IRJET- Review Paper on Call Admission Control with Bandwidth Reservation Schemes in Wireless Communication System

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 06 | June 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 3209
Review Paper on Call Admission Control with Bandwidth Reservation
Schemes in Wireless Communication System
Mithilesh Kumar1, Prof. Suresh S. Gawande2, Prof. Satyarth Tiwari2
1Research Scholar, Electronics & Communication Department, Bhabha Engineering Research Institute, Bhopal
2,3Faculty Electronics & Communication Department, Bhabha Engineering Research Institute, Bhopal
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Abstract - In this paper, we bring forward the important
aspect of energy savings in wireless access networks. We
specifically focus on the energy saving opportunities in the
recently evolving heterogeneous networks (HetNets), both
Single-RAT and Multi- RAT. Issuessuchassleep/wakeupcycles
and interference management are discussed for co-channel
Single-RAT HetNets. In addition to that, a simulation based
study for LTE macro-femto HetNets is presented, indicating
the need for dynamic energy efficient resource management
schemes. Multi-RAT HetNetsalsocomewithchallengessuch as
network integration, combined resource management and
network selection. Along with adiscussiononthesechallenges,
we also investigate the performance of the conventional
WLAN-first network selection mechanism in terms of energy
efficiency (EE) and suggest that EE can be improved by the
application of intelligent call admission control policies.
Key Words: Energy efficiency, Heterogeneous Networks,
Long-Term Evolution, Multi-RAT, WLAN.
1. INTRODUCTION
The evolution of wireless communication devices
continues to explode the traffic demand in wireless
communication systems. It is expected that the traffic
demand will increase up to thirteen fold by 2017 as that of
2012, at a compound annual growth rate (CAGR) of 66%[1].
Therefore, wireless network providers face an enormous
challenge to increase theirnetwork capacity,inordertocope
with the increasing traffic demand. Since improvements in
spectral efficiency (SE) at link level approaches its
theoretical limits with currently existing technologies, the
next generation of technology is about improving spectral
efficiency per unit area [2]. Therefore, network providers
and equipment vendors are looking intoanevolved network
topology to improve the network capacity. To this end, the
heterogeneous network (HetNet) architecture is seen as a
promising solution to the capacity problem of wireless
communication networks.
A HetNet may consist of different size of cells with
different radio access technologies (RATs). Fig. 1 depicts a
typical example of a HetNet. In HetNets, small cells bring
down the distance between transmitter and receiver, which
results in low path loss. This leads to an increased received
signal power, signal to noise ratio (SNR) and better SE.
Therefore, the area efficiency (AE) (i.e SE per unit area) can
be improved [3].
Fig -1: A typical example of HetNet
Apart from the capacity demand, energy consumption of
mobile terminals becomes an increasing concern due to in-
creased network usage of latest advanced wireless
communication devices (e.g. smart phones and tablet PCs).
Therefore, there is a significant threat that the 4G mobile
users will be searching for power outlets rather than
network access, and once again binding them to a single
location. This problem is sometimesdescribedastheenergy-
trap of 4G systems [4]. At the same time, EE of the network
also considered as an important aspect of network
operation, due to the increased cost of energy and
environmental concerns. Hence, apart from the coverage,
capacity and QoS, the energy efficiency(EE)alsobecomes an
important performance indicator from the component
design to the network operation.
2. LITERATURE SURVEY
In general, conventional antenna system architectures
used in mmWave band are inadequate to combine wide-
angles with high directionality. Existing reflective, parabolic
dishes and lens antennas can create narrow beam, thus
delivering the needed 30_40 dB antenna gain, but they lack
the flexibility to cover wideangle coverage andare relatively
bulky. Phased patchantennaarraysallowssteeringthebeam
to a desired direction. However, to achieve the necessary
directivity, the array must consist of a large number of
elements (several hundred to thousands).
Phase antenna arrays composed of a large number of
antenna elements have been proposed to achieve the
necessity of the widedirectionality. The Phase antennaarray
architectures currently used for mass production employ a

single module, containing a radio frequency integrated
circuits (RFIC) chip that includes controlled analogue phase
shifters capable of providing several discrete phase shifting
levels. The antenna elements are connected to the RFIC chip
via feed lines. However, due to the loss inherent in the feed
lines, this approach reduces antenna gain and efficiency, and
becomes a severe problem when the number of antenna
elements and RF increase [7].
The call admission control strategies investigated in the
literature are variedly classified into types. Generally,
Deterministic Call Admission Control and Stochastic Call
Admission Control are the two categories of call admission
control schemes in cellular networks [5]. In deterministic
CAC, QoS parameters are guaranteed with 100% confidence.
These schemes typically require extensive knowledge of the
system parameters such as user mobility which is not
practical, or sacrifice the scarce radio resources to satisfythe
deterministic QoS bounds. On the other hand, in stochastic
CAC, QoS parameters are guaranteedwithsomeprobabilistic
confidence. By relaxing QoS guarantees, these schemes can
achieve a higher utilization than deterministic approaches.
CAC schemes can also be classified as proactive (parameter-
based) or reactive (measurement-based). In proactive
schemes [6], the incoming call is admitted or rejected based
on some predictive/analytical assessment of the QoS
constraints. In reactive schemes [7], the incoming call might
start transmission (by transmitting someprobing packets or
using reduced power) before the admission controller
decides to admit or reject the call based on the QoS
measurements during the transmission attempt at the
beginning. In [8], CAC is classified based on the information
needed in the CAC process. Some CAC schemes use the cell
occupancy information [9]. This class of schemes requires a
model or some assumption for the cell occupancy.
Alternatively, CAC schemes might use mobility information
(or estimation) in making the admission decision. The use of
mobility information, however, is more complicated and
requires more signaling. The information granularityusedin
CAC schemes can be considered at the cell level or at the user
level. If a uniform traffic model is assumed, information of
one cell is enough to represent the whole network condition.
In a non-uniform traffic model, however, information from
different cells is required to model the networkstatus,which
increases the information size. The third case, in which
information of each individual user is considered, of course
leads to a huge information size.
Furthermore, CAC schemes have been designedeitherforthe
uplink [10] or the downlink [11]. In the uplink, transmit
power constraint is more serious than in the downlink since
the MS is battery operated. On the other hand, CAC in the
downlink needsinformationfeedbackfromMSstotheBSsfor
efficient resource utilization. Applying CAC for both links
jointly is crucial since some calls might be admissible in one
of the links and non-admissible in the other, particularly for
asymmetrical traffic. In the uplink direction of a wireless
network, one CAC is based on the number of users and is
referred to as number-based CAC [3] and the other is based
on the interference level and is referred to as interference-
based CAC [12]. The operation of the number-based CAC
schemes is quite similar to the fixed-assignment
FDMA/TDMA systems. That means that capacity is „hard‟ as
the number of users that can be admitted into the system is
fixed. The Signal to Noise Interference Ratio-basedalgorithm
computes the minimum required powerforthenewuserand
accepts it if it is not below a predefined minimumlinkquality
level.
3. SYSTEM MODEL
We consider a multimedia wireless/mobilenetworkwith
a cellular infrastructure, comprising a wired backbone and a
number of base stations (BSs). The geographical area
controlled by a BS is called a cell. A mobile, while staying in a
cell, communicates with another party, which may be a node
connected to the wired network or another mobile, through
the BS in the same cell. When a mobile move into an adjacent
cell in the middle of communication session, a handoff will
enable the mobile to maintain connectivity to its
communication partner, i.e., the mobile will start to
communicatethrough thenewBS,hopefullywithoutnoticing
any difference.
In this paper, we are concerned with CAC and bandwidth
management in each cell. Therefore, we decompose the
cellular network into individual sub-systems, each
corresponding to a single cell. The correlation betweenthese
sub-systems, results from handoff connections between the
corresponding cells, which is re-introduced as an input to
each sub-model. Under this assumption, each cell can be
modeled andanalyzed individually. A samemodel is usedfor
all cells in the network, but the model parameters may be
different, reflecting the mobility and traffic conditions in
individual cells, as well as the channel assignment policy
employed by the network. Therefore, we can model the
system at single-cell level.
We assume the system uses Fixed Channel Allocation
(FCA), which means each cell has a fixed amount of capacity.
No matter which multiple access technology (FDMA, TDMA,
or CDMA) is used; we could interpret system capacity in
terms of effective or equivalent bandwidth [10]. Hereafter,
whenever we refer to the bandwidth of a connection, we
mean the number of basic bandwidth units (BBUs) that is
adequate for guaranteeing desired QoS for this connection
with certain traffic characteristics.
Consider a cell that has a total capacity of C BBUs.
Two types of connections share the bandwidth of the cell:
new connections and handoff connections. In this work, we
consider only real-time services. Typically, class-1 traffic
includes voice service while class-2 traffic is comprised of
video service. Thus, traffic arriving at the cell is partitioned
into two separateclasses based on bandwidth requirements.

Each class-i connection requires bandwidth ci BBU (i = 1, 2).
The classes are indexed in an increasing order according to
their bandwidth requirements, such that: c1≤ c2. The block
diagram representation of the wireless cell is shown inFig.2.
Fig -2: System Model
4. HETNET DEPLOYMENT
In terms of network deployment, a HetNet can consist of
different size of cells, such as macro, micro, pico and femto
cells that provide services to same coverage area in a multi-
tier configuration, that utilizesingleRAT.ThiskindofHetNet
is known as Single-RAT HetNet. For example, the 3rd
Generation PartnershipProject(3GPP)LongTermEvolution
- Advanced (LTE-A) system, with outdoor macro Base
Station (BS) and indoor Home BS (HBS) is a prime example
for such Single-RAT HetNet. On the other hand, in a Multi-
RAT Het-Net, multiple RATssuchasWidebandCodeDivision
Multiple Access (WCDMA), Worldwide Interoperability for
Microwave Access (WiMAX), Wireless Local Area Network
(WLAN) and LTE can jointly provide service to same
coverage area in a complementary manner with different
coverage ranges. A network of outdoor WCDMA/LTE macro
cells with indoor and hot spot coverage of WLAN is a
practical example of Multi-RAT HetNets.
The advantage of Single-RAT HetNets comes from the
relatively less complex network operation compared to the
Multi- RAT. For example, a Multi-RAT HetNet needs
additional authentication, authorization and accounting
(AAA) system, allowing users to perform authenticationand
authorization processes in different RATs, attending to
security suites and subscription profiles for security and
billing purposes. However, Single-RAT HetNet suffers from
the cross-tier interference. Since the spectrum is scarce and
expensive, the available licensed spectrum is limited to each
operator.
Therefore, in most cases, the same spectrum will be shared
between different tiers in a Single-RAT HetNet. To this end,
mitigating interference while increase thenetwork capacity,
is considered as a major challenge in Single-RAT HetNet. On
the other hand, in a multi-RAT HetNet, the advantage is,
having different RATs that utilize different frequency
spectrum including the unlicensed spectrum (e.g. WiFi).
Therefore, Multi-RAT HetNet does not suffer from cross-tier
interference. However, integration of different RATs
becomes one of the major problems in multi-RAT HetNet,
due to different techno-logical and architectural aspects of
each RAT.
5. ENERGY EFFICIENCY ANALYSIS OF LTE-WIFI
HETNET
Traditionally, total network EE has not been an
optimization parameter in a Multi-RAT HetNet. Moreover, in
current Cellular WLAN HetNets, the user terminals selectthe
desired network based on the user preference, without
specific optimization, due to the complexity involved in such
optimization processes. For example, in the widely used
network selection scheme, known as WLAN-first [10], the
mobile terminals always connect to the available WLAN,
without considering network load, qualityofservice(QoS)or
EE. Further, there is no CAC policy in the WLAN-first
scheme. Therefore, the WLAN network can become
congested; hence the whole network performance degrades.
To this end, we investigate the performance of LTE-
WiFiHetNet in terms of total network EE and per user
throughput for WLAN-first scheme with and without CAC.
Here, when there is a CAC policyapplied to theWiFinetwork,
we assume that the APs only allow certain number of users
(e.g. 4 users) who have best channel condition under its
coverage.
For this study, weconsideranLTE-WiFiHetNetcomprisingof
a single LTE cellular macro base station (BS) and multiple
WiFi access points (APs), providing service to the same
coverage area. Fig. 2 depicts such typical network
architecture. Since we are interested in access part of the
network, we adopt tight coupled network of LTE and WiFi,
where the WiFi APs are connectedtotheEvolvedPacketCore
(EPC) through a gateway router in a samemanner as theLTE
BS (eNodeB).We evaluate the system performance through
Network Simulator 3 (NS3) based system level simulations,
adopting realistic power consumption models for both
networks and considering all practical aspects of full
communication protocol stack according to the relevant
standards.For energy consumption evaluations, weadopted
power consumption profile for macro BS and WiFi AP from
[11] and [12] respectively.
Fig -3: Typical LTE-WiFiHetNet

Fig. 3 show the simulation results in terms of per user
throughput and normalized EE respectively, with respect to
increased AP deployment in the considered coverage area.
6. CONCLUSION
Due to the recent evolution of mobile communication de-
vices, demand for network capacity increases exponentially.
At the same time, the energy efficiency (EE) of both wireless
communication devices and network attracts increasing
interests due to short battery life time of advanced mobile
terminals and increasing operation cost of mobilenetworks.
The HetNet architecture is considered as a promising
solution for both aforementioned capacity andEEproblems.
Therefore, in this article, we summaries the challenges and
opportunities to improve the EE while increasing the
network capacity in both Multi-RAT and Single-RAT HetNet.
Especially, in LTE-WiFi and LTE macro-femto HetNet
respectively. It is evident that, through proper network
operation policies and resource management strategies, the
total network EE can be improved while increasing the
network capacity by off-loading the traffic to WiFi hotspots
or femto cells.
REFERENCES
[1] Enoruwa Obayiuwan, OlabisiFalowo,“AdaptiveMobility
Aware Call Admission Control For Mobile Hotspot
Networks”, 2016 IEEE 27th Annual IEEE International
Symposium on Personal, Indoor and Mobile Radio
Communications - (PIMRC): Mobile and Wireless
Networks.
[2] Lei Xu, Ping Wang, Qianmu Li, and Yinwei Jiang,
“Call Admission Control with Inter-Network
Cooperation for Cognitive Heterogeneous Networks”,
IEEE Transactions on Wireless Communications, Vol.
16, No. 03, 2017.
[3] D. Moscholios, V. G. Vassilakis and M. D. Logothetis,“Call
blocking probabilities for poisson traffic under the
multiple fractional channel reservation policy”, 10th
International Symposium on Communication Systems,
Networks and Digital Signal Processing (CSNDSP), pp.
01-05, 2016.
[4] Amilcare Francesco Santamaria and Andrea Lupia, “A
New Call Admission Control Scheme Based on Pattern
Prediction for Mobile Wireless Cellular Networks”, pp.
01-06, 2015.
[5] Younghyun Kim, HaneulKo, Sangheon Pack, Xuemin
Sherman Shen, “Vehicular Passenger Mobility-Aware
Bandwidth Allocation in Mobile Hotspots”, IEEE
Transactions on Wireless Communications, Vol. 13, No.
06, pp. 3281-3292, 2014.
[6] Yao-Liang Chung, Zsehong Tsai, Chih-HuiYang,“AStudy
of Quota-Based Dynamic Network Selection for
Multimode Terminal Users”, Vol.08,No.03,PP.759-768,
2014.
[7] Younghyun Kim, HaneulKo, SangheonPack,WonjunLee
and Xuemin Shen, “Mobility-Aware Call Admission
Control Algorithm With Handoff Queue inMobile
Hotspots”, IEEE Transactions on Vehicular
Technology, Vol. 62, No. 08, pp. 3903-3912, 2013.
[8] Chenn-Jung Huang, Yi-Ta Chuang and Dian-Xu Yang,
“Implementation of CAC schemes in next generation
mobile communication networks using PSO
optimization and fuzzy logic systems”, Expert Systems
with Applications, Vol. 35, No. 3, pp. 1246-1251, 2010.
[9] S. Chaltziperis, P. KoutsakisandM.Paterakis,“AnewCAC
mechanism for multimedia traffic over next-Generation
wireless cellular networks”, IEEE Transactions on
Mobile Computing, Vol. 7, No.1, pp. 95-112, 2008.
[10] L. Barrolli, F. Xhafa, A. Durresiand A. Koyama, “A fuzzy
based CAC system for wireless cellular networks”, An
International Conference on Distributed Computing
Workshop, ICDCSW-07, pp. 38-40, 2007.
[11] M. Chatterjee, H. Lin and S. K. Das, “Rate Allocation
and Admission control for Differentiated Services in
CDMA Data Networks”, IEEE Transactions on Mobile
Computing, Vol. 6, No. 2, pp. 179-191, 2007.
[12] C. A. Coello, “Evolutionary multiobjectiveoptimization –
a historical view”, International Journal of IEEE
Computational Intelligence, Vol. 1, No. 1, pp. 28-36,
2006.

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