance compared to proportional fair
algorithm as increase in the number of users. Now a day QoS
parameters are extremely important for next generation mobile
communication. In this paper, QoS parameters are
guaranteeing by the proposed algorithm (PFQoS-Centric)
REFERENCES
[1] Kausar, Y. Chen, K.K. Chai “QoS aware packet
scheduling with adaptive resource allocation for
OFDMA based LTE-advanced networks” in IET
International Conference on Communication
Technology and Application (ICCTA 2011),January
2011, pp. 207 – 212.
[2] Troels Emil Kolding, “QoS-Aware Proportional Fair
Packet Scheduling with Required Activity Detection,”
in IEEE 4th Vehicular Technology Conference, 2006.,
Sept. 2006, pp. 1–5
[3] I. Z. K. P. E. M. G. Mongha, K. I. Pedersen, “QoS
oriented time and frequency domain packet schedulers
for the utran long term evolution,” IEEE Vehicular
Technology Conference, spring, 2008, 11-14 May
2008, pp. 2532 – 2536.
[4] Xiaoqiu Wang, Konishi S., Suzuki T,” Multi-Layer
Optimized Packet Scheduling for OFDMA-Based
Cellular Systems” in IEEE Vehicular Technology
Conference, 2009.April.2009,pp 1-5..
[5] J. S. P. B. Erik Dahlman, Stefan Parkval, 3G Evolution:
HSPA and LTE for Mobile Broadband, 2nd ed.
Academic Press, 2008, no. 2008931278.
[6] 3GPP-LTE,”LTE physical layer framework for the
performance verification” Release 10, no.R1-070674,
February 2007.
BIOGRAPHIES
Nilam Dhameliya has completed her
graduate in Electronics and Communication
engineering from L D College of
Engineering, Ahmedabad. At present, she is
pursuing her M.Tech in area of Digital
Communications from College of Science and Technology,
Bhopal. Her research area includes OFDM and LTE.
Rajni Bhoomarker has completed her M.Tech
in Digital Communication from Samrat Ashok
Technological Institute, MP. Currently she is
assistant professor at Patel College of Science
and Technology, Bhopal. She is pursuing PhD
from Barkatullah University, Bhopal. Her research area
includes OFDM and Signal Processing.
Sameena Zafar has completed her M.Tech in
VLSI and Embedded System from Maulana
Azad National Institute of Technology,
Bhopal. At present, she is HOD in EC
department of Patel College of Science and
Technology, Bhopal. Her area of research includes VLSI,
System C, Hardware and Software Design.

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