International Journal of Engineering Trends and Technology (IJETT) – Volume 23 Number 9- May 2015
Efficient Algorithm for Resource Allocation with
Carrier Aggregation in downlink LTE-A Networks
Padmavati koradhanyamath1, Hemanth Kumar A.R2
1
Bangalore Institute of Technology
M.Tech (DEC), Dept. of ECE, Bangalore Institute of Technology, Bengaluru, Karnataka, India
2
Professor, Dept. of ECE, Bangalore Institute of Technology, Bengaluru, Karnataka, India
Abstract— LTE-Advanced (LTE-A) is the latest set of mobile
technology specifications and enhancement over LTE. Previously
submitted as candidate for 4G system to ITU-T and then
upgraded in 3GPP’s Release 10 with specification of very high
data rates. To achieve these data rates Carrier aggregation
technique is used in downlinks. Downlink scheduler is an
important component for structured radio resource utilization,
hence, in respect of LTE simulation; the availability of better
down-link scheduler models is very significant.
In this paper an important problem of downlink radio
resource allocation in LTE-A systems by using carrier
aggregation (CA) technology is identified and thus analyze the
performance of a greedy algorithm in downlink radio resource
allocation with carrier aggregation. The allocation is based on
MCS constraint which most of the previous studies haven’t
considered and that the component carriers of user equipments
can be changed. The improvement in the throughput with greedy
algorithm over without using greedy algorithm is shown by
simulation results.
Keywords— LTE-A , carrier aggregation, carrier allocation,
MCS constraint, packet scheduling.
I. INTRODUCTION
LTE-Advanced was specified in release 10 to further
develop LTE towards LTE–Advanced. It specified higher
bitrates in a cost efficient way and, at the same time,
completely fulfill the requirements set by ITU for IMT
Advanced, also referred to as 4G. New functionalities
introduced in LTE-A are carrier aggregation (CA), support
for relay nodes (RN). 3GPP specify IMT requirements of
1Gbps in downlink and 500Mbps in uplink[1]. Increased
number of simultaneous active subscribers and higher spectral
efficiency can be achieved.
User equipments (UE) support bandwidth up to 100MHz.
Also the spectrum is widely allocated to existing legacy
systems and hence continuous frequency band is rarely
available for CA. CA involves UE simultaneously aggregating
two or more different frequency fragments called component
carriers (CCs) to form wider transmission bandwidth[2].CA is
backward compatible in the sense that LTE-A supports both
LTE and LTE-A users. The LTE user can only access one
Component Carrier and on the other hand LTE-A can access
and non-contiguous CA[3]. figure i shows CA in different
scenarios.
Fig i : carrier aggregation in LTE-A networks
The MCS constraint, as given in 3GPP TR 36.912[4],
requires that only one MCS can be selected for each assigned
CC across all its assigned RBs for a UE at any TTI in absence
of MIMO spatial multiplexing. This paper will formulate the
downlink radio resource allocation as:
(i) The scheduler can reassign CCs to each UE at each TTI
(ii) Only one MCS can be selected for each assigned CC
across all its assigned RBs for a UE at any TTI.
A Backlogged traffic model with fixed number of UE is
used for simulating the LTE- A network. The backlogged
traffic network implies fixed number of UEs with full buffer.
The LTE-Advanced network employs LTE-SAE architecture
as shown in fig ii.
The remainder of this paper is structured as follows:
Section II discusses prior work in the field of carrier
aggregation. The brief introduction to frame structure and
traffic model is given in Section III. Section IV discusses the
methodology and Section V the gives the testing sequence.
Section VI gives the complexity and section VII giving
results. Finally, we summarize our conclusion on the present
work in Section VIII
all component carriers.CA allows the UE to aggregate
multiple CCs in same or different bands leading to contiguous
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International Journal of Engineering Trends and Technology (IJETT) – Volume 23 Number 9- May 2015
III. LTE-FRAME STRUCTURE AND TRAFFIC MODEL
A. Frame Structure
LTE-Advanced network employs orthogonal frequency
division multiple access (OFDMA) technique. It is based on
fixed frame-based transmission, for downlink transmission.
In OFDMA, a radio frame with 10 ms duration is divided
into 10 equal-sized sub-frames each with 1 ms duration. Each
sub-frame is composed of two equal-sized time-slots with 0.5
ms duration. Each slot corresponds to 7 or 6 consecutive
OFDM symbols depending on the cyclic prefix length. A basic
scheduling unit in LTE-A, called a resource block (RB),
consists of a time-slot in time domain and 12 consecutive
subcarriers in frequency domain. Each subcarrier has 15 kHz
bandwidth [[15]. LTE downlink scheduling: task of allocating
RBs of a CC to UEs, is performed at each TTI (i.e., sub frame
duration of 1 ms).
Fig ii: LTE-A architecture
II.
PREVIOUS STUDIES
The downlink radio resource management in LTE-A
systems with CA configuration has been widely studied. In
[5], [6], [7], the authors gave a new proportional fair
scheduler to perform joint scheduling so as to address the
scheduling problem for optimizing the radio resource
meanwhile maintaining proportional fairness among all UEs
of LTE-A.
The proportional fair scheduler achieve high throughput as
well as maintain proportional fairness among all UEs by
giving higher priority to UEs based on historical average
throughput of users from all carriers. That is higher priority to
higher current transmission rate and lower average
transmission rate in the past. But, they did not consider that
the assigned CCs in each UE can be changed dynamically
according to channel quality. And also the MCS constraint has
not been considered in the above works.
In [8], [9], the authors introduced the inter-band CA
scenario, here UEs employ CA in different frequency bands
with proposed load balancing schemes for the inter-band CA
scenario to achieve higher performance
Kwan et al. [11] modeled an optimization problem to
maximize the system throughput, thus proposed a simple
greedy-based scheme to reduce the computational complexity
of the optimization problem. He showed that the optimal
scheme can achieve higher performance than the sub-optimal
scheme but with a higher complexity. In [15], Kwan et al.
gave another optimization problem for either maximizing the
system throughput or achieving proportional fairness among
all UEs.
The works in [12], [13] [14] followed the above
optimization problem of kwan and proposed greedy-based or
meta-heuristic schemes to get sub-optimal solutions. However,
all the above works are designed for LTE systems without CA
configuration. This chapter gave brief review of the work done
in Carrier Aggregation, packet scheduling and carrier
assignment in LTE-A networks.
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Fig iii: LTE-A frame structure
B. Downlink system model
Consider a downlink scenario in LTE-A networks with an
E-UTRAN NodeB (eNB) and number of active UEs. The eNB
can employ any number of CCs to transmit data, and each UE
can employ few among those CCs to receive data. Each CC
has the same bandwidth of let us say p RBs. Downlink
scheduling is performed at each TTI. Multiple RBs of CCs can
be allocated to a single UE at each TTI, while each RB of a
CC can be assigned up to one UE.
Backlogged traffic model is assumed and the eNB always
has data for transmission to every UE at each TTI hence, eNB
can schedule all RBs of all CCs to UEs at each TTI.. The
scheduler knows the channel conditions which vary with time,
frequency across all UEs and all RBs of all CCs from the
CQIs reported by all UEs. The modulation and coding plays
important role in determining efficiency and BER[18].
Furthermore, there are fixed number of MCSs that can be
used, where MCS 1 has the lowest transmission rate and MCS
q has the highest transmission rate.
CC assignment determines which CCs are efficiently
assigned to each UE. Packet scheduling is referred to the task
of allocating time-frequency resources of CCs, called resource
blocks (RBs), as well as modulation and coding schemes
(MCSs) to UEs at each transmission time interval (TTI). The
assignment of CCs to each UE can be reconfigured by radio
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International Journal of Engineering Trends and Technology (IJETT) – Volume 23 Number 9- May 2015
resource control for the purpose of balancing the traffic load
over multiple CCs or increasing the channel quality of UEs.
IV. PROPOSED GREEDY ALGORITHM
The objective of this algorithm is to maximize the system
throughput. And also to assure proportional fairness of radio
resource allocated among all UEs. The greedy algorithm is run
for every TTI [16]. The detailed procedure of this algorithm is
as follows:
Let U be the set of all pair of possible assignments of
a CC to UE.[U]
Set the weighted transmission rate of UE currently
being assigned RB of CC initially to zero[V=0]
Calculate weighted transmission rate of UE on RB of
CC with MCS[v]
Calculate gain of assignment[g] of cc with MCS to
UE and do this for all possible assignments to find
the assignment with largest gain. Then that CC with
MCS is assigned to UE.[g*]
Now for each RB on a CC if weighted transmission
rate of UE on RB of a CC from last step is greater
than that of UE currently being assigned RB of a CC
then RB of a CC is reassigned to previous UE.
Now the pair of this UE and CC are removed
Repeat steps above until all UEs have decided with
CCs and no assignment with higher gain is possible.
MCS that a UE uses on CC is obtained as follows:
MCS index is the maximum of all index of highest
rate MCS that can be used by UE on RB of CC.
The available values are hard coded i.e., they can be
tabled and can be read into the algorithm which run at every
transition time interval (TTI). Thus the algorithm proves to be
intractable. And the algorithm proves to be very complex. The
weighted transmission rates and other factors are obtained by
base station from channel quality indicators(CQI)[19]. CQIs
help to improve data reliability.
Each iteration computes:
(i) All possible gains.
(ii) Reassigning some RBs of CC to UE
(iii) Reassigning MCSs to UEs for the transmission on CC
VII. RESULTS
i.
Radio resource allocation in consecutive TTIs
Fig iv : CCs allocated in first TTI.
The figure indicates the allocation of RBs of a CC with
same MCS in one TTI. The next TTI shows that CC
reallocation with MCS constraint.
V. TESTING OF THE MODEL
Configure simulation time needed (TTI), protocol
used for routing.
Then network model parameters like number of UEs,
buffer size (default) etc.
Configure packet size, data rate.
Run the simulation from tcl script
Allocation is visualized from the simulation.
Generate the graphs for throughput and analyze the
performance of proposed scheme.
Fig v : CCs allocated in second TTI
The figure shows the reallocated CC in next TTI with
MCS constraint.
ii.
Throughput analysis
VI. COMPUTATIONAL COMPLEXITY
The needs to compute all weighted transmission rates, with
the computational complexity O(mnpq). m, n, p, q refers to the
number of UEs, CCs, RBs and MCS. Also the algorithm needs
of the main assignment procedure.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 23 Number 9- May 2015
mobile technology evolved, the latency targets have become
stringent. For LTE networks, 3GPP target latency for the user
plane latency is about 20 ms (excluding the backhaul transport
network). The backhaul network adds additional 10 ms.
Fig v: throughput of the system
The above figure shows the increase in the number of
packets received at all nodes. It has shown considerble
increase by using greedy algorithm. The throughput for
various number of nodes is shown as a graph.
iii. Fairness index
v.
Fig vii: End-to-end delay for the system
Packets loss
Since the MCS constraint and CC reassignment considers
all the channel quality parameters before assignment, the
packet loss is prevented as shown by figure below with
logarithmic x axis.
Fig vii: ID of generated packets at node 0 (ENodeB)
Fig vi: fairness index
From the figure above the fairness index shown with
widened scale. The fairness index varies from (1/m) to 1. Here
m is the number of UEs. The proposed scheme resulted in
better throughput performance and lower fairness index but
gives higher priority of assigning RBs to UEs with lower
average transmission rate in the past. Therefore, UEs are not
likely to suffer starvation.
iv. Latency
The total latency is measured in milliseconds (ms), and is
comprised of individual network elements and interfaces.
These may only add microseconds (µs), together constitute
end-to-end latency. Latency includes delay from buffering,
propagation, queuing, signal processing, introduced at every
link and network element through which a packet travels. As
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Fig viii: ID of packets received at all nodes (UE)
vi. Jitter
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International Journal of Engineering Trends and Technology (IJETT) – Volume 23 Number 9- May 2015
The jitter values obtained from the proposed logarithm is
very low in terms of 10-17 is very low giving higher
performance.
[4]
[5]
[6]
[7]
[8]
[9]
[10]
Fig ix: Jitter for the system
VIII. CONCLUSION AND FUTURE WORK
[11]
The paper investigates the problem of downlink radio
resource allocation in downlink LTE-A networks with carrier
aggregation. The algorithm aims to propose a greedy
algorithm concerning the allocation of RBs of CCs to UEs
with MCS constraint as per 3GPP specifications. The
proposed scheme maximizes the system throughput while
maintaining proportional fairness among all UEs. The
simulations are carried using ns2 simulator for variable
number of nodes and thus simulation results reveal that the
proposed scheme can improve substantially throughput
performance compared with the schemes in previous studies.
Future work aims to deal with the radio resource
scheduling problem regarding CA with MIMO techniques and
handover scenarios.
[12]
[13]
[14]
[15]
[16]
ACKNOWLEDGEMENTS
[17]
[18]
Authors would like to express their deep gratitude towards the
Department of Electronics and Communication Engineering of
Bangalore Institute of Technology for their support and
encouragement during this work.
[19]
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Author Profile
Padmavati Koradhanyamath received her B.E. degree in
Electronics
and
Communication Engineering
from
G.M.Institute of Technology in 2013. She is presently
pursuing her final year M.Tech in Digital Electronics and
Communication from Bangalore Institute of Technology and
the proposed research work in this paper is part of her M.Tech
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International Journal of Engineering Trends and Technology (IJETT) – Volume 23 Number 9- May 2015
thesis. Her area of interests includes wireless networks, next
generation networks.
Hemanth Kumar A. R is presently working as Professor &
Principal Investigator R&D,ECE, at BIT, Bangalore. He
completed his B.E in E&C from Manipal Institute of
Technology in the year 1999. He specilised in Digital
Commmunication from B.M.S College of Engineering in 2003
and received hi PhD from Manipal University in 2011. His
area of interst includes MANETS, Clustering the network,
Path Planning and Routing algorithms. He has published 24
international papers in various indexed jornals, and national
conference in India, Singapore, Thailand.
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